KTU Open Journal Systems (Kaunas University of technology)
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What is not clear is not French: Reflections on syntax clarity and right-peripheral subject pronoun duplication
No full textFrench is often celebrated for its clarity and precision – a legacy shaped by Cartesian rationalism and prescriptive language policies. However, the evolving forms of spoken French challenge this ideal of fixed linguistic norms. This study examines one such feature: the right-peripheral duplication of the subject pronoun je with its tonic counterpart moi, a recurrent but underexplored phenomenon in spoken French. The primary objective is to understand how this syntactic feature functions pragmatically and emotionally in real-life discourse. Using a corpus of movie dialogues, the analysis shows that duplication plays a role in managing conversational flow, expressing personal stance, and enabling self-repair. Through a multidisciplinary lens that draws from sociolinguistics, pragmatics, and applied linguistics, the study argues that such variation enriches the expressive potential of French and complicates the rigid divide between written norms and spoken practice. It also suggests that incorporating these features into language pedagogy can support a more inclusive, realistic understanding of French as a living language
The Optimization of Biogas Upgrading: The CO2 Adsorption on Sugarcane Bagasse-Based Biochar and Zeolite
No full textMost households in Indonesia use liquefied petroleum gas (LPG) for cooking. Still, the increasing import of LPG is feared to burden Indonesia\u27s current trade balance. Therefore, to meet the demand, it needs other alternative energies. Biogas is a promising alternative energy source for households because it can be produced from agricultural waste or animal manure. Nevertheless, sustainable biogas adoption faces many challenges, including the presence of carbon dioxide (CO2), which affects the calorific value of biogas. The removal of CO2 from biogas is known as biogas purification and is often carried out using carbon material (charcoal) as a CO2 adsorbent. Carbon-based biomass waste as a CO2 adsorbent in biogas is an uncommon practice. So far, biomass waste has usually been investigated for biogas upgrading on a laboratory scale using synthetic biogas. This study investigated the use of biomass waste, sugarcane bagasse-based biochar, and natural zeolite, in the different adsorption contact times for increasing the calorific value of biogas by CO2 adsorption. The CO2 adsorption was carried out in a biogas purificator in adsorption times of 10, 15, 20, and 25 minutes at room temperature and gas pressure ranging from 6 to 8 bar. The CO2 adsorption capacity was further reduced by increasing the adsorption time. The combination of sugarcane bagasse-based biochar and zeolite exhibited a high CO2 removal capacity of 78.49% at 10 minutes of adsorption time. The increase in adsorption time saturated the CO2 adsorption capacity. The lowest CO2 adsorption capacity was achieved at 25 minutes of adsorption time with a CO2 removal capacity of 12.34%
Optimization of Hexavalent Chromium Removal from Aqueous Solution by Ascorbic Acid Treated Sugarcane Bagasse
No full textThe study focuses on developing an effective adsorbent for the removal of Hexavalent chromium from aqueous solutions. It utilizes chemically modified sugarcane bagasse cellulose, enhanced with ascorbic acid, as the adsorbent material. A comprehensive model was established to examine both the individual and combined effects of different variables on the adsorption process, employing a central composite rotatable experimental design rooted in response surface methodology (RSM). The model underwent experimental verification and statistical validation through analysis of variance (ANOVA) before determining the optimal conditions for Hexavalent chromium removal. The optimal parameters identified were an initial Hexavalent chromium concentration of 100 ppm, a pH of 2, and a modified sugarcane bagasse dosage of 0.5 g/L. Under these conditions, an impressive removal rate of 162 mg/g of Hexavalent chromium was achieved. The findings were consistent with the optimization study, and the adsorption process was well described by the Langmuir model. This research highlights the potential of utilizing agricultural waste, modified in a straightforward manner, to create a cost-effective adsorbent for heavy metal removal from water. Nevertheless, some limitations were observed regarding the material’s reuse potential and its adsorption capacity in complex wastewater conditions
How Consumers’ Materialistic Values and Narcissistic Tendencies affect their Pro-Environmental Attitudes, Intentions, and Choices
No full textThis study aims to investigate the relationship between materialism success and pro-environmental outcomes, while also considering the moderating effect of narcissism. By performing this, it seeks to address the contradictions seen in previous research on this topic. The regression analysis found a little negative correlation between materialism success and pro-environmental attitudes. Surprisingly, there were positive associations discovered between pro-environmental intentions and behavior, which contradicted previous research results. The presence of narcissism did not have a substantial impact on these interactions. Furthermore, the Chi-square test indicated that narcissism has an impact on the relationship between materialism success and pro-environmental behavior, while it does not act as a direct moderator. The complex nature of these connections emphasizes the necessity for additional investigation into specific characteristics that influence environmental attitudes and behaviors. The limitations of this study are to its narrow focus on pro-environmental intentions and behavior within a simulated online store. Further investigation is needed to examine these factors across various product categories and real-life contexts
From Local Experiments to Global Transition Pathways: Integrating Circular and Nature-Based Solutions for a Climate-Resilient Future
No full textBuilding a climate-resilient future involves a strategic integration of local actions with robust transition pathways that achieve regional and global outcomes. Such integration empowers local entrepreneurs and communities with a solid foundation for their creativity of new methods, products and services that will provide a foundation for long-lasting practices and standards. An important aspect for creating a sustainability strategy lies in developing a conversion path from a traditional “linear” economy approach to circular loops that include technological, institutional, cultural, nature-based, and market dimensions
