Browse Articles

Application of MALDI-TOF-MS in the surveillance of microbial diversity in butter production: a case study of Polish dairy

Abstract The primary objective of the study was to adapt and evaluate the utility of the rapid MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry) identification method for comprehensive microbiological safety monitoring across the farm-to-butter chain. The research was conducted as a detailed case analysis within a selected Polish dairy facility, focusing on the microbial profiling from the milking to the final product stage. The application of this method enabled comprehensive microbiota profiling of 400 milk samples, along with 63 additional samples from nine stages of the butter production process, resulting in the identification of 146 different cultivable microorganisms. The microbiota of fresh bovine milk was primarily composed of bacteria from the genera Staphylococcus , Corynebacterium , Aerococcus , and the species Bacillus licheniformis . The highest microbial diversity was observed in samples taken from tankers transporting milk to the dairy. Pasteurization effectively reduced the total number of microorganisms, but did not completely eliminate spore-forming or psychrophilic bacteria. The results emphasize the need to maintain a high level of hygiene at every stage of production in order to ensure microbiological safety and quality of dairy products.

Povidone iodine demonstrates strong efficacy in reducing Candida biofilm in an in vitro fungal prosthetic infection

Taxonomic descriptions of Nocardia anocheti sp. nov. and Streptomyces odontomachicola sp. nov. isolated from ants

Identifying intact and fibrotic parenchyma in pancreatic ductal adenocarcinomas using compression optical coherence elastography

Applying a gendered lens to the issue of adolescent social media use and well-being by exploring salient susceptibilities and alternative within-person processes

24-week multidimensional predictors of return to play post-ACLR in high-sports demanders: a randomized trial

Distribution, pollution status and controlling factors of trace metal(loid)s in Yellow river Estuary and adjacent area

Effect of sand particle size on the thermal effusivity of clay-admixed cement mortar bricks

Health risks of carbon monoxide and mitigation strategies in Abuja municipal area council, Nigeria

High-gain CRLH vivaldi antenna for enhanced channel performance at Ku-band communication systems

Palladium-Catalyzed Asymmetric Oxidative Amination of Internal α,β-Unsaturated Esters with Lewis Basic Amines

Evaluating AI models for food and alcohol advertisement classification against human benchmarks

Abstract The growth of food and alcohol marketing on social media creates a need for scalable monitoring methods that go beyond manual processing. This study evaluates whether Large Language Models and Vision-Language Models can recognize advertisements and identify their features in consistence with general public or expert opinion. We collected 1000 Facebook ads from major Belgian brands, and annotated them with 600 crowd workers, three dieticians and four AI models (GPT-4o, Qwen 2.5, Pixtral and Gemma3). Our analysis of the data shows that for single-option advertisement features, like alcohol presence or target group, GPT-4o and Qwen reached agreement with the dietician consensus above 90%, similar to the level of pairwise agreement observed between individual dieticians. Though agreement was lower for multiple choice features, like premium offers and marketing strategies, it was still within the variability observed in crowd raters. The bias analysis revealed how models interpret certain labels, with some being consistently under- or over-detected. Based on these findings, we propose tiered deployment recommendations that distinguish between ad features that MLLMs can already monitor with human-level accuracy, and more complex features requiring expert oversight and taxonomy refinement, like marketing strategies or food categories.

A novel decision analytic model for environmental sustainability challenges using interval-valued complex spherical fuzzy soft sets

Integration of machine learning and microstructural characterization for strength forecasting with silica fume and M-sand for sustainable concrete

Concurrent Hydrolysis Resistance and High Thermal Conductivity in Aluminum Nitride Enabled by Phase‐Engineered Graphene Encapsulation

