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Biomechanical improvements in performance and injury prevention in volleyball spikes: effects of a 4-week training program
Investigation of the effect and mechanisms of moxa smoke in the treatment of Influenza A Virus (IAV) infection
Influenza, primarily caused by the Influenza A virus, is a highly contagious respiratory disease. While moxa burning is a traditional method used to reduce respiratory infections, most studies have focused on the components of moxa and air disinfection, often neglecting the pharmacological effects and mechanisms of moxa smoke. This study aimed to explore the antiviral and anti-inflammatory effects of moxa smoke in vivo, as well as the underlying mechanisms involved. Utilizing multiple databases, we identified 52 components of moxa smoke that target 384 proteins, with 92 of these potentially linked to protection against H1N1. Network analysis conducted using Cytoscape revealed 16 core targets, including PPARG and STAT3. We performed molecular docking to verify the stable binding affinities of core compounds with their corresponding targets. In vivo experiments demonstrated that moxa smoke significantly decreased the number of inflammatory cells in bronchoalveolar lavage fluid (BALF), lowered the levels of H1N1 nucleoprotein (H1N1NP), and reduced the mRNA expression of cytokines with chemokines in lung tissue, including Il-6, Il-1β, Tnf-α, Cxcl1, Cxcl2, Cxcl10 and Ccl2. These results suggest a reduction in lung inflammation in mice infected with the PR8 strain of the IAV. Western blot analysis indicated that moxa smoke upregulated PPARγ and reduced phosphorylated STAT3 levels. GW9662 inhibited the reduction of recruitment of inflammatory cells by moxa smoke, but didn’t inhibit the reduction of viral load after moxa smoke treatment. A four-day treatment did not cause functional injury to the lungs, kidneys, or liver of H1N1-infected mice. However, after four weeks of exposure to moxa smoke, the mice exhibited changes in organ weight and pathological damage in the lungs and kidneys. In summary, Moxa smoke suppressed influenza virus-induced inflammatory cell infiltration by upregulating PPARγ, while simultaneously reducing viral load through PPARγ-independent mechanisms. Short-term exposure to moxa smoke did not cause significant impairment of pulmonary, hepatic or renal function; however, prolonged exposure may result in respiratory and renal dysfunction, potentially leading to more severe adverse effects.
Employing feedforward backpropagated neural network for Doppler scale estimation in underwater acoustic CP-OFDM communication
Framework to prioritize health outcomes of particulate matter exposure using national claims data
Objectives Although particulate matter (PM) exposure poses significant public health risks, previous research has focused on limited clinical areas. However, emerging evidence and pathological mechanisms of PM suggest that PM may exert broader systemic effects across a wide range of diseases. Therefore, we aim to identify and prioritize research questions to evaluate health impacts of PM exposure across various clinical specialties. Methods A structured collaborative process was conducted between April and November 2024 in South Korea, incorporating systematic literature reviews, multidisciplinary expert discussions, and knowledge-sharing seminars. The primary outcomes were the identification of diseases potentially influenced by PM exposure and the development of corresponding research questions. The literature review synthesized more than 417 publications, including the U.S. Environmental Protection Agency’s integrated science assessment materials, a government-issued abstract compendium on PM covering 2010–2019, and studies published from 2020 to 2024 identified via a structured search. These were categorized by exposure duration (short- or long-term) and diseases outcome (incidence or progression). Prioritization was based on three criteria: pathological causality, clinical impact (public health burden), and feasibility using the Korea National Health Insurance Service (K-NHIS). Results A total of 99 experts from epidemiology, data science, and 14 clinical specialties participated. The experts panel (mean age: 46.1 years; mean professional experience: 20.5 years) identified 211 research questions across 80 diseases. These were classified by disease outcome: disease incidence (short-term, 54; long-term, 64) and progression (short-term, 47; long-term, 46). Notably, several clinical areas such as ophthalmology, dermatology, and otolaryngology were underrepresented. Conclusion This structured, multidisciplinary approach broadened the scope of PM-related clinical research beyond commonly studied clinical area. This scalable framework can be adapted in other regions with similar claims data systems to guide evidence-based research agendas and inform public health policies.
