Rapid Spread Research Articles

Language standardization and railway-walking distance. Levenshtein distance and distribution in GAJ

Abstract In this paper, new quantitative methods were applied to elucidate the geographical distribution of Japanese dialects. (1) Regional differences (individual differences) of usage rate of standard language at each locality in GAJ (Grammar Atlas of Japanese Dialects) were dealt with. (2) In addition to the railway distance from Tokyo to each locality, the walking distance from the railway stations to the survey localities was measured and used as the “railway-walking distance”. (3) Using a scatter-plot in what is called a “limestone cave model”, the values on the axis of standard language usage were reversed, and the Japan Islands were divided into east and west according to their direction from Tokyo. It showed that the standard language usage rate and distance show a proportional relationship even at each locality (individual informant) value. (4) Focusing on the prefectures near Tokyo, the distance from Tokyo regulates the usage rate. From this, the rapid spread of standard language from modern Tokyo and influence of railway can be ascertained. It was found that the phenomena pointed out by the average values by prefectures of vocabulary items in LAJ are valid to the data for each locality (individual) of GAJ.

From local to global: Village YouTubers and rural creator cultures in South India

This article examines emerging rural creator cultures in South India. Specifically, it focuses on the uptake and production of YouTube videos by village creators, a relatively understudied but increasingly important phenomenon given the rapid spread of social media entertainment in rural India. Through a case study on My Village Show, a YouTube media collective, this article shows how rural cultural producers transformed into ‘media workers’, ‘creators’, and ‘influencers’. The South Indian YouTubing landscape, with a multitude of village-centric social media entertainment channels, reveals a new dynamic of cultural production in the Global South, distinguished by collective, collaborative, and formalizing creator labor practices. Drawing upon ethnographic observation and in-depth interviews with rural cultural producers, this article analyzes and conceptualizes rural creator cultures within particular socio-cultural, vernacular, and regional dynamics in India.

O trudach dialogu – sztuka komunikacji z AI w projektowaniu

The rapid spread of LLMs (large language models) and other technologies based on generated content and image has significantly impacted the perception of creative work, including the area of product design. Tools using generative AI enable the acceleration or facilitation of the ideation processes, producing many different solutions to a given problem. Beside facilitating creative processes due to automation of some tasks, especially those based on working with large databases, generative AI can assume a form of specific creativity stimulator. The key notion conditioning the effective use of generative AI tools is the ability of asking adequate questions. This requires increased engagement in works towards changes in education, currently directed to reproduction and memorisation. Contrary to popular opinion, popularisation of AI as element of design process could result in expanding the spectrum of designer’s possibilities. Methodical education in compliance with the basics of cause-and-effect thinking and teaching the analytical design approach guarantee providing the young generation with solid foundation of collaboration with generative AI models. Keywords: artificial intelligence (AI), design education, creativity

Exploring the experience of cardiothoracic ICU clinicians during the COVID-19 pandemic: A grounded theory study.

Prior to the COVID-19 pandemic a flexible approach to visiting was adopted by many Intensive Care Units in the United Kingdom. Due to the rapid spread globally of COVID-19, significant policy changes were put in place, including the restriction on visitors to patients in hospital. Evidence has emerged demonstrating the negative impact of these restrictions on patients with COVID-19, their families and the staff caring for them. However, there is limited data about the impact of these restrictions in the non-COVID ICU environment. This study aimed to explore the experiences of staff caring for non-COVID-19 patients in a cardiothoracic critical care unit during the COVID-19 pandemic. This qualitative research study adopted a grounded theory methodological approach. This methodology was used due to the unique situation, with no prior research available. We recruited healthcare staff that worked in a cardiothoracic critical care unit during the COVID-19 pandemic. Semi structured interviews were carried out, transcribed, and analysed. Using the data collected, a theory was constructed. Interviews were carried out with 20 healthcare staff from a range of professions including nurses, doctors, and allied health professionals. Following data analysis four main categories emerged from the data: impact and implementation of visiting restrictions; the dehumanisation of patients; end-of-life care and witnessing distress. From these four categories, a theory has emerged suggesting that healthcare staff in a non-covid ICU were regularly exposed to potentially moral injurious events, despite being shielded from caring for patients with COVID-19. This study provides a theory that healthcare staff caring for non-COVID-19 critical care patients during the period of visiting restrictions were exposed to potentially morally injurious events. Repeated exposure to potentially morally injurious events can lead to the development of moral injury and its adverse consequences. This study highlights the need to support all staff in the post COVID era, including those who worked in a non-COVID environment.

