Search Results (726)

An individual’s ability to process flickering light is expressed by critical flicker fusion frequency (CFFF), tested with the flicker test. CFFF is used to assess visual processing, arousal, and cognitive functioning, among other things, although it is unclear how it reflects these processes. Due to possible differences between CFFF values obtained in trials with increasing and decreasing frequency, it also remains questionable to use only averaged CFFF values in research. The main objective of the present study was to assess how CFFF is related to cognitive functions (attention, short-term and working memory, and executive functions), and psychomotor speed. The research objectives also included assessing the stability of CFFF and its variability with age and comparing CFFF between men and women. Thirty-six participants (17 women and 19 men) completed computerized cognitive tests (Simon and flanker tasks, the Corsi block-tapping task, and the digit span task) three times, along with the flicker test. We found that CFFF scores were stable across sessions but differed between fusion and flicker thresholds, with age significantly correlating only with the fusion frequency. Given that, we suggest that future studies analyze not only the averaged CFFF, but also examine flicker and fusion thresholds separately to better understand their distinct contributions. Our results also revealed generally weak correlations between CFFF and neuropsychological test scores, with significant associations found only in women, suggesting that CFFF may not be a reliable indicator of cognitive functioning. Full article

This study reports on the preparation and comprehensive characterisation of new brominated hydrogen-bonded liquid crystalline (HBLC) materials. Two distinct series of supramolecular complexes were prepared by hydrogen-bond formation between 3-bromo-4-pentyloxybenzoic acid as the proton donor and non-fluorinated and fluorinated azopyridines with variable terminal chains as proton acceptors. The successful formation of a hydrogen bond was confirmed by FTIR spectroscopy. The impact of alkyl chain length and fluorination on the mesomorphic properties of the HBLCs was systematically investigated. The molecular self-assembly was thoroughly examined using polarised optical microscopy (POM) and differential scanning calorimetry (DSC), revealing the presence of smectic C (SmC), smectic A (SmA), and nematic (N) phases, with thermal stability being highly dependent on the molecular architecture. Notably, the introduction of fluorine atoms significantly influenced the phase transition temperatures and the overall mesophase range. Using bromine as a lateral substituent induces the formation of SmC phases in these HBLCs, a feature absent in their non-brominated analogues. Further structural insights were obtained through X-ray diffraction (XRD) investigations, confirming the nature of the observed LC phases. Additionally, the photo-responsive characteristics of these HBLCs were explored via UV-Vis spectroscopy, demonstrating their ability to undergo reversible photoisomerisation upon light irradiation. These findings underscore the critical role of precise molecular design in tailoring the properties of HBLCs for potential applications such as optical storage devices. Full article

Among the main man-made water pollutants that pose a danger to the environment are oil products, heavy metals, and radionuclides, as well as micro- and nanoplastics. To purify such waters, it is necessary to use advanced methods, with sorption being one of them. The aim of this work is to develop a nano-functionalized composite, comprising magnetically responsive, thermally expanded graphite (TEG) and the natural clay bentonite, and to assess its ability to purify man-made contaminated waters. Throughout the course of the research, the methods of scanning electron microscopy, optical microscopy, dynamic light scattering, radiometry, and atomic absorption spectrophotometry were used. The use of the TEG–bentonite composite for the purification of the model water, simulating radioactively contaminated nuclear power plant (NPP) effluent, reduced the content of organic substances by 10–15 times, and the degree of extraction of cesium, strontium, cobalt, and manganese was between 81.4% and 98.8%. The use of the TEG–bentonite composite for the purification of real radioactively contaminated water obtained from the object “Shelter” (“Ukryttya” in Ukrainian), in the Chernobyl Exclusion Zone, Ukraine, with high activity, containing organic substances, including micro- and nanoplastics, reduced the radioactivity by three orders of magnitude. The use of cesium-selective sorbents for additional purification of the filtrate allowed for further decontamination of radioactively contaminated water with an efficiency of 99.99%. Full article

Polymeric optical waveguides represent an essential component in photonic technology thanks to their ability to guide light through controlled structures, enabling applications in telecommunications, sensors, and integrated devices. With the development of new materials and increasingly versatile manufacturing methods, these structures are being integrated into various systems at a rapid pace, while their dimensions are constantly being reduced. This article explores the main fabrication methods for polymeric optical waveguides, such as traditional and maskless photolithography, laser ablation, hot embossing, nanoimprint lithography, the Mosquito method, inkjet printing, aerosol jet printing, and electrohydrodynamic (EHD) printing. The operating principle of each method, the equipment and materials used, and their advantages, limitations, and practical applications are evaluated, in addition to the propagation losses and characterization of the waveguides obtained with each method. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