Data assimilation reveals behavioral dynamics of sea cucumbers as a model for slow-moving benthic animals
Abstract Understanding the movement behavior of Japanese sea cucumbers ( Apostichopus japonicus ) is essential for ecological research and fisheries management. However, tracking their locomotion is challenging due to their slow movement and environmental variability. In this study, we employed acoustic telemetry combined with a data assimilation approach using the Kalman filter to estimate movement trajectories with high accuracy, overcoming the limitations of traditional visual tracking methods. To characterize movement complexity, we applied fractal dimension analysis, quantifying the randomness and variability of individual locomotion across different environmental conditions. Additionally, we examined the influence of key environmental factors, including water temperature, diel cycles, and boulder presence, using Generalized Linear Models (GLM). The results indicate that during the growing stage, higher water temperatures significantly increased movement activity, while boulder zones influenced movement differently depending on the season. This study also provides long-term tracking data on released sea cucumbers, offering new insights into their settlement and dispersal patterns. By combining acoustic telemetry, data assimilation, fractal analysis, and statistical modeling, we established a framework to investigate the behavioral dynamics of slow-moving benthic organisms. These findings enhance our understanding of sea cucumber ecology and provide a quantitative framework for future studies on marine invertebrate movement.
Epigenome analysis of an algae-infecting giant virus reveals a unique methylation motif catalogue
DNA methylation can epigenetically alter gene expression and serve as a mechanism for genomic stabilization. Advancements in long-read sequencing technology have allowed for increased exploration into the methylation profiles of various organisms, including viruses. Studies into the Nucleocytoviricota phylum of giant dsDNA viruses have revealed unique strategies for genomic methylation. However, given the diversity across this phylum, further inquiries into specific lineages are necessary. Kratosvirus quantuckense (formerly known as Aureococcus anophagefferens Virus, AaV) is predicted to encode six distinct methyltransferases, which bear homology to other methyltransferases across the many clades of Nucleocytoviricota . We found that the virus’ DNA is methylated with high consistency, including nine different motifs targeted for DNA adenine methylation. Methylation levels varied depending on the associated motif. Likewise, distinct motifs were enriched within unique genomic regions. Collectively our data suggest that each methyltransferase targets unique DNA regions, suggesting they have varying functionality. This work reveals an array of methyltransferase activity in Kratosvirus quantuckense and implicates the importance of DNA methylation to the Nucleocytoviricota infection cycle.
Hybrid XGBoost-RF-MLP model and PSO optimization for performance and emissions of CI engine using waste cooking biodiesel blends
Abstract Transesterification was used to create methyl ester from waste cooking oil (WCO). Diesel oil and biodiesel blends in 25, 50, 75, and 100% were developed and authorized by ASTM. The primary contribution of this study lies in integrating experimental WCO biodiesel data with a novel hybrid machine learning and Particle Swarm Optimization (PSO) framework. A hybrid model, combining XGBoost, Random Forest, and MLP, was developed to predict engine performance and emissions. The core novelty is the use of base model predictions as meta-features for a final meta-learner, createing a superior stacked ensemble. This hybrid model was then coupled with PSO to identify optimal engine operating conditions. Key experimental results revealed that pure biodiesel (B100) reduced CO, HC, and smoke emissions by 25%, 43%, and 45%, respectively. However, increased NOx emissions by 23% and brake-specific fuel consumption by 22% were shown compared to diesel at full load. Crucially, the hybrid model demonstrated exceptional predictive accuracy, achieving a significantly lower Mean Squared Error (MSE in the order of 10⁻⁷) across all 13 output parameters compared to the individual MLP (MSE ~ 10⁻ 3 ), RF (MSE ~ 10⁻⁴), and XGBoost (MSE ~ 10⁻⁶) models. The PSO algorithm successfully converged to an optimal solution of 86% engine load and 26% biodiesel blend (B26), maximizing the defined fitness function that balanced performance and emissions. The results unequivocally demonstrate that the proposed hybrid modeling approach offers a robust and highly accurate framework for engine optimization, establishing WCO biodiesel as a viable alternative fuel when used in optimal blends.
Computational analysis of stochastic delay dynamics in maize streak virus
Objectives The primary goal of this research is to analyze the transmission dynamics of Maize Streak Virus (MSV) by means of a computational and stochastic modeling technique where the time delay and uncertainty factors in the epidemic process are vital considerations. Methodology A compartmental MSV deterministic model was established, which later got an extension to a stochastic delay differential system having five biological compartments consisting of susceptible, insecticide-treated, exposed, infected, and recovered plants. Analytical methods were employed to find the maize streak–free and endemic equilibriums and to derive the treatment reproduction number. The stability of the deterministic and stochastic systems was studied. The numerical methods used for comparison were Euler-Maruyama, stochastic Runge–Kutta, and the stochastic Nonstandard Finite Difference (NSFD) scheme, which were assessed for accuracy, stability, and computational efficiency. Key Results Theoretical results show that under some parameter values, both equilibrium points are stable in an asymptotic sense. The numerical experiments reveal that the stochastic NSFD scheme is more stable, preserves positivity better, and is independent of step size than the classical methods. Including the stochasticity captures the uncertainty associated with MSV transmission in the real world, thereby enhancing the predictive simulation’s validity. Conclusions The suggested stochastic NSFD model is indeed a strong computationally efficient and biologically realistic method to simulate MSV and other plant virus epidemics. The results boost our understanding and management of the agricultural disease control strategies.