Public preferences for edible invasive alien marine species - The Atlantic blue crab in southern Italy

Since 2014, the provision of the aquatic ecosystem services has been gradually affected due to the biological invasion of Callinectes sapidus (Rathbun, 1896, Crustacea, Decapoda, Portunidae), commonly known as Atlantic blue crab, across several lagoon-like locations in Italy. In addition, this serious aquatic invasive species, native of North American coasts, has already inflicted economic damage of about EUR 100 million to the Italian fishing and farming communities over the past year. To counter their severe and rapid spread, the Italian Government has encouraged the fishing communities to catch as many as possible and commercially exploit them for human consumption in an attempt to manage their expansion. Since there is an ongoing promotion for the consumption of blue crab by forging novel food businesses in Italy, this paper aims to predict the public preferences and their willingness to pay (WTP) towards this biological invader. For this purpose, a discrete choice experiment approach is used, by means of a multinomial logit model (MNL) and latent class model (LCM). The social field survey involves a representative sample of 440 valid respondents in Apulia Region, southern Italy. The descriptive statistics results reveal that 67.50% of the local citizens interviewed know about the blue crab invasion, while 29.09% of them have already consumed this seafood species. In addition, the MNL results show that the most appreciated attributes of the blue crabs by respondents are freshness and large size. Further, the LCM findings reveal two representative classes of Apulian consumers; the first group of citizens (70.9% of total respondents) expresses positive appreciation for consuming blue crabs, while the second group (29.1% of total respondents) is not willing to pay a premium price regarding this potential commercial activity. Furthermore, the econometric results show that the average value that Apulian’s (i.e. those belonging to Class/Group I of respondents) WTP for blue crabs’ consumption is about EUR 18 per kilogram. In this regard, this research has public and private implications and may reasonably promote the commercial exploitation of blue crabs, enhancing the reduction of its population density through human consumption as a promised management control strategy and forging a novel profitable business mainly for local small-scale artisanal fisheries.

Artemisia campestris L. as a promising source of potential antiviral drugs for SARS-CoV-2: Docking and dynamic simulation studies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been considered as global public health security threats. Due to its rapid spread, high mortality rate and unavailability of treatment, we need to find a potent drug against SARS-CoV-2. Natural products are useful agents for the discovery of new potential drugs to combat coronavirus. Artemisia campestris, an aromatic plant widely used in traditional medicine, particularly in southern region of Algeria, is recognized for its essential oils and phenolics compounds possessing a wide range of biological activities. This study selected sixty-nine compounds from this plant to determine their binding interactions with the SARS-CoV-2 main protease (Mpro) and receptor-binding domain of the spike (S-RBD) protein, by using computational methods. Rutin, isoquercetin, and quercetin-3-O-glucuronide were shown to be the most potent inhibitors for Mpro and S-RBD with docking scores and Ki values ranging from -16.06 to -10.67 Kcal/mol and 0.005 to 30.01 nM, respectively. Evaluation of ADMET pharmacokinetic properties and the drug likeness in silico revealed that only 3,5-dicaffeoylquinic acid, 3–4–5-tricaffeoylquinic acid, isorhamnetin-3-O-glucoside, and rubescensin A could be more effective drugs against COVID-19. Molecular dynamics (MD) simulations (100 ns) and MM-GBSA calculations confirmed the stability of ligand-protein complexes via hydrogen bonding interactions with crucial residues. The analysis of structural parameters (RMSD, RMSF, H-bonds, Rg, and SASA) indicates that 3,4,5-tricaffeoylquinic acid and rubescensin A compounds have good stability and significant binding affinity with the Mpro and S-RBD protein. Taken together, our findings confirm that Artemisia campestris as a plausible source of anti-SARS-COV-2 phytochemicals and suggest that may play important role in this activity.

