Browse Articles

Beyond the Vibrational Stark Effect: Unraveling the Large Redshifts of Alkyne C–H Bond in Solvation Environments

Author Correction: Optimization of process parameters of catalytic pyrolysis using natural zeolite and synthetic zeolites on yield of plastic oil through response surface methodology

Bio-orthogonal Labeling of Chitin in Native Pathogenic Candida Species via the Chitin Scavenge Pathway

Publisher Correction: Repurposing raltegravir for reducing inflammation and treating cancer: a bioinformatics analysis

Three-Dimensional Covalent Organic Frameworks with lil Topology

Information seeking and the expected utility of information about COVID-19 can be associated with uncertainty and related attitudes

Self-Assembled Monolayers of Triazolylidenes on Gold and Mixed Gold/Dielectric Substrates

Development of a new hybrid model to enhance streamflow estimation using artificial neural network and reptile search algorithm

Abstract A new metaheuristic optimizer combined with artificial neural networks is proposed for streamflow prediction. Hence, the study aimed to forecast monthly streamflow of the main rivers in Urmia, Iran, by considering data shortage and using artificial neural network (ANN) models. By combining three variables: temperature, precipitation, and streamflow, we formulated five patterns, where 70% of the data were used for model training, and 30% for model testing. To improve the performance of ANN, we evaluated a new optimization algorithm, reptile search algorithm (RSA), and compared the results with combinations of ANN, particle swarm optimization algorithm (PSO), and whale optimization algorithm (WOA) models. The results of the ANN + RSA were promising at most stations and patterns. At Band station streamflow simulation testing gave RMSE, MAE, and NSE of 1.65, 1.21 MCM/month, and 0.80, respectively. At Babaroud station they were 4.01, 3.0 MCM/month and 0.68, respectively, at Nazlo station 5.62, 3.79 MCM/month, and 0.69, respectively, and at Tapik station 5.69, 3.82 MCM/month, and 0.59, respectively. However, the results of the ANN + PSO hybrid model were better than ANN + RSA. The impact of different parameters on the accuracy of streamflow prediction varied depending on model and streamflow station, indicating that the models do not perform consistently across different locations, times, and conditions. The inclusion of lagged monthly streamflow in the model was an influential input parameter. The results demonstrated that the new algorithm consistently improved predictions, enhancing the performance of traditional algorithms. The findings of this study highlight advantage of the ANN + RSA hybrid model for specific areas, suggesting its potential application in other similar hydrological problems for further validation.

A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes

Establishment of an experimental model of canine apocrine gland anal sac adenocarcinoma organoid culture using a three-dimensional culture method

Fluorination: Simple Change but Complex Impact on Ferroelectric Nematic and Smectic Liquid Crystal Phases

Semantic segmentation feature fusion network based on transformer

Reversible Angle Distortion-Dependent Electrochemical CO₂ Reduction on Cobalt Phthalocyanine

Prediction criterion of the interaction between hydraulic fractures and bedding planes via theoretical and simulation approaches

Measuring the evolution of entanglement in Compton scattering

Cycles

Activating the Basal Plane of 2D Transition Metal Dichalcogenides via High-Entropy Alloying

Association of arachnoid fossae and endocranial lesions in a historical population from Poland: new diagnostic possibilities

Abstract This study examines the arachnoid fossae (AF) in crania from early modern Wrocław, Poland, and their connection to endocranial lesions, specifically abnormal blood vessel impressions (ABVI) and periosteal appositions of the dura mater (PADM). AF are fovea on the inner surface of the cranial bone, which arise as the result of arachnoid granulations’ (AG) protruding and causing the resorption of the bone. ABVI and PADM are non-specific pathological lesions caused by inflammation and hemorrhages in the meninges. The goal of the study is to determine if the size of AF is associated with the occurrence of endocranial lesions (ABVI and PADM), whether they can aid in diagnosing pathological conditions in historical populations, and to propose a measurement methodology. Using an endoscopic device, the study analyzed 80 adult crania from a collection in Wrocław, Poland. The size of AF was measured using scaled photographs, and the occurrence and severity of ABVI and PADM were also documented. The study then investigated the relationship between the size of AF and the presence of ABVI and PADM. It was determined that the individuals with ABVI had greater AF than those without the lesions. The results indicate that while the presence of AF can be considered a normal cranial variation, their size may be useful in diagnosing pathological conditions in the meninges, such as trauma, tumors, or infectious diseases.

Unravelling Size-Dependent Upconversion Luminescence in Ytterbium and Erbium Codoped NaYF₄ Nanocrystals

High risk of political bias in black box emotion inference models

Abstract This paper investigates the presence of political bias in emotion inference models used for sentiment analysis (SA). Machine learning models often reflect biases in their training data, impacting the validity of their outcomes. While previous research has highlighted gender and race biases, our study focuses on political bias—an underexplored, pervasive issue that can skew the interpretation of text data across many studies. We audit a Polish sentiment analysis model developed in our lab for bias. By analyzing valence predictions for names and sentences involving Polish politicians, we uncovered systematic differences influenced by political affiliations. Our findings suggest that annotations by human raters propagate political biases into the model’s predictions. To prove it, we pruned the training dataset of texts mentioning these politicians and observed a reduction in bias, though not its complete elimination. Given the significant implications of political bias in SA, our study emphasizes caution in employing these models for social science research. We recommend a critical examination of SA results and propose using lexicon-based systems as an ideologically neutral alternative. This paper underscores the necessity for ongoing scrutiny and methodological adjustments to ensure the reliability of the use of machine learning in academic and applied contexts.