Multidimensional assessment of large language model responses to patient questions on gestational diabetes mellitus
The impact of influencer marketing in the tourism industry: A digital marketing perspective
This study aims to examine how influencer marketing shapes tourists’ perceptions and purchase intentions in the tourism sector of Bangladesh. Specifically, it examines the impact of word of mouth, content characteristics, consumer trust, emotional connection, and brand awareness on consumer perception, as well as how these perceptions influence purchase intention within a digital marketing context. A quantitative, cross-sectional design was employed, utilizing a structured online questionnaire distributed to active social media users who follow travel influencers. Data were collected from 400 respondents through non-probability purposive sampling, representing individuals familiar with influencer-generated travel content. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed hypotheses and evaluate both measurement and structural models. Results revealed that word of mouth (β = 0.269, p < 0.001), content characteristics (β = 0.152, p < 0.05), consumer trust (β = 0.207, p < 0.05), emotional connection (β = 0.170, p < 0.05), and brand awareness (β = 0.200, p < 0.001) significantly influence consumer perception. In turn, consumer perception strongly predicts purchase intention (β = 0.561, p < 0.001). All five influencer-related factors; word of mouth, content characteristics, trust, emotional connection, and brand awareness significantly influence consumer perception, which in turn strongly predicts purchase intention. Among these, trust, word of mouth, and content quality were found to be the most influential predictors of success. The findings confirm that credible and engaging influencer communication enhances tourists’ perceptions and motivates travel-related purchasing behavior. The study provides actionable insights for tourism marketers and policymakers, emphasizing the importance of authenticity, message quality, and trust-building in influencer collaborations. It also highlights the need for integrating influencer campaigns into broader destination branding strategies. This research contributes theoretically by integrating Social Influence Theory and Source Credibility Theory into a single model. It provides new empirical evidence from an emerging tourism market (Bangladesh), expanding cross-cultural understanding of influencer marketing’s role in shaping consumer behavior in digital tourism.
Productivity prediction, dynamic evaluation and reservoir heterogeneity analysis of HS 4 block carbonate gas reservoir
Applications of newly defined diamond Pythagorean fuzzy CODAS method via multi-criteria decision-making problems
The diverse decision values may fail to capture an accurate perspective when multiple decision-makers are part of the process. To address this challenge, this work introduces the diamond Pythagorean fuzzy set (Dia‑ PyFS ), an advancement over both the Pythagorean fuzzy ( PyFS ) set and the interval-valued Pythagorean fuzzy set ( IVPyFS ). Due to the established extension of intuitionistic fuzzy sets into Pythagorean fuzzy sets, the Dia‑ PyFS model, a broader form of the diamond intuitionistic fuzzy set model, demonstrates enhanced performance. We then introduce the Dia‑ PyFS as an extension of PyFS . The Diamond Pythagorean fuzzy values that define the elements within Dia‑ PyFS may share a common norm. For Dia‑ PyFS , we define fundamental algebraic and arithmetic operations, including union, intersection, addition, multiplication, and scalar multiplication, and analyze their primary properties. Additionally, we propose some new Dia‑ PyF weighted average and geometric aggregation operators as well as explore their unique properties. We also then propose several algebraic operations between Dia‑ PyFVs using general triangular 𝓉 -norms and 𝓉 -conorms. To transform input values represented by Dia‑ PyFs into a single output value, we also introduce specific weighted aggregation operators based on these algebraic methods. Additionally, the Dia‑ PyFS framework builds upon the “combinative distance-based assess” ( CODAS ) methodology, which relies on both Euclidean and Hamming distances. To illustrate the applicability of this new approach, the feasibility and suitability of the Dia‑ PyFS set approach for choosing the best options are demonstrated by the summary and comparative analysis of the produced reports.
Pre and postharvest application of regulators enhances bell pepper quality and antioxidant levels during cold storage
As time goes by: Long-term retention of economics skills
The vast majority of research on student learning is based on assessments of student knowledge given during or at the end of an academic term. Until now, we have known very little about what knowledge students retain after a course is over or what determines how much they retain. In this paper, we analyze data collected from students who took one of six courses in introductory or intermediate microeconomics. All students in these courses took a low-stakes standard assessment of their learning at the end of the term. At follow-ups, one to 2.5 years later, these students were surveyed about their academic and job-related activities, and given the same assessment they took at the end of the course. We find that some demographic characteristics and prior preparation for the course are strong predictors of how much students retain while initial attitudes toward economics are not. We also find evidence that for some students, application of economic skills in subsequent jobs and courses helps students retain course skills.
