16168 research outputs found
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Comparative analysis of flow behavior and geochemical impact of CO2 and hydrogen in Lithuanian saline aquifer: a simulation and experimental study /
No full textLithuania covers the deepest parts of the Baltic basin and contains many reservoirs that have been explored for Hydrocarbon production and gas storage projects, including CO2 and hydrocarbon gas storage. Studies have also been conducted to assess the storage potential of these reservoirs for gases like CO2 and Hydrogen. In the studies, four saline aquifers, including Syderiai, Vaskai, and D11, and depleted hydrocarbon reservoirs in the Gargzdai structure were evaluated for potential CO2 storage. However, the long-term fate of these gases’ migration at the field scale has not been reported previously. In response to the existing gap, this study aims to evaluate the risks and challenges associated with subsurface CO2 and Hydrogen storage by conducting numerical simulations at two injection rates, of fluid migration, pH variations, and geomechanical responses using the tNavigator platform, complemented by laboratory experiments on outcrops representative of Syderiai formation, to achieve a detailed understanding of geochemical interactions between rocks and fluids. The results reveal distinct gas-specific behaviors: CO2 exhibits enhanced solubility trapping, density-driven convective mixing, and pronounced pH reduction, whereas Hydrogen demonstrates rapid buoyant migration, higher pressure buildup, and negligible geochemical reactivity. Both gases demonstrate short-term storage viability in the Syderiai aquifer under the modeled conditions, with pressure and total vertical stress remaining below the bottom-hole pressure limit of 450 bars. This integrated simulation and experimental study enhances our understanding of Lithuanian reservoirs for the safe, long-term storage of both CO2 and Hydrogen
Measurement of charge carrier mobilities in thin films via the space-charge limited current (SCLC) method; a practical example /
No full textStudy of space charge limited current (SCLC) transport in charge carrier injection is presented. It is shown that the accurate and convenient calculation of carrier mobility, which has been neglected in many previous studies on transport in optical and electrical devices, is essential to obtain physically meaningful spatial carrier densities and field distributions. In this work, the SCLC technique to accurately determine the mobility of holes and electrons in organic semiconductors is investigated in detail. Recognizing the importance of balanced charge transport to the performance of optical and electronic devices, the fundamentals of SCLC, including Mott-Gurney's law, are discussed and its advantages over alternative methods are highlighted. A carbazole-based compound is used as a practical example, with single-carrier devices fabricated to selectively measure hole-only and electron-only transport. The current-voltage characteristics were analysed in the trap-free SCLC regime (slope ≈ 2 in log-log plots), and yielded mobilities of μe =4.02 × 10−5 cm2V-1s-1 and μh = 1.84 × 10–3 cm2V-1s-1. This study not only demonstrates a clear and reproducible method for mobility extraction, but also highlights the importance of SCLC measurements under device-like conditions for material selection and performance optimization in optoelectronic applications
Evaluation of a company’s media reputation based on the articles published on news portals /
No full textA company’s reputation is an important, intangible asset, which is heavily influenced by media reputation. We developed a method to measure a company’s reputation based on sentiments detected in online articles. The sentiment of each sentence was evaluated and categorized into one of three polarities: positive, negative, or neutral. Then, we developed another method to assess a company’s media reputation using all available online articles about the company. The company’s media reputation is presented as a tuple consisting of their media reputation on a scale from 0 to 100, the number of articles related to the company, and the margin of error. Experiments were conducted using articles written in Lithuanian published on major news portals. We used two different tools to assess the sentiments of the articles: Stanford CoreNLP v.4.5.10, combined with Google API, and the pre-trained transformer model XLM-RoBERTa. Google API was used for translation into English, as Stanford CoreNLP does not support the Lithuanian language. The results obtained were compared with those of existing methods, based on the coefficients of media endorsement and media favorableness, showing that the results of the proposed method are less moderate than the coefficient of media favorableness and less extreme than the coefficient of media endorsement
Delirium after coronary artery bypass grafting with cardiopulmonary bypass surgery: the value of cerebral autoregulation /
No full textIntroduction: Postoperative delirium affects up to 60% of cardiac surgical patients. No reliable gold standard method exists for preventing delirium after cardiac surgery. An example of patient-personal monitoring is cerebral autoregulation (CA). This study aims to highlight the personal monitoring of patients’ cerebral autoregulation and to determine its relationship with postoperative delirium. Additionally, it seeks to test the hypothesis that the duration of CA impairment influences the onset of postoperative delirium. Methods: A prospective study was conducted in 2021–2023. After approval of the Ethics Committee and with the patient’s written consent, 104 adult patients undergoing elective coronary artery bypass graft (CABG) with cardiopulmonary bypass (CPB) surgery were enrolled. To diagnose possible delirium, all patients underwent a Confusion Assessment Method for the Intensive Care Unit (CAM–ICU) test. CA monitoring using transcranial Doppler was performed. CA status index – Mx was recorded. Results: Our study found that 12.5% of patients were diagnosed with delirium after on-pump CABG surgery. The total duration of cerebral autoregulation impairment (TCAI) was longer in the delirium group, 4783.0 seconds versus 4204.5 seconds (p = .047), with a cut-off at 4380 s. Longer cardiopulmonary bypass (CPB) leads to prolonged CA impairment (p < .001). The mean arterial pressure (MAP) during CPB was 69.67 mmHg in the non-delirium group and 74.91 mmHg in the delirium group (p = .001), with a cutoff at 73.669 mmHg. Conclusions: CA impairment is crucial for delirium development after cardiac surgery. The duration of the TCAI event increases the risk of delirium