A Comprehensive Review of Transformer Fault Diagnosis Studies Based on Dissolved Gas Analysis: Classical Methods, Historical Development of the Devices Used, Artificial Intelligence Based Methods, Accuracy of Classifications of Predictions
No full textTransformers are critical and expensive components of power systems. Therefore, it is important that these systems operate at optimum performance levels and sustainable economic conditions. In the normal operating environments, mineral oil passes on a slow and natural deterioration, while under conditions of thermal or electrical stress, the deterioration ratio increases. Due to breakdown, the hydrocarbon gases H2, CH4, C2H6, C2H4, C2H2, CO, and CO2) are composed in the transformer mineral oil. There are several conventional methods for identifying and classifying incipient faults in power transformers based on dissolved gas analysis (DGA). However, these methods have the disadvantage of not being able to distinguish situations where multiple electrical or thermal faults occur simultaneously. Due to the serious disadvantage of traditional DGA methods in terms of accuracy and consistency estimation compared to algorithm calculations made with artificial intelligence techniques, researchers have started to work intensively on artificial intelligence techniques in recent years. This comprehensive review aims to combine and present in a single source basic information about classical methods of power transformer fault diagnosis for DGA, historical development of the devices used, artificial intelligence-based methods, accuracy classifications of predictions. This investigation also revealed the contribution of the parameter optimisation process to eliminate the imbalance of the dataset in the accuracy of prediction when applying artificial intelligence techniques in DGA. In this study, the prediction performance of each research method performed with artificial intelligence techniques in fault diagnosis among the compared methods was analysed. This review emphasises the importance of eliminating dataset imbalance by performing parameter optimisation in artificial intelligence technique with an in-depth research-orientated perspective. As a result, this study not only encourages new ideas, but also provides a comprehensive source of literature for future accessibility of the subject
Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis
No full textThis study introduces an intelligent method to monitor grid-connected solar power stations, focussing on detecting problems in their energy output through the use of artificial neural networks (ANN). The main goal is to improve energy efficiency and bolster the reliability of solar power plants by forecasting their performance through real-time data analysis and modelling essential operational variables. The research was carried out in a solar field in AOULEF-ADRAR (South of Algeria), which covers six hectares and consists of 20,460 solar panels with an efficiency of 15 % to 20 %. The cumulative installed capacity is 5 MW, and the system is connected to a 30 kV electrical grid. The experimental findings validated the efficacy of the suggested ANN-based fault detection method. Subsequent to a sandstorm, the system exceeded standard operational limits, culminating in a total power overshoot of 200 KW. This procedure facilitated the identification of system faults and the execution of corrective measures, including the cleaning of PV modules to restore efficiency. The research highlights the importance of artificial intelligence (AI)-based monitoring systems to reduce downtime and maintenance expenses and guarantee consistent operation of photovoltaic plants under various environmental conditions. Research advocates for the integration of artificial neural networks with other machine learning methodologies, such as support vector machines, to improve fault prediction precision. Augmenting the data set by integrating data from various PV stations in different regions may improve the adaptability of the model to different environmental conditions. This method improves the creation of intelligent self-diagnosing solar power systems, promoting increased reliability and efficiency in the integration of global renewable energy
Damage Identification of Conduit Rack in Offshore Platform Structures Based on a Novel Composite Neural Network
No full textStructural health monitoring (SHM) of offshore jacket platforms is crucial, and currently traditional deep learning methods such as artificial neural networks (ANNs) are widely used in damage identification of offshore conduit rack platform structures, which focusses on mapping feature information caused by damage to structural damage patterns. However, traditional methods have limitations in dealing with the time series data in the feature information. To improve the application of the time series information generated from offshore platform structures in damage modes, we propose a new integrated deep learning network model, which is used to improve the accuracy of the damage mode recognition based on the acceleration information of the conduit rack structure. First, the temporal convolutional network (TCN) breaks through the localisation of traditional convolutional neural networks in modelling the temporal dimension by efficiently extracting the long-term time since of the structural vibration response through an expansive causal convolution mechanism. Second, the bidirectional long short-term memory network (BiLSTM) further extracts the contextual information and global features of the data by extracting feature information in both directions and fusing the before and after correlations of vibration response signals. In addition, we adopt the Newton-Raphson-based optimiser (NRBO) optimisation algorithm for global optimisation of the hyperparameters of TCN and BiLSTM to avoid the subjectivity of manual parameter tuning, which significantly improves the model convergence speed and generalisation performance. Experimentally validated by finite element model simulation and testbed construction, our proposed NRBO-TCN-BiLSTM combined neural network damage identification accuracy is as high as 99 % on average, exceeding existing deep learning methods. The method has a wide range of applications in SHM for offshore platforms