Use of gilsonite in the presence of salt for the control of formation and stability of aphrons
A multi-image codebook approach for secure text transmission
In modern digital communication, Confidentiality of text transmission is remains a concern in the current online communication as cyber threats and intrusion. To address these challenges, this paper proposes a dual-layered security system that integrates cryptography and multi-image steganography to strengthen text protection during transmission. The cryptography layer is done based eight steps; in the first one, the message is converted to ASCII format, then convert the ASCII values into their equivalent binary numbers and make a complement to the binary values where each 0’s becomes 1’s and vice versa. In the next step, it needs to enter a key that includes a combination of characters, numbers, and special characters. This key is also converted to binary, and then the XOR operation is made between the message of the binary values and the key. In the fifth step, switching the values of each two adjacent binary values are together and converted to decimal values. While the second layer embeds the ciphertext in several cover images using a randomized codebook along with the Least Significant Bit (LSB) substitution, thus enhancing undetectability. Experimental evaluation demonstrates fast execution times for both the encryption/decryption processes and the multi-image hiding/extraction procedures. The achieved results validate that the proposed system provides an efficient and highly secure framework for protecting sensitive information.
Optimised MobileNet for very lightweight and accurate plant leaf disease detection
Abstract The development of accurate and efficient plant disease classification systems is vital for addressing the challenges of climate change and the growing global demand for food. This study presents $$\hbox {V}^2$$ PlantNet, a novel lightweight multi-class classification model based on a modified MobileNet architecture, designed to detect plant leaf diseases across a diverse range of crop types. $$\hbox {V}^2$$ PlantNet employs depthwise separable convolutions to significantly reduce model complexity without compromising accuracy. The architecture integrates Batch Normalization (BN) and Rectified Linear Unit (ReLU) activation after each convolutional layer, while a multi-stage design enhances feature extraction and overall performance. Despite its compact size, comprising only 389,286 parameters and requiring just 1.46 MB of memory, $$\hbox {V}^2$$ PlantNet achieved up to 99% training accuracy, with validation and test accuracies of 97% and 98%, respectively. Across most classes, precision, recall, and F1-scores ranged from 0.97 to 1.0, demonstrating consistent and robust generalization across diverse plant species. These architectural innovations enable $$\hbox {V}^2$$ PlantNet to outperform larger models such as ResNet-50 and Inception V3 in terms of computational efficiency, owing to its smaller model size (1.46 MB), reduced parameter count (389,286), and faster inference time (0.676 s), offering a scalable solution for real-time plant disease detection in precision agriculture.
Efficacy and safety of PM-AR-T versus edwards MC3 rings in tricuspid regurgitation: A non-inferiority, randomized controlled trial
Objectives Tricuspid valve repair, particularly with annuloplasty rings, is increasingly recognized as an effective treatment. PM-AR-T is a semi-rigid annuloplasty ring based on a nickel-titanium alloy which has made progress in animal models, however, studies on PM-AR-T’s performance in patients with tricuspid regurgitation (TR) are lacking. This study aimed to compare the efficacy and safety of the PM-AR-T with the Edwards MC3 ring for the TR treatment. Methods A non-inferiority, randomized controlled trial was conducted in 20 centers across China, enrolling patients with tricuspid valve disease requiring surgical repair. Patients were randomized to receive either PM-AR-T or Edwards MC3 ring. The primary endpoint was the success rate of valve repair at 6 months. Results A total of 164 patients underwent valve annuloplasty, 83 and 81 in the PM-AR-T and Edwards MC3 groups. Valve repair success rates were 92.8% and 93.8% in the PM-AR-T and Edwards MC3 groups, demonstrating non-inferiority with a difference of −1.1% (95% confidence interval [CI]: −9.5 to 7.4), which was less than the pre-specified non-inferiority margin of −10%. No significant intergroup differences were found in valve regurgitation, echocardiographic parameters, and New York Heart Association (NYHA) functional classification at any postoperative time point. At 12 months, the proportions of patients without regurgitation were comparable, 30.4% and 27.8% in the PM-AR-T and Edwards MC3 groups (P = 0.705). Improvement to NYHA functional class I status was detected in 46.2% and 45.6% of the two groups by 12 months (P = 0.893). Both rings exhibited comparable safety profiles, with no device-related serious adverse events, cardiovascular deaths, major bleeding events, severe structural damage, infective endocarditis, or thromboembolic events. Conclusions The PM-AR-T tricuspid valve semi-rigid ring is effective in improving TR, demonstrating non-inferiority to the Edwards MC3 ring, with a favorable safety profile. Clinical Trial Registration: This study was registered at Chinese Clinical Trial Registry (ChiCTR2100043007).