Miracle-dz/UniFAR-FTUs huggingface dataset:
According to BERTopic, what do Danish Parties Debate on when they Address Energy and Environment?

This paper investigates how two policy areas, Environment and Energy were dealt with by seven Danish left and right wing parties in their electoral manifestos (2007-2019) and parlia-mentary debates between 2009 and 2020. The main aim is to determine whether the topics discussed by the parties in the debates are the same as those addressed in the electoral man-ifestos, and whether the parties give the same weight to the two policy areas in the manifestos and debates. We both determine how often and for how long time the parties address the two policy areas in the two datasets, and we compare the topics addressed in the electoral manifestos and those generated by a topic modeling system, BERTopic. Both a multilingual and a Danish BERT model are tested. In our comparison, we take into account the relation between issue and party competition, the parties’ profile and their being part of the government or the opposition, as proposed by Danish political scientists. Our comparison shows that only a few parties have a consistent behavior in the Parliament and in their electoral manifestos with respect to the topics that they address.
Comparing Pre-Training Schemes for Luxembourgish BERT Models

Despite the widespread use of pre-trained models in NLP, well-performing pre-trained models for low-resource languages are scarce. To address this issue, we propose two novel BERT models for the Luxembourgish language that improve on the state of the art. We also present an empirical study on both the performance and robustness of the investigated BERT models. We compare the models on a set of downstream NLP tasks and evaluate their robustness against different types of data perturbations. Additionally, we provide novel datasets to evaluate the performance of Luxembourgish language models. Our findings reveal that pre-training a pre-loaded model has a positive effect on both the performance and robustness of fine-tuned models and that using the German GottBERT model yields a higher performance while the multilingual mBERT results in a more robust model. This study provides valuable insights for researchers and practitioners working with low-resource languages and highlights the importance of considering pre-training strategies when building language models.
Bubble up – A Fine-tuning Approach for Style Transfer to Community-specific Subreddit Language

Different online communities (social media bubbles) can be identified with their use of language. We looked at different social media bubbles and explored the task of translating be-tween the language of one bubble into another while maintaining the intended meaning. We collected a dataset of Reddit comments from 20 different Subreddits and for a smaller subset of them we obtained style-neutral versions generated by a large language model. Then we used the dataset to fine-tune different (smaller) language models to learn style transfers between social media bubbles. We evaluated the models on unseen data from four unseen social media bubbles to assess to what extent they had learned the style transfer task and compared their performance with the zero-shot performance of a larger, non-fine tuned, language model. We show that with a small amount of fine-tuning the smaller models achieve satisfactory performance, making them more attractive than a larger, more resource-intensive model.
Biogenic nanoparticles from liquid and solid matrices: biochemical and biophysical properties of Extracellular Vesicles-enriched samples from human plasma and skeletal muscle tissue

The majority of studies on extracellular vesicles (EVs) focused on samples isolated from liquid matrices, such as cell culture media and blood, due to their accessibility. However, recent research highlights the emerging roles of EVs derived from solid tissues, including the brain, muscles and tumors. Investigating EVs from the extracellular matrix of solid tissues offers insights into their microenvironment and potential biological influences on surrounding cells. This study presents a method for comparing EV-enriched samples from solid (human skeletal muscle biopsy) and liquid (plasma) matrices, addressing technical challenges and minimizing biases in separation techniques. By employing optimized protocols and advanced analytical techniques, the study reveals differences in biochemical composition, nanomechanical properties, particle yield, size distribution, and colloidal stability between human muscle and plasma EVs. Understanding these distinctions may contribute to the development of novel diagnostic assays for muscular pathologies and shed light on EVs’ roles in diverse tissue environments. Graphical abstract Extracellular vesicles (EVs) studies have primarily focused on liquid matrices like cell culture media and blood. However, emerging research highlights the importance of investigating EVs from solid tissues such as the brain, muscles, liver, and tumors. This study introduces a method to compare EVs-enriched samples from solid (human skeletal muscle) and liquid (plasma) matrices, addressing technical challenges and minimizing biases in separation techniques. Utilizing standardized protocols and advanced analytical methods, the research reveals differences in biochemical composition and biophysical properties between muscle and plasma EVs. Created with BioRender.com
Steatotic liver disease induced by TCPOBOP-activated hepatic constitutive androstane receptor: Primary and secondary gene responses with links to disease progression