ABSTRACT Aluminum nitride (AlN) stands as a cornerstone material for next‐generation thermal management, yet its notorious susceptibility to hydrolysis severely undermines long‐term reliability. Here, we transcend conventional surface modification by introducing a phase‐engineering strategy to fundamentally reconfigure the AlN surface. Through fluidized bed‐chemical vapor deposition, we precisely construct conformal, high‐crystalline and low‐defective graphene “skin” on AlN powders (the intensity ratio of D‐peak to G‐peak ∼0.088), where the unique growth kinetics and interfacial phase are dictated by the AlN substrate, thus differ from the conventional non‐metallic substrates. As revealed by density functional theory calculations, this process yields a covalently‐bonded heterointerface characterized by distinct C–Al–N configurations, thereby moving beyond weak van der Waals interactions. The phase‐engineered graphene skin delivers dual, synergistic functions, enhancing the thermal conductivity of AlN by 38.7% via optimized thermal transport pathways, while simultaneously acting as an ultrastable barrier, granting exceptional resistance to prolonged hygrothermal aging with the thermal conductivity variation of thermal interface material less than 1% in 30 days. This work resolves the long‐standing trade‐off between environmental stability and thermal performance in AlN, establishing a paradigm of phase‐engineered graphene encapsulation for ceramic fillers, thereby enabling the scalable fabrication of robust, hydrolysis‐resistant and high thermal conductivity composites.

Analysis and prediction of the burden of lung cancer attributable to diet low in fruits in China and the global from 1990 to 2021

Objectives To analyze the trend of the disease burden of lung cancer attributable to diet low in fruits among the Chinese and the global populations from 1990 to 2021, describe the disease burden situation in 2021, and predict the development trend of the disease burden attributable to diet low in fruits over the next 25 years, so as to provide scientific suggestions for the prevention and control of lung cancer. Methods The paper utilized data from the Global Burden of Disease Study 2021 (GBD 2021). The joinpoint regression model was employed to calculate the annual percentage change (APC) and the average annual percentage change (AAPC) to assess the changing trend of the burden of lung cancer attributable to diet low in fruits. The disease burden of lung cancer attributable to diet low in fruits was predicted for the next 25 years using a Bayesian age-period-cohort (BAPC) model. Results From 1990 to 2021, the number of mortality and disability-adjusted life years (DALY) of lung cancer attributable to diet low in fruits in China and the global increased significantly, while the age-standardized rates decreased significantly. In China, the estimated annual percentage change (EAPC) in the total population and different gender categories ranged from −4.0 to −2.8. The mortality number of lung cancer attributable to diet low in fruits in China increased with age, reaching a peak at 70−74 years. Similarly, the age-standardized DALY rate paralleled mortality rate trends across genders and age groups. The AAPC in age-standardized mortality and DALY rates were −2.69 and −3.15, respectively. According to the BAPC model prediction results that by 2046, the age-standardized mortality and DALY rates of lung cancer attributable to diet low in fruits in China and the global will decrease by 31.58%, 24.68%, 29.28%, and 24.34%, respectively. Conclusions From 1990 to 2021, the mortality and DALY rates of lung cancer attributable to diet low in fruits in China and the global both decreased. The disease burden of lung cancer attributable to diet low in fruits in male has always been higher than that in female, and the mortality and DALY rates were the highest among the elderly. It is expected that by 2046, the mortality and DALY rates of lung cancer attributable to diet low in fruits will further decrease.

ESC-YOLOv8: An enhanced deep learning framework for semantic understanding of single-line diagram imagery

Accurate interpretation of single-line diagrams (SLDs) is crucial for analyzing electrical systems, as they encapsulate vital information about operational safety and efficiency in a simplified format. Traditional SLD processing methods rely on manual inspection and basic image analysis, which are computationally intensive, error-prone, and require extensive preprocessing. Although deep learning has been applied to symbol classification, existing models often fail to capture fine-grained symbol details, leading to misclassification. To address these limitations, this study proposes a hybrid deep learning-based symbol classification method. A newly created dataset was benchmarked using state-of-the-art deep learning models, and an optimal model was systematically designed, developed, and tested. The proposed approach integrates a Hybrid Residual Attention Module (HRAM) to enhance the model’s ability to identify fine-grained symbol details and a Proximity-aware Loss Function to improve performance in cluttered regions by motivation of this work stems penalizing misclassifications based on the spatial proximity of neighboring symbols. These modifications result in an optimized method for semantic processing in symbol classification tasks. The proposed model achieves 93.5% mean average precision (mAP) a 3.8% improvement over the top-performing baseline, alongside a 19.6% reduction in model parameters. These advancements contribute to more efficient and accurate semantic processing of SLDs, paving the way for improved analysis of electrical system diagrams.

