16168 research outputs found
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Effect of enzyme on chromed leather dyeing with acidic dyes /
No full textLeather dyeing is a difficult task due to its heterogeneity and complexity. Several studies are aimed at improving the effectiveness of dyeing, including process optimization, use of dye auxiliaries, ultrasound, liposomes, and enzymes. Enzymes are receiving more attention due to their selectivity and high activity under optimum conditions. They are already applied in pretanning; however, information about enzyme application in the dyeing process is very limited. The study is aimed at investigating dyeing using enzyme preparation in the process together with a dye or pretreating leather with enzyme preparation prior to dyeing and comparing it with conventional leather dyeing. The results of the leather dyeing showed better adsorption in a control compared to the experiments, and it was also observed that the process is temperature dependent. Dyeing experimental samples had different kinetics. Using low dye concentration, enzyme preparations together with dye at 40°C resulted in the lowest adsorption; however, with higher dye concentrations, the tendencies were different. Fiber dyeing showed that pore removal has influence on enzymatic dyeing processes; dyeing together with EP resulted in better dye adsorption compared to EP pretreatment. To assess adsorption isotherms, three equations were applied: Langmuir, Freundlich, and Redlich–Peterson. The correlation coefficient showed that a suitable model depended on the sample and dyeing conditions. In some cases, the more suitable isotherm was Langmuir; in the other, it was Redlich–Peterson. The Langmuir adsorption capacity QL depended on the sample, the control had the highest values, and it increased with increasing temperature. Freundlich’s constant n indicated that all three processes are favorable. Although control dyeing had a higher dye exhaustion, better diffusion was observed with enzyme preparation. EP dyeing not only led to better dye diffusion into leather but also resulted in a darker shade compared to pretreated samples. Research results give new insight into the mechanism of the enzymatic dyeing process and its effect on dye penetration and color index values between samples
Navigating the CISO’s mind by integrating GenAI for strategic cyber resilience /
No full textAbstract: AI-driven cyber threats are evolving faster than current defense mechanisms, complicating forensic investigations. As attacks grow more sophisticated, forensic methods struggle to analyze vast wearable device data, highlighting the need for an advanced frame- work to improve threat detection and responses. This paper presents a generative artificial intelligence (GenAI)-assisted framework that enhances cyberforensics and strengthens strategic cyber resilience, particularly for chief information security officers (CISOs). It addresses three key challenges: inefficient incident reconstruction, open-source intelligence (OSINT) limitations, and real-time decision-making difficulties. The framework integrates GenAI to automate routine tasks, the cross-layering of digital attributes from wearable devices and open-source intelligence (OSINT) to provide a comprehensive understanding of malicious incidents. By synthesizing digital attributes and applying the 5W approach, the framework facilitates accurate incident reconstruction, enabling CISOs to respond to threats with improved precision. The proposed framework is validated through ex- perimental testing involving publicly available wearable device datasets (e.g., GPS data, pairing and activity logs). The results show that GenAI enhances incident detection and reconstruction, increasing the accuracy and speed of CISOs’ responses to threats. The ex- perimental evaluation demonstrates that our framework improves cyberforensics efficiency by streamlining the integration of digital attributes, reducing the incident reconstruction time and enhancing decision-making precision. The framework enhances cybersecurity resilience in critical infrastructures, although challenges remain regarding data privacy, accuracy and scalability
A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images /
No full textDeep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imbalance, and computational inefficiencies due to numerous hyperparameters persist. This study aims to address these challenges by presenting a novel deep-learning framework for classifying and localizing gastrointestinal (GI) diseases from wireless capsule endoscopy (WCE) images. The proposed framework begins with dataset augmentation to enhance training robustness. Two novel architectures, Sparse Convolutional DenseNet201 with Self-Attention (SC-DSAN) and CNN-GRU, are fused at the network level using a depth concatenation layer, avoiding the computational costs of feature-level fusion. Bayesian Optimization (BO) is employed for dynamic hyperparameter tuning, and an Entropy-controlled Marine Predators Algorithm (EMPA) selects optimal features. These features are classified using a Shallow Wide Neural Network (SWNN) and traditional classifiers. Experimental evaluations on the Kvasir-V1 and Kvasir-V2 datasets demonstrate superior performance, achieving accuracies of 99.60% and 95.10%, respectively. The proposed framework offers improved accuracy, precision, and computational efficiency compared to state-of-the-art models. The proposed framework addresses key challenges in GI disease diagnosis, demonstrating its potential for accurate and efficient clinical applications. Future work will explore its adaptability to additional datasets and optimize its computational complexity for broader deployment
The paradox of being: correlations of music and happiness.