Constitutive Androstane Receptor (CAR, Nr1i3), a liver nuclear receptor and xenobiotic sensor, induces drug, steroid and lipid metabolizing enzymes, stimulates liver hypertrophy and hyperplasia, and ultimately, hepatocellular carcinogenesis. The mechanisms linking early CAR responses to subsequent disease development are poorly understood. Here we show that exposure of CD-1 mice to TCPOBOP, a halogenated xenochemical and selective CAR agonist ligand, induces pericentral steatosis marked by hepatic accumulation of cholesterol and neutral lipid, and elevated circulating alanine aminotransferase levels, indicating hepatocyte damage. TCPOBOP-induced steatosis was weaker in the pericentral region but stronger in the periportal region in females compared to males. Early (1-day) TCPOBOP transcriptional responses were enriched for CAR-bound primary response genes, and for lipid and xenobiotic metabolism and oxidative stress protection pathways; late (2-wk) TCPOBOP responses included many CAR binding-independent secondary response genes, with enrichment for immune response, macrophage activation, and cytokine and reactive oxygen species production. Late upstream regulators specific to TCPOBOP-exposed male liver were linked to pro-inflammatory responses and hepatocellular carcinoma progression. TCPOBOP administered weekly to male mice using a high corn oil vehicle activated carbohydrate-responsive transcription factor (MLXIPL)-regulated target genes, dysregulated mitochondrial respiratory and translation regulatory pathways, and induced more advanced liver pathology. Thus, TCPOBOP exposure recapitulates histological and gene expression changes characteristic of emerging steatotic liver disease, including secondary expression changes in liver non-parenchymal cells indicative of transition to a more advanced disease state. Upstream regulators of both the early and late TCPOBOP gene responses include novel biomarkers for foreign chemical-induced metabolic dysfunction-associated steatotic liver disease.
The Strongest Protein Binder is Surprisingly Labile

Bacterial adhesins are cell-surface proteins that anchor to the cell wall of the host, thus initiating infection. The initial step in infection is precisely the binding to fibrinogen (Fg) from human tissue, after which bacteria can colonize the heart valves by the formation of biofilms. The study of this family of proteins is hence essential to develop new strategies to fight bacterial infections. In the case of Staphylococcus aureus, there exists a type of adhesins known as Microbial Surface Components Recognizing Adhesive Matrix Molecules (MSCRAMMs). Here, we focus on one of them, the Clumping Factor A (ClfA), which has been found to bind Fg through the dock-lock-latch (DLL) mechanism. Interestingly, it has recently been discovered that MSCRAMMs proteins employ a catch-bond to withstand forces exceeding 2 nN, making this type of interaction as mechanically strong as a covalent bond. However, whether this strength is an evolved feature characteristic of the bacterial protein or is typical only of the interaction with its partner is not known. Here we combine single-molecule force spectroscopy (smFS), biophysical binding assays and molecular simulations to study the intrinsic mechanical strength of ClfA. We find that despite the extremely high forces required to break its interactions with Fg, ClfA is not by itself particularly strong, in the absence of its human target. Integrating the results from both theory and experiments we dissect contributions to the mechanical stability of this protein.
Sign Language Classification With MediaPipe Hand Landmarks

The research presented here offers a strategy for addressing the communication issues that the deaf-mute community face on a regular basis. The suggested method uses the open-source MediaPipe framework and machine learning algorithms in an effort to simplify Sign Language Recognition (SLR) by utilizing recent developments in artificial intelligence. The created prediction model is intended to be portable and adaptable to smart devices, allowing for easy integration into regular communication tools. Two datasets—the American Sign Language number system and American Sign Language gestures were used to assess the framework's capabilities. The model's astounding average accuracy of 97% demonstrates the Support Vector Machine (SVM) algorithm's dominance over other machine learning techniques in terms of effectiveness and accuracy.
GreenGuard: A Fusion Approach for Plant Leaf Disease Detection Using Deep Learning Architecture