ECG-based detection of acute myocardial infarction using a wrist-worn device /
No full textBackground: A wrist-worn wearable device for acquiring limb and chest ECG leads (wECG) may constitute a promising approach to detection of acute myocardial infarction (AMI). However, it remains to be demonstrated whether the information conveyed by the wECG is sufficient for AMI detection. Objective: To explore explainable machine learning models for detecting AMI using the wECG. Methods: Two types of machine learning models are explored: a convolutional neural network (CNN) using the raw ECG as input and a gradient-boosting decision tree (GBDT) using clinically informative features. 123 participants were included, divided into patients with AMI, patients with other cardiovascular diseases, and healthy individuals. A wristworn device equipped with three biopotential electrodes was used to acquire two ECG leads with a single touch: limb lead I and another lead involving a specific body site, i.e., either the V3 or V5 electrode positions, or the abdomen. Results: The best performance on the test dataset is obtained using models that incorporate all four leads. The CNN model performs slightly better than the GBDT model, with a sensitivity of 0.77 and specificity of 0.75 compared to 0.77 and 0.72, respectively. When distinguishing between AMI and healthy participants, the specificity increases to 0.94 for the CNN model and 0.90 for the GBDT model. Feature importance analysis shows that the GBDT model primarily relies on the J point, while the CNN model primarily relies on the QRS complex. Conclusions: wECG-based AMI detection shows considerable promise in out-of-hospital settings. However, caution is needed as CNN explanations rarely agree with the ECG intervals typically analyzed in clinical practice
Exploring the synergy of human-robot teaming, digital twins, and machine learning in Industry 5.0: a step towards sustainable manufacturing /
No full textSustainable manufacturing remains a central objective of Industry 5.0. By successfully implementing harmonic human-robot teams in intelligent industrial systems, the efficiency and well-being of human workers can be increased. Achieving this requires a gradual approach from caged robots to advanced, seamless collaboration between humans and robots. Initially, that means transitioning to human-robot interaction (HRI) where there is an exchange of commands between the human and the robot. Further advancements within safety considerations, including collision avoidance through advanced machine vision, enable the exchange of workspace that defines human-robot collaboration (HRC). The next stage is physical HRC (pHRC) which requires safe and controlled exchange of forces through impedance and admittance control. Finally, this paper describes human-robot teaming (HRT), which is defined by the exchange of solutions as teammates. This is enabled by combining cutting-edge technologies such as digital twin (DT), advanced vision sensors, machine learning (ML) algorithms and mixed reality (MR) human–machine interfaces for operators. A key contribution of this work is reviewing the integration of HRT with DT and ML, highlighting how these technologies enable seamless perception, prediction, and decision-making in human-centric industrial systems. By reviewing these technologies, the paper highlights current challenges, limitations and research gaps within the field of HRT and suggests potential future possibilities for HRT, such as advanced disassembly of used goods for a more sustainable manufacturing industry
Revealing the energy level and charge dynamics interplay in mixed Pb-Sn perovskite solar cells with novel phenoxazine and phenothiazine self-assembled monolayers /
No full textHole-selective self-assembled monolayers (SAMs) based on carbazole head groups have enabled major performance improvements of perovskite solar cells (PSCs) by eliminating parasitic absorption and nonradiative losses. However, the energy levels of the carbazole-based, commercially available SAMs poorly match the valence band maximum (VBM) of narrow-bandgap, lead-tin (Pb-Sn) perovskites, relevant for tandem applications. In this work, we expand the library of SAMs compatible with Pb-Sn PSCs by synthesizing four novel SAMs containing phenoxazine (POz) and phenothiazine (PTz) as their head groups and investigate their interaction with the Pb-Sn perovskite in detail. We obtain working devices with all SAMs, but despite significant differences between the highest occupied molecular orbital (HOMO) levels of the SAMs, the open-circuit voltage (VOC) and fill factor (FF) across devices remains similar, suggesting that the role of energy level alignment is less relevant at this interface. Through in-depth analysis including photoluminescence quantum yield (PLQY), transient photocurrent (TPC), and combined time-resolved surface photovoltage (trSPV) and time-resolved photoluminescence (trPL) measurements, we unveil the charge extraction dynamics of these systems featuring different head groups and HOMOs. This work highlights that the SAMs’ structure affects the overall charge extraction process and provides insights into the strategies needed to maximize charge extraction for more efficient PSCs
Increasing sustainability in the fashion industry by using industrial textile waste in an original design fashion collection.