The heterologous expression of novel recombinant protein composed of HN and F moieties of Newcastle disease virus and immunogenicity evaluation in mouse model

Background and Objectives: The rapid spread of Newcastle disease (ND), driven by extensive commercial exchange in the poultry industry, necessitates urgent preventive measures. Although effective vaccines against the Newcastle disease virus (NDV) have been used since 1940, recent outbreaks and the limitations of current vaccines highlight the need for improved solutions. Advances in synthetic biology, reverse vaccinology, molecular biology, and recombinant DNA technology over the past 20 years have led to the development of recombinant vaccines, which offer enhanced protection and broader immuno- genic coverage against NDV. This study aimed to express the immunogenic domains of Hemagglutinin Neuraminidase (HN) and Fusion (F) glycoproteins, linked to the heat-labile enterotoxin B subunit (LTB) bio-adjuvant, to develop an effective and reliable recombinant vaccine for NDV. Materials and Methods: In this study, the L(HN)2F protein, composed of the LTB bio-adjuvant and the immunogenic regions of the doubled Hemagglutinin Neuraminidase (HN-HN) and Fusion (F) epitope, was expressed in Escherichia coli. Subcutaneous injection was used to evaluate the humoral immune response in mice and the result was compared with B1 vaccine. Results: The induction of strong humoral immune responses proved the strong immunoreactivity of the recombinant protein. Conclusion: The IgG elicited by the recombinant proteins was comparable to that of the commercial B1 vaccine against NDV, indicating its potential as a viable candidate for further development and evaluation as a recombinant vaccine against NDV.

Relationship Between Mediterranean Diet Adherence and Body Composition Parameters in Older Adults from the Mediterranean Region

In recent decades, the rapid spread of various communication media has led to changes in traditional eating habits. In the Mediterranean region, the classic (Mediterranean) dietary pattern has been lost as a result. This has led to a shift in eating habits towards unhealthy eating patterns, which in turn has resulted in an inadequate distribution of body composition. It is known that, among other things, the number of non-communicable diseases increases with the inadequate distribution of body composition. The aim of our study was to examine the level of adherence to the Mediterranean diet (MD) of older adults in the Mediterranean region in relation to specific body composition parameters. This study included 521 older adults with a mean age of 69.6 ± 6.3 years. Body composition was measured using the BIA 101 Anniversary device (Akern s.r.l., Florence, Italy) and adherence to the MD was assessed using the MEDLIFE index questionnaire. This study found significant differences in body composition between males and females. The mean adherence to the MD was 17.0 ± 3.3 points among the participants and there was higher adherence in females (p = 0.002, ηp2 = 0.019). A multiple linear regression was performed to assess the relationship between the body composition parameters and MD. Multiple linear regression models were significant for reactance, fat mass (%), fat-free mass (%), skeletal muscle index, and total body water (%), with specific individual MEDLIFE items such as the consumption of processed meat, meat, white meat, fruit, vegetables, olive oil and limiting snacks between meals. Moreover, promising correlations were found between certain MD characteristics and BIA parameters, but the overall health effects of the MD remain unclear.

Digital Communication Technology is not the Only Cause of Rapid Spread of Misinformation in Society

While achieving technological development in media communication, it is important to recognize the dual nature of technological advancement. This rapid development has led to a great threat of spreading misinformation, where ideas are no longer geographically restricted and spread globally at an unprecedented rate (Wang et al., 2019). Statistics from the Central Statistical Office (CSO) 2021 show that 62% of Internet users have seen untrue or suspicious content online Georgiev (2024). Many people believe that digital communication technologies are the only reason for the accelerated spread of misinformation. On the contrary, I believe that this view ignores the more complex political motivations, social, cultural, and psychological underpinnings. While technology itself is an important agent in the process of spreading misinformation, other factors are also integral. Therefore, I do not agree that the cause lies solely in digital communication technology, and this paper will illustrate how technological and social factors interact with each other through examples.

Mitochondrial Network Branching Enables Rapid Protein Spread with Slower Mitochondrial Dynamics

Mitochondrial network structure is controlled by the dynamical processes of fusion and fission, which merge and split mitochondrial tubes into structures including branches and loops. To investigate the impact of mitochondrial network dynamics and structure on the spread of proteins and other molecules through mitochondrial networks, we used stochastic simulations of two distinct quantitative models that each included mitochondrial fusion and fission, and particle diffusion via the network. Better-connected mitochondrial networks and networks with faster dynamics exhibit more rapid particle spread on the network, with little further improvement once a network has become well connected. As fragmented networks gradually become better connected, particle spread either steadily improves until the networks become well connected for slow-diffusing particles or plateaus for fast-diffusing particles. We compared model mitochondrial networks with both end-to-end and end-to-side fusion, which form branches, to nonbranching model networks that lack end-to-side fusion. To achieve the optimum (most rapid) spread that occurs on well-connected branching networks, nonbranching networks require much faster fusion and fission dynamics. Thus, the process of end-to-side fusion, which creates branches in mitochondrial networks, enables rapid spread of particles on the network with relatively slow fusion and fission dynamics. This modeling of protein spread on mitochondrial networks builds toward mechanistic understanding of how mitochondrial structure and dynamics regulate mitochondrial function. Published by the American Physical Society 2024