In Indian Cuisine Tomatoes plays a substantial role on the everyday platter. India is one of the largest producers of tomatoes in the world, nevertheless, the stature and volume of tomato yield befall accompanying numerous recent diseases. Tomatoes may help fight various diseases in human health but it is appropriate to save tomatoes from getting affected by a variety of diseases. Existing CNN models are good in disease classification, still, accurate disease detection with minimal computational complexity is a challenging task. A fusion-based voting strategy is performed to propose a novel disease detection technique with optimized deep learning for enriching the detection accuracy. A deep learning-based CNN model including EfficientNet, ShuffleNet, and SqueezeNet categorizes the diseases by using the following iteration, Identification of plant leaf images, pre-treatment of images, segmentation, feature extraction, and categorization of different diseases based on the voting strategy. The effectiveness of this study is assessed using various evaluation metrics and among the CNN models evaluated, EfficientNet achieved the highest accuracy at 98.74%, followed by SqueezeNet at 97.39%, and ShuffleNet at 95.86%. Enabling farmers to achieve bountiful crop yields and enhance food production, this research assists in early disease detection in plants with minimal computational complexity.
Real Time Object Recognition Based on YOLO Model

Real-time object recognition is a fundamental task in computer vision with many applications. This paper presents a comprehensive survey of the evolution of the You Only Look Once (YOLO) object detection models, spanning from YOLOv1 to YOLOv8. Each iteration of the YOLO model is examined in detail, elucidating architectural advancements and innovations that have propelled real-time object recognition performance. The survey delves into the strengths and limitations of each YOLO version, highlighting their respective contributions to the field. Additionally, two prominent practical implementations of YOLO models are elucidated, exemplifying the models' efficacy in complex scenarios. The first case study explores YOLO's role in enabling real-time object detection for autonomous driving systems, enhancing safety and situational awareness. The second case study investigates the integration of YOLO models in unmanned aerial vehicles, showcasing their utility in aerial surveillance and reconnaissance. By providing an in-depth exploration of YOLO models and their evolution, this paper equips researchers and practitioners with a comprehensive understanding of real-time object recognition techniques. Furthermore, the analysis of practical applications underscores the tangible impact of YOLO models on cutting-edge technologies. Looking forward, this survey sets the stage for future advancements in real-time object recognition, addressing challenges and opportunities for refining performance in dynamic and complex environments.
Upper water mass variability in the Anegada–Jungfern Passage, NE Caribbean, during the last 11,100 cal. yr

Using qualitative and quantitative analyses of planktonic foraminifera assemblages, δ18O measurements, Mg/Ca–temperature relationship (Globigerinoides ruber pink sensu stricto) data and sea-surface temperatures (SSTs) derived from artificial neural network (ANN) transfer functions, a reconstruction was made of upper water mass variability in the Anegada–Jungfern Passage (AJP), northeastern Caribbean Sea, over the last c. 11,100 years. Our record is based on the study of two marine sediment cores and reveals three main circulation stages during the Holocene. In the early-Holocene (11,100–6300 cal. yr BP), ANN-based SST estimations indicate moderately cooler than present winter condition in the NE Caribbean with SSTs of ~25.5°C. These conditions presumably reflect advection of well-mixed upper water masses from the Guyana upwelling area associated with a strong Atlantic Meridional Overturning Circulation (AMOC) and enhanced trade wind activity, linked to a more northerly location of the inter-tropical convergence zone (ITCZ). Between 6300 and 3700 cal. yr BP, a relative warming of winter SSTs (~26.5°C) was probably related to a weaker circulation and upper water mass mixing because of less intense trade wind activity, as the ITCZ moved southwards. From 3700 cal. yr BP to the present, the region was characterised by small seasonal SST variations and generally stable winter and summer SSTs. The data suggest a minor shift in the (sub)surface inflow pattern during the last 2000 years, possibly related to changes in Northern Hemisphere large-scale atmospheric circulation also observed at higher latitude. The ANN-based temperature pattern is supported by fluctuations in the Mg/Ca-derived temperature record, although temperature maxima derived from the Mg/Ca ratio appear anomalously high. On a Holocene timescale, we conclude that the northeastern Caribbean SST and circulation regime have been mainly dependent on the position of the ITCZ, which, in turn, is controlled by changes in hemispheric solar insolation.
Factors affecting Moroccan Citizens’ intention to use E-Government Services – A Measurement Model