No full textThe article presents an analysis of happiness and music experiences using the categories of fullness, constancy and totality or intensity, duration and extensity. The article poses questions regarding the existence of music and a happy life, considers the parameters of a music work, and seeks reliable sources and factors of happiness. It is evident that attempts to discover and express the essence of music and find a universal basis for happiness are prone to getting stuck in labyrinths of technical details or ending up in metaphors. The fundamental expression of the paradox of being is the dichotomy of inside and outside, manifested in reflecting experiences of music and happiness.The discovered antinomies and aporias confirm and highlight the paradox of being – being is the closest to a human, but its proximity remains the most distant
Asmeninių paroksizminio prieširdžių virpėjimo trigerių identifikavimo metodas.
No full textAtrial fibrillation (AF) is the most common cardiac arrhythmia, affecting over 50 million people worldwide, with its true prevalence likely being higher due to asymptomatic cases. AF places a significant burden on healthcare systems due to complications such as stroke and heart failure. Early detection is crucial but remains challenging, and current treatments primarily rely on anticoagulants and antiarrhythmic drugs, which are associated with significant side effects. Recent research highlights the role of modifiable AF triggers—acute exposures that contribute to the short-term occurrence of AF episodes (e.g., alcohol, physical exertion, stress). Identifying and managing triggers, such as alcohol consumption, physical exertion, and psychological stress, can empower patients to modify their lifestyles and align with personalized AF management strategies. This doctoral thesis addresses the clinically relevant scientific-technological challenges of detecting suspected AF triggers in physiological signals and identifying their relation to AF episode occurrence on an individual level. For trigger detection, ECG and acceleration signals have been used to compute time-varying parameters, with distinct thresholds for specific trigger identification. A quantitative approach has been proposed to assess the relational strength between suspected AF triggers and AF episode occurrence, relying on the pre- and post-trigger AF burden, defined as the percentage of time spent in AF during the monitored period. Additionally, a model for simulating trigger-affected AF episode occurrence has been developed to evaluate the proposed relation assessment methods
Composition and technological properties of modified lingonberry (Vaccinium vitis-idaea L.) pomace /
No full textLingonberry pomace (LP) is a by-product rich in valuable bioactive compounds and can be used in the food industry after various treatments and property characterization. This study aimed to evaluate the impact of commercially available enzymes (Viscozyme® L, Pectinex® Ultra Tropical, and Celluclast® 1.5 L) and supercritical carbon dioxide (SFE-CO2) extraction technology on the chemical composition and technological properties of treated LP products. The Megazyme kit was used to determine the soluble dietary fiber (SDS) and insoluble dietary fiber (IDF) contents, while the changes in mono-, disaccharide, and oligosaccharides were analyzed by applying high-pressure liquid chromatography with a refractive index detector. The analyzed properties were as follows: the water swelling capacity (WSC), water retention capacity (WRC), water solubility index (WSI), oil retention capacity (ORC), bulk density (BD), and emulsion stability of modified LP. The tested LP contained 8.49 g/100 g of SDF and 65.36 g/100 g of IDF (in dry matter). The partial separation of lipophilic substances during SFE-CO2 extraction did not significantly affect the enzymatic hydrolysis efficiency. The amount of oligosaccharides in the LP increased using enzymes with pectinolytic activity (Viscozyme® L and Pectinex® Ultra Tropical), while cellulolytic enzymes (Celluclast® 1.5 L) increased the amount of SDF and improved the IDF/SDF ratio. Enzymatic hydrolysis increased the SI, WRC, and ORC of LP powder. Emulsions with LP hydrolyzed with Pectinex® Ultra Tropical demonstrated the highest stability during storage. This study demonstrates that the modification of LP powders provides diverse technological properties, which could expand the application of such products for further food production
Green lentil fortification of wheat bread: a strategy for quality improvement and acrylamide reduction /
No full textThis study aimed to assess how non-treated (N), milled (M), and with Lactiplantibacillus plantarum fermented (F) green lentils affect the quality and safety parameters, including volatile compound (VC) profile and acrylamide (AA) concentration, of wheat bread (WB). The overall acceptability (OA) of WB with 5, 10, 15, and 20% lentils, as well as with 25% the non-M F and N non-F lentils was similar to that of the control. The addition of M lentils resulted in a higher increase in AA concentration in WB, compared to those prepared with non-M lentils. Lentil quantity and type added significantly influenced most of the VC formation in bread. Correlations between AA content in WB and separate VC were found. Finally, it can be suggested to supplement the bread with 5, 10, or 15% fermented non-milled green lentils to provide the safest variant with a low AA level as well as favorable OA