Multi-source harmonic estimation method for distribution networks based on variational modal decomposition

To address the limitation of harmonic monitoring on the low-voltage side of distribution networks, this paper proposes a multi-source harmonic estimation method based on variational mode decomposition. The method integrates short-term test data with long-term power data. First, dominant harmonic users are identified through a strategy that combines Fisher optimal segmentation and derivative dynamic time warping. Second, an electrical data transformation approach is designed by combining variational mode decomposition with Gramian angular fields, which maps the power signals of dominant harmonic users and low-voltage side harmonic signals into pseudo-color Gramian power images and grayscale Gramian harmonic images, respectively. Finally, an improved PSRGAN (pix2pix-super-resolution generative adversarial network) model is constructed to train and learn from these images, establishing the mapping relationship between power data and low-voltage side harmonic data of the distribution network, thereby enabling the migration and generation of long-term low-voltage side harmonic monitoring data. Simulation cases and field measurements validate the effectiveness and accuracy of the proposed method in multi-source harmonic scenarios. Moreover, the required data are easily accessible, demonstrating strong potential for engineering applications.

Successes and challenges of an online based nutrition awareness program in 9–11-year-old children in four Arab countries: The Ajyal Salima digital platform qualitative study

Introduction The rapid expansion of digital technologies has significantly influenced the lives of children and youth, leading many to seek nutrition education through digital platforms. This study aims to assess the usability and acceptability of Ajyal Salima, a nutrition awareness digital platform targeting children aged 9–11, in four Arab countries. Methods A qualitative study was led across four countries: Lebanon, Bahrain, Palestine, and Jordan. Semi-structured focus groups discussions (FGDs) were held separately with children (21 FGDs; n = 145) and parents (16 FGDs; n = 98), complemented by In Depth Interview (IDIs) with teachers (n = 19) and Key Informant Interviews (KIIs) with program staff (n = 8). All interviews and focus groups lasted approximately 40 minutes. Data was analyzed thematically using NVivo software, resulting in four main themes. Results Four major themes emerged: platform’s usability, content enjoyment, changes in children’s habits and recommendations to improve the platform. Overall, parents and teachers found the digital experience positive and useful and the content appropriate for children, particularly younger age groups. Challenges included registration difficulties, technical problems, internet accessibility, low parental involvement, and difficulties integrating the platform into teachers’ schedules. The platform’s animations were less effective in sustaining children’s attention amid evolving digital standards. Conclusion To enhance the platform’s effectiveness, recommendations include simplifying the registration process, enhancing content interactivity, aligning the platform with school curricula, and equipping teachers with supportive resources. Fostering stronger school-family partnerships and engaging parents through community initiatives may be considered to maximize the platform’s potential to promote healthier eating habits and improve nutritional awareness among children and their families, across the region.

A social network analysis of fraud prediction on crowdsourcing platforms

In the context of crowdsourcing contests, where winners take all, attracting high-quality solvers and solutions presents a significant challenge. A key issue in this environment is protecting solvers’ intellectual property and preventing fraud risks such as solution plagiarism and theft. Addressing these challenges is essential for maintaining the integrity of the platform and encouraging innovation. This study applies social network analysis to examine the structural characteristics of fraudulent seekers and investigate whether they exhibit distinct social network features compared to legitimate users. Specifically, we focus on centrality, cohesion, and structural equivalence to identify potential markers of fraudulent intent. Using a dataset from 9,282 contest projects initiated in China in 2014, involving 6,241 active users and 246 fraudulent seekers, we tested a fraud detection model based on social network metrics. The results reveal significant differences in degree centrality, betweenness centrality, closeness centrality, and clustering coefficients between fraudulent and non-fraudulent nodes. The findings demonstrate that social network features, particularly centrality measures, can effectively differentiate fraudulent seekers from legitimate users. This study contributes to the theoretical understanding of fraud detection in crowdsourcing and offers practical insights for the development of more robust fraud detection strategies.