No full textOne way to ensure sustainability is through the creation of individualized fashion products by applying original design solutions and utilizing industrial textile waste. The aim of this project is to analyze fashion design and manufacturing technologies and select one that can be adapted for the use of industrial textile waste in the production of sustainable, high-performance fashion products. In this project, a mini-collection of dresses was developed, based on the application of bonding technology in dress design. A literature review was conducted, covering sustainability aspects of the fashion industry, garment production technologies, and the influence of various factors on the peel strength of bonded seams. In the experimental part of the work, the mechanical properties (strength, elongation, and bending stiffness) of the main fabric (A) and decorative fabrics (D1-D7) were evaluated. Additionally, the peel strength of bonded seams was analyzed before and after washing to determine the impact of fabric direction, adhesive film thickness, and washing on the peeling strength. Based on the research results, a mini-collection of dresses was created by integrating bonding technology into the design, while incorporating 2026 fashion and interior trends and the artistic principles of the 20th-century modernist artist Piet Mondrian
CoLIME with 2D copulas for reliable local explanations on imbalanced network data /
No full textLocal Interpretable Model-agnostic Explanations (LIME) is a widely used technique for interpreting individual predictions of complex “black-box” models by fitting a simple surrogate model to synthetic perturbations of the input. However, its standard perturbation strategy of sampling features independently from a Gaussian distribution often generates unrealistic samples and neglects inter-feature dependencies. This can lead to low local fidelity (poor approximation of the model’s behavior) and unstable explanations across different runs. This paper presents CoLIME, which is a copula-based perturbation generation framework for LIME, designed to capture the underlying data distribution and inter-feature dependencies more accurately. The framework employs bivariate (2D) copula models to jointly sample correlated features while fitting suitable marginal distributions for individual features. Furthermore, perturbation localization strategies were implemented, restricting perturbations to a defined local radius and maintaining specific property values to ensure that the synthesized samples remain representative of the actual local environment. The proposed approach was evaluated on a network intrusion detection dataset, comparing the fidelity and stability of LIME under Gaussian versus copula-based perturbations, using Ridge regression as the surrogate explainer. Empirically, for the most dependent feature pairs, CoLIME increases mean surrogate fidelity by 21.84–50.31% on the merged CIC-IDS2017/2018 dataset and by 29.28–60.24% on the UNSW-NB15 dataset. Stability is similarly improved, with mean Jaccard similarity gains of 3.78–5.45% and 1.95–2.12%, respectively. These improvements demonstrate that dependency-preserving perturbations provide a significantly more reliable foundation for explaining complex network intrusion detection models
The intersection of artificial stupidity, creativity and ethics: a study of jailbreak narratives in algorithmic culture /
No full textThe article discusses the practices of creativity, where the aesthetics of conscious error is transferred to the realm of algorithms. Taking the perspective of artificial stupidity as a point of reference, the article analyzes the conditions of creativity in an algorithmic culture through close reading methodology. As one of the practices of artificial stupidity, the article discusses jailbreaks, the purpose of which is to deliberately confuse large language models, forcing systems to behave outside the intended purpose defined by the manufacturer. The article puts forward the idea that jailbreaks are a new type of dual-purpose narratives, which, on the one hand, act as a technical tool that breaks the limitations of algorithmic media, and, on the other hand, they are significant structures, facing the user and performing a certain function of self-reflection. The article formulates the narratological structure of jailbreaks, highlights their similarity to the narrative structure of conspiracy theories and raises the question for discussion: can the ethics of hypnosis help formulate appropriate ethics for working with artificial intelligence