Image processing algorithms analysis for roadside wild animal detection /
No full textThe study presents a comparative analysis of five distinct image processing methodologies for roadside wild animal detection using thermal imagery, aiming to identify an optimal approach for embedded system implementation to mitigate wildlife–vehicle collisions. The evaluated techniques included the following: bilateral filtering followed by thresholding and SIFT feature matching; Gaussian filtering combined with Canny edge detection and contour analysis; color quantization via the nearest average algorithm followed by contour identification; motion detection based on absolute inter-frame differencing, object dilation, thresholding, and contour comparison; and animal detection based on a YOLOv8n neural network. These algorithms were applied to sequential thermal images captured by a custom roadside surveillance system incorporating a thermal camera and a Raspberry Pi processing unit. Performance evaluation utilized a dataset of consecutive frames, assessing average execution time, sensitivity, specificity, and accuracy. The results revealed performance trade-offs: the motion detection method achieved the highest sensitivity (92.31%) and overall accuracy (87.50%), critical for minimizing missed detections, despite exhibiting the near lowest specificity (66.67%) and a moderate execution time (0.126 s) compared to the fastest bilateral filter approach (0.093 s) and the high-specificity Canny edge method (90.00%). Consequently, considering the paramount importance of detection reliability (sensitivity and accuracy) in this application, the motion-based methodology was selected for further development and implementation within the target embedded system framework. Subsequent testing on diverse datasets validated its general robustness while highlighting potential performance variations depending on dataset characteristics, particularly the duration of animal presence within the monitored frame
Learner satisfaction, academic performance, micro-credential design: a case study from European universities /
No full textRecently, asynchronous distance learning has been gaining research attention at an accelerating pace, enabling digital transformation in education and helping learners acquire the necessary skills. Hence, micro-credentials have also raised significant scholarly interest. One of the main challenges in distance online learning is to keep the learners engaged throughout and satisfied with their learning journeys. This is especially true in micro-credentials, which, more often than not, come on top of regular studies and professional and social commitments of the learners. In general, a substantial body of research exists on student satisfaction with online distance learning and micro-credentials specifically. Researchers investigate the effectiveness of specific activities, create and compare varied course designs, and analyse factors affecting student satisfaction with online learning. However, the existing research is fragmented due to the different spectrums of learning activities analysed in different educational contexts. In addition, the research on innovative learning designs of micro-credentials is limited. To address these gaps, we present a case study of a micro-credential “AI for Business” which is proposed to students from 14 European universities collaborating under the European Consortium of Innovative Universities umbrella. The body of the micro-credential includes 51 learning activities, including hands-on small projects invoking artificial intelligence and process automation tools, discussions, interactive e-texts, memory cards, games with sound and motion, interactive videos, quizzes, peer assessment, experiential learning, challenge-based learning with challenges from real businesses – our social partners and, at the same time, leaders in their markets, and much more. Such a great variety of learning activities within the same micro-credential allows for comparing the learning activities by student satisfaction while ensuring equal environmental conditions and eliminating potential inaccuracies which otherwise could have arisen from different courses – that is, contexts – in which the activities are employed. In addition, we present the micro-credential design and explore whether the learner satisfaction rates correlate with the grades received. Therefore, this case study enriches the research on innovative learning designs for micro-credentials and learner satisfaction with online learning activities, both subjects which scholars have highlighted as needing deeper academic investigation