Infrared thermography, with its ability to obtain information remotely and without contact, is often applied as a valuable method for investigating the surface layers of historical buildings. In this paper, the results of applying infrared thermography during academic investigations of two Italian treasures—the Sanctuary of Vicoforte in Piedmont and the airship hangar in Augusta, Sicily—are presented. At the Sanctuary of Vicoforte, imaging was performed to evaluate the condition of the exterior finishes and interior frescoes, while at the airship hangar, imaging was conducted to detect cracks that are difficult to observe using visible-light imaging. Full article

Viticulture, a key economic activity in the Mediterranean area, is facing several challenges including soil degradation. Among the sustainable practices available, the management of cover crops in vineyard inter-rows using a roller crimper to create dead mulch is gaining pace as an effective strategy for soil conservation. Nevertheless, the effectiveness of roller crimpers in terminating groundcovers in vineyards may be reduced by pedoclimatic conditions, type of vegetation and roller crimper configuration and operational parameters. This study aimed to evaluate the effectiveness of a roller crimper with two different weight configurations, light (LR) and ballasted (HR), each tested with one (P1) or two passes (P2), in terminating a cover crop mixture in a vineyard. To evaluate the termination performance, plant green cover data were modeled using a one phase exponential decay nonlinear regression. The four systems were also assessed for their ability to conserve soil moisture and their impact on soil compaction. Although the HR + P2 showed the highest termination performance, the system using the HR + P1 obtained comparable results, with k values of 0.07 and 0.11 days⁻¹ and half-life values of 9.50 and 6.09 days in 2023 and 2024, respectively. Given the need to coordinate multiple vineyard operations within short and weather-dependent timeframes, a one-pass approach such as HR + P1 offers operational advantages, providing a practical compromise between efficacy and efficiency. Full article

In 2023, 95.5 million Europeans were aged over 65, falling within the definition of the “elderly population”. According to statistics, this number will rise to 129.8 million by 2050, making Europe the oldest continent in the world. One of the consequences of such growth is a sharp increase in the number of elderly drivers. Although they have more experience, which can positively impact road safety, their performance and health generally decline, limiting some of the physical and mental abilities required for safe vehicle control. The main objective of this research was to shed light on the behavior of elderly drivers by comparing three different drivers’ age groups: young, middle-aged and elderly drivers. Based on analysis of road accidents involving elderly drivers, the road safety situation for elderly drivers in Slovenia was highlighted, a questionnaire was developed to understand how elderly drivers perceive traffic, and an experiment was conducted where 30 volunteers were tested using a driving simulator and eye-tracking glasses. Objective driving and gaze behavior data were obtained, and very different performance was found among the three age groups, with elderly drivers having poorer reaction times and overlooking many elements compared to younger drivers. Full article

Virtual reality (VR) is widely used across various areas of human life. One field where its application is rapidly growing is sport and physical activity (PA). Training applications are being developed that support various sports disciplines, motor skill acquisition, and the development of motor abilities. Immersive technologies are increasingly being used to assess motor and cognitive capabilities. As such, validation studies of these diagnostic tools are essential. The aim of this study was to estimate the validity and reliability of reaction speed (RS) measurements using the Rezzil Player application (“Reaction” module) in immersive VR compared to results obtained with the SMARTFit device in a real environment (RE). The study involved 43 university students (17 women and 26 men). Both tests required participants to strike light targets on a panel with their hands. Two indicators of response were analyzed in both tests: the number of hits on illuminated targets within a specified time frame and the average RS in response to visual stimuli. Statistically significant and relatively strong correlations were observed between the two measurement methods: number of hits (r_S = 0.610; p < 0.001) and average RS (r_S = 0.535; p < 0.001). High intraclass correlation coefficients (ICCs) were also found for both test environments: number of hits in VR (ICC = 0.851), average RS in VR (0.844), number of hits in RE (ICC = 0.881), and average RS in RE (0.878). The findings indicate that the Rezzil Player application can be considered a valid and reliable tool for measuring reaction speed in VR. The correlation with conventional methods and the high ICC values attest to the psychometric quality of the tool. Full article

Efficient water decolorization techniques are vital for ensuring fresh water for future generations. Azo dyes are used heavily in the textile industry and are a challenge to remove from industrial wastewater. This research expands on recent innovative work where anionic azo dyes themselves were used to functionalize track-etched porous polycarbonate filtration membranes with decolorized water obtained as a byproduct. The objective of this research is to determine whether the observed dye rejection is dependent on the magnitude of the intrinsic charge of the dye molecule or on its structure, using two selectively chosen anionic azo dye series during functionalization. The first group is a negative two intrinsic charge series with six dyes, each differing in structure, and the second group is a five-dye series that increases from −1 to −6 in intrinsic charge. Rejection measurements as a function of both time and concentration during functionalization are made using ultraviolet-visible light spectroscopy. For 100 µM aqueous dyes, comparing pre- and post-functionalization, a systematically increasing trend in the ability to functionalize porous polycarbonate based on the number of double 6-carbon ring structures in the dyes is illustrated and found to be independent of intrinsic charge. Full article