DEVELOPING A WEB BROWSER EXTENSION TO PREVENT THE SPREAD OF FAKE NEWS

Fake news causes huge social problems such as misdirecting and provoking the masses, creating an atmosphere of chaos by spreading fear. Detecting and stopping the dissemination of fake news has been an important and priority issue due to its rapid spread, difficulty to detect, and negative effects. In our study, a new Chrome extension that detects fake news has been developed in order to detect and prevent the spread of fake news. In the study; natural language processing, data mining methods, and various machine learning techniques for instance Passive Aggressive, Random Forest, Support Vector Machine, AdaBoost, XGBoost, and Long-Short-Term Memory (LSTM) algorithms are used. The accuracy rates of the algorithms, confusion matrices, AUC rates, and ROC curves were compared. The created machine learning model has been implemented in the online internet environment by using Flask and Rest API in the Python program. Finally, the Chrome extension interface was built using Javascript, HTML, and CSS. The LSTM algorithm gave the highest prediction result with a rate of 90.72% compared to other algorithms.

What influences the development of wine-producing regions? New economic opportunities in the eastern foothills of the Helan Mountains in China

The formation and development of wine regions require unique natural conditions and hundreds of years of technical and cultural accumulation. The development of the wine industry at the eastern foot of Helan Mountain in China, in addition to the appropriate geographical environment, the technical migration, and talent flow supported by consistent policies, have improved the allocation ability of resource factors and conversion efficiency, making it a world-renowned new wine-producing region in just 40 years. This particular mechanism deserves attention as new industries develop in other parts of the world. Using the methods of spatial autocorrelation, hot spot analysis, and geographic detector, this paper comprehensively analyzes the growth and spatial evolution characteristics of wineries in the eastern foot of Helan Mountain in China and deeply discusses the mechanism of the interaction between the localization of global floating capital and China's unique regional economic policies. It is found that the local selection of global floating capital has a spatial locking effect on the formation of CHMWR, and the rolling support policies of China's central and local governments promote the rapid expansion and regional spread of the number and scale of wineries, with significant spatial agglomeration and spatial diffusion. The profound mechanism of such changes lies in the dual interaction of the local selection of global liquid capital and the Chinese government's wine-producing policies adapted to the geographical environment. In this process, a series of internal and external factors such as the innovation of grape planting and wine-making technology, consumer market, and government behavior have accelerated the continuous growth of the wine-producing region at the eastern foot of Helan Mountain in Ningxia. This paper adds a new understanding and deep understanding of the interaction between the local implantation of economic globalization and the macro-control policies of the Chinese government.

Revolutionizing Influenza Treatment: A Deep Dive into Targeted Drug Delivery Systems.

Influenza, a highly transmissible respiratory infection caused by influenza viruses A and B, poses a persistent threat to global public health due to its high mutation rate, ability to develop resistance to existing antiviral drugs, and capacity for rapid spread. Current treatment options, including four main classes of antiviral agents-adamantanes, neuraminidase inhibitors, RNA-dependent RNA polymerase inhibitors, and polymerase acidic endonuclease inhibitors- are limited by the emergence of drug-resistant viral strains, non-specific drug distribution, and adverse side effects. Moreover, the effectiveness of traditional vaccines is often compromised by antigenic drift and shift, necessitating the development of alternative therapeutic strategies. This review comprehensively explores the potential of novel targeted drug delivery systems to address these limitations and improve influenza management. Nanotechnology-based platforms, including lipid-based, polymer-based, inorganic, and hybrid nanoparticles, offer enhanced drug delivery through improved bioavailability, targeted action, and controlled release, thus minimizing systemic toxicity and optimizing therapeutic outcomes. Inhalation delivery systems such as dry powder inhalers (DPIs), nebulizers, and nanotechnology-based inhalation formulations provide direct delivery of antiviral agents to the respiratory tract, ensuring rapid onset of action with reduced systemic side effects. Transdermal delivery methods, including microneedle patches and hydrogel-based systems, offer non-invasive alternatives that enhance patient compliance and allow for sustained drug release. Furthermore, this review discusses recent innovations, such as responsive drug delivery systems and multifunctional nanoparticles capable of simultaneous delivery of multiple therapeutic agents, representing a significant advancement in the fight against influenza. These novel approaches promise improved targeting and efficacy and enable personalized treatment strategies, enhancing patient outcomes in both seasonal flu and pandemic scenarios. Integrating these advanced drug delivery systems into clinical practice could revolutionize the management of influenza, offering a promising pathway toward more effective and safer therapies.