Using agro-industrial byproducts as functional ingredients represents a sustainable approach to food development. This study aimed to characterize the physicochemical and techno-functional properties of grape marc flour and evaluate the metabolite content and antioxidant activity of the extract obtained from these residues. Grape marc flour analysis included pH, color, and techno-functional parameter assessment. The metabolite content and antioxidant activity of the extracts were determined in vitro and in a meat system. The grape marc flour exhibited low pH, lightness (L*), and yellowness (b*) index values, as well as increased redness (a*) values. It also showed the ability to retain water and oil, along with notable swelling capacity. The extracts exhibited high levels of phenolic, tannins, flavonoids, and chlorogenic acid, as well as anti-radical activity and reducing power. When incorporated into a cooked meat system, the extracts decreased pH and lipid oxidation levels. These findings suggest that grape marc flour has potential as a functional ingredient in the formulation of meat products. Full article

Underwater image enhancement (UIE) is a key technology in the fields of underwater robot navigation, marine resources development, and ecological environment monitoring. Due to the absorption and scattering of different wavelengths of light in water, the quality of the original underwater images usually deteriorates. In recent years, UIE methods based on deep neural networks have made significant progress, but there still exist some problems, such as insufficient local detail recovery and difficulty in effectively capturing multi-scale contextual information. To solve the above problems, a Multi-Scale Contextual Fusion Residual Network (MCFR-Net) for underwater image enhancement is proposed in this paper. Firstly, we propose an Adaptive Feature Aggregation Enhancement (AFAE) module, which adaptively strengthens the key regions in the input images and improves the feature expression ability by fusing multi-scale convolutional features and a self-attention mechanism. Secondly, we design a Residual Dual Attention Module (RDAM), which captures and strengthens features in key regions through twice self-attention calculation and residual connection, while effectively retaining the original information. Thirdly, a Multi-Scale Feature Fusion Decoding (MFFD) module is designed to obtain rich contexts at multiple scales, improving the model’s understanding of details and global features. We conducted extensive experiments on four datasets, and the results show that MCFR-Net effectively improves the visual quality of underwater images and outperforms many existing methods in both full-reference and no-reference metrics. Compared with the existing methods, the proposed MCFR-Net can not only capture the local details and global contexts more comprehensively, but also show obvious advantages in visual quality and generalization performance. It provides a new technical route and benchmark for subsequent research in the field of underwater vision processing, which has important academic and application values. Full article

This study analyzed the heavy metal tolerance and chromium reduction and the potential of plant growth to promote Rhizobium sp. OS-1. By genetic makeup, the Rhizobium strain is nitrogen-fixing and phosphate-solubilizing in metal-contaminated agricultural soil. Among the Rhizobium group, bacterial strain OS-1 showed a significant tolerance to heavy metals, particularly chromium (900 µg/mL), zinc (700 µg/mL), and copper. In the initial investigation, the bacteria strains were morphologically short-rod, Gram-negative, appeared as light pink colonies on media plates, and were biochemically positive for catalase reaction and the ability to ferment glucose, sucrose, and mannitol. Further, bacterial genomic DNA was isolated and amplified with the 16SrRNA gene and sequencing; the obtained 16S rRNA sequence achieved accession no. HE663761.1 from the NCBI GenBank, and it was confirmed that the strain belongs to the Rhizobium genus by phylogenetic analysis. The strain’s performance was best for high hexavalent chromium [Cr(VI)] reduction at 7–8 pH and a temperature of 30 °C, resulting in a total decrease in 96 h. Additionally, the adsorption isotherm Freundlich and Langmuir models fit best for this study, revealing a large biosorption capacity, with Cr(VI) having the highest affinity. Further bacterial chromium reduction was confirmed by an enzymatic test of nitro reductase and chromate reductase activity in bacterial extract. Further, from the metal biosorption study, an Artificial Neural Network (ANN) model was built to assess the metal reduction capability, considering the variables of pH, temperature, incubation duration, and initial metal concentration. The model attained an excellent expected accuracy (R² > 0.90). With these features, this bacterial strain is excellent for bioremediation and use for industrial purposes and agricultural sustainability in metal-contaminated agricultural fields. Full article

Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article

To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36–56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions. Full article