The establishment and potential spread of Osmia cornuta (Hymenoptera: Megachilidae) in North America.

Mason bees, subgenus Osmia Panzer (Hymenoptera: Megachilidae), are economically and ecologically significant pollinators. In eastern North America, the rapid spread of 2 non-native species from Asia, Osmia cornifrons Radoszkowski and Osmia taurus Smith, has coincided with declines in native Osmia populations, raising concern about the effects of further exotic arrivals. Here we investigate the recent establishment in British Columbia, Canada of the European orchard bee, Osmia cornuta Latreille, previously thought to be limited to Europe and its periphery. We document O. cornuta records ranging more than 170 km, including sightings of live adults and the discovery of a multigenerational nest with hundreds of cocoons. We tested whether these cocoons could be discriminated from other Osmia species by training a machine learning classifier on features extracted from images. The best performing model could not reliably discriminate cocoons by species, raising the possibility O. cornuta could be inadvertently intermingled in future commercial shipments. Recent occurrence records of O. cornifrons and O. taurus were spatially isolated, suggesting ongoing anthropogenic dispersal of these species. We predicted the suitability of North American habitats for O. cornuta by estimating its native climate niche. This analysis indicated broad regions of the Pacific Northwest and eastern North America contain potentially suitable habitat. Our findings document the establishment of O. cornuta in North America and the potential for its expansion. Our study demonstrates the utility of accessible biodiversity data archives and public observation programs in tracking non-native species spread and highlights the need for future monitoring of exotic Osmia.

Outbreak with OXA-23-producing Acinetobacter baumannii in a COVID-19 ICU cohort: unraveling routes of transmission.

An outbreak of OXA-23-producing carbapenem-resistant Acinetobacter baumannii amongst ICU-patients with COVID-19 likely occurred by transmission through inanimate surfaces, potentially facilitated by a contaminated positioning pillow shared between patients. Subsequent rapid spread may have been caused by exposure to respiratory secretions contaminating healthcare worker's gloves and gowns during prone positioning.

Development of an Eco-Friendly Nanogel Incorporating Pectis brevipedunculata Essential Oil as a Larvicidal Agent Against Aedes aegypti

Background/Objectives: Arboviruses, transmitted by mosquitoes like Aedes aegypti, pose significant public health challenges globally, particularly in tropical regions. The rapid spread and adaptation of viruses such as Dengue, Zika, and Chikungunya have emphasized the need for innovative control methods. Essential oils from plants, such as Pectis brevipedunculata (Gardner) Sch.Bip. (Pb), have emerged as potential alternatives to conventional insecticides. Methods: In this work, we developed an eco-friendly nanogel using a low-energy, solvent-free method, incorporating the copolymer F127 and Carbopol 974p, enriched with a high concentration of essential oil from Pb (EOPb). The resulting nanogel displayed excellent physical stability, maintained under varying temperature conditions. Characterization techniques, including FTIR and DLS, confirmed the stable incorporation of EOPb within the nanogel matrix. Results: The in vitro assays against Aedes aegypti larvae revealed that at 500 μg/mL, the mortality rates were 96.0% ± 7.0 after 24 h and 100.0% ± 0.0 after 48 h. The positive control group treated with temefos, achieved 100% mortality at both time points, validating the experimental conditions and providing a benchmark for assessing the efficacy of the nGF2002Pb nanogel. Conclusions: These results indicate that nGF2002Pb demonstrates a pronounced concentration-dependent larvicidal effect against Aedes aegypti, offering an innovative and sustainable approach to arbovirus vector control.

Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images

Background The COVID-19 pandemic has had a significant influence on economies and healthcare systems around the globe. One of the most important strategies that has proven to be effective in limiting the disease and reducing its rapid spread is early detection and quick isolation of infections. Several diagnostic tools are currently being used for COVID-19 detection using computed tomography (CT) scan and chest X-ray (CXR) images. Methods In this study, a novel deep learning-based model is proposed for rapid detection of COVID-19 using CT-scan images. The model, called pre-trained quantum convolutional neural network (QCNN), seamlessly combines the strength of quantum computing with the feature extraction capabilities of a pre-trained convolutional neural network (CNN), particularly VGG16. By combining the robust feature learning of classical models with the complex data handling of quantum computing, the combination of QCNN and the pre-trained VGG16 model improves the accuracy of feature extraction and classification, which is the significance of the proposed model compared to classical and quantum-based models in previous works. Results The QCNN model was tested on a SARS-CoV-2 CT dataset, initially without any pre-trained models and then with a variety of pre-trained models, such as ResNet50, ResNet18, VGG16, VGG19, and EfficientNetV2L. The results showed the VGG16 model performs the best. The proposed model achieved 96.78% accuracy, 0.9837 precision, 0.9528 recall, 0.9835 specificity, 0.9678 F1-Score and 0.1373 loss. Conclusion Our study presents pre-trained QCNN models as a viable technique for COVID-19 disease detection, showcasing their effectiveness in reaching higher accuracy and specificity. The current paper adds to the continuous efforts to utilize artificial intelligence to aid healthcare professionals in the diagnosis of COVID-19 patients.

Deep Traces of Social Media: A Study on its Economic and Psyshological Effects

The rapid spread of social media platforms has deeply affected modern societies and led to significant changes in economic and psychological fields. This article aims to examine the deep traces of this phenomenon by focusing on the economic effects and psychological consequences of social media. It covers topics such as the economic effects of social media, its effects on consumer behavior, the transformation of advertising and commerce, as well as revenue models and innovations in the business world. In particular, the development of digital marketing strategies, brand awareness creation processes and their effects on businesses' customer relationship management were examined. In terms of psychological effects, the effects of social media on individuals, social connections, self-perception and psychological health are discussed. New psychological conditions such as loneliness, anxiety, social comparison and digital fatigue are among the problems that arise with the widespread use of social media. This article provides a critical review to understand the complex effects of social media on today's societies and lay the foundation for future research.

Antimicrobial Resistance: What Lies Beneath This Complex Phenomenon?

Antimicrobial Resistance (AMR) has evolved from a mere concern into a significant global threat, with profound implications for public health, healthcare systems, and the global economy. Since the introduction of antibiotics between 1945 and 1963, their widespread and often indiscriminate use in human medicine, agriculture, and animal husbandry has led to the emergence and rapid spread of antibiotic-resistant genes. Bacteria have developed sophisticated mechanisms to evade the effects of antibiotics, including drug uptake limitation, drug degradation, target modification, efflux pumps, biofilm formation, and outer membrane vesicles production. As a result, AMR now poses a threat comparable to climate change and the COVID-19 pandemic, and projections suggest that death rates will be up to 10 million deaths annually by 2050, along with a staggering economic cost exceeding $100 trillion. Addressing AMR requires a multifaceted approach, including the development of new antibiotics, alternative therapies, and a significant shift in antibiotic usage and regulation. Enhancing global surveillance systems, increasing public awareness, and prioritizing investments in research, diagnostics, and vaccines are critical steps. By recognizing the gravity of the AMR threat and committing to collaborative action, its impact can be mitigated, and global health can be protected for future generations.

ADA-UDA: A transferable transformer framework for rumor detection using Adversarial Domain Alignment within Unsupervised Domain Adaptation

The rapid spread of misinformation on social media has posed significant challenges, particularly in the early detection of rumors, which is critical to mitigating their negative impact. However, the limited data available during the initial stages of trending topics poses significant challenges, including analysis constraints and model overfitting. Therefore, the adoption of specific methods are needed to identify patterns from historical sample data and enable effective knowledge transfer, facilitating inductive reasoning with limited data on new topics. To address these challenges, we propose an Adversarial Domain Alignment Unsupervised Domain Adaptation (ADA-UDA) method based on the Transformer architecture. Our approach leverages labeled historical data from the source domain alongside a limited quantity of unlabeled data from new trending topics in the target domain. At the heart of our method is the Parameter Transferable Module (PTM), which guides the Transformer to focus on transferable and distinguishable features, thereby enhancing the model’s ability to perform effective inductive reasoning with limited data. We conducted extensive experiments to evaluate our method, benchmarking it against current mainstream rumor detection techniques. The findings indicate that our ADA-UDA method outperforms existing approaches, underscoring its potential for early and accurate rumor detection in emerging topics.