Morocco, which is undergoing rapid socioeconomic development is facing challenges in implementing online administration systems. Factors such as technological advancements, government regulations, user behavior, and socio-economic constraints affect the quality and utilization of these services. This research examines factors influencing citizens' intention to use e-services in Morocco using the Technology Acceptance Model (TAM), considering perceived utility, ease of use, intention of use, social influence, user satisfaction, and trust.To design a standardized measurement tool for e-government (E-Gov) services adoption, an exploratory analysis approach was adopted. Based on the analysis of a survey dataset collected from 44 citizens using e-gov services, it can be advocated that the construct validity and internal consistency are both highly satisfactory for the variables examined.
LPL-SLAM: LiDAR SLAM with Plane and Line Optimization for Structural Environment

In LiDAR SLAM, data association of sparse LiDAR scan and high computational complexity of point-to-model metrics limit real-time applications of joint optimization module like Bundle Adjustment in Visual SLAM, which leads to cumulative errors deteriorating performance. To address this drawback, in this paper, we propose LPL-SLAM with plane and line optimization for the structural environment. Since plane and line primitives are ubiquitous in man-made environments, we treat the primitives as landmarks to process scans. We introduce line and plane factors relying on line-to-line and plane-to-plane metrics to optimize keyframe poses, planes and lines. With the line and plane factors, our system avoids a large-scale optimization problem and yields accurate and consistent tracking poses. Experimental results demonstrate that LPL-SLAM outperforms the state-of-the-art algorithms and achieves real-time performance.
Mitigation of Disturbances caused by Voltage Harmonics using Grid-Supporting Converter

This paper presents an impact assessment of voltage harmonics on the transient behaviour of microgrid converter; as it often results in poor power quality, using a virtual synchronous generator (VSG) based grid supporting power converter /inverter. Power quality improvement is one of the major areas of concern for modern energy producers using power electronics converters. VSGs are known for imitating the performance characteristics of conventional synchronous generators. The proposed VSG employs a robust control structure that reduces the effects of voltage harmonics; small transient caused by abrupt changes in mode of operation or due to faults, preventing malfunction of protection relays. The proposed work emphasizes on the importance of VSG control using fractional control characteristics. It produces robust and reliable control irrespective of disturbances in the incoming grid voltage or due to the changes in load dynamics across the point of integration (PCC). MATLAB/simulink is used for validation of the proposed work.
The Audiobook Project Based Learning: An Approach Aimed at Improving Students’ English Reading Fluency

Since one of the objectives of the English in Focus (EIF) class was to help students improve their reading fluency, the research aimed to investigate the effects of an audiobook project on students’ reading fluency in the EIF class based on students’ perception. The participants were students from two EIF classes, for a total of eighty-three students. The investigation was conducted using a qualitative approach, and the data were gathered using a questionnaire. The questionnaire was divided into two parts: the first consisted of fifteen statements accompanied by a Likert scale, and the second contained two open-ended questions designed to collect more detailed information. Within 7 meetings, the audiobook implementation project was successfully finished. After finishing the audiobook project, the students’ reading fluency and experience were significantly enhanced, as demonstrated by the results of the research. During the course of working on the audiobook project, students were able to demonstrate correct intonation and articulation when reading English text, learned new words, and improved their ability to work together on the project by discussing it with the other members of their group. In conclusion, based on students’ perception, the research findings indicate that implementing an audiobook project to improve reading fluency is viable.
Numerical evaluation and analysis of the occurrence of earth fissures in faulted sedimentary basins

This dissertation describes the occurrence of pumping-induced earth fissures associated with quaternary faulting using numerical simulations. The Eglington Fault located in Las Vegas valley has been selected as the prototype fault described herein. The finite-element software program ABAQUS is used for the numerical simulations. The Eglington fault area is chosen because it represents one of the best examples displaying the complex relationship between fissuring, faulting and pumping-induced stress. This fault is known to influence both the vertical and horizontal deformation patterns through the accumulation of stress in its vicinity. The result is that fissures are observed on both sides of the fault and in close proximity to the fault plane. In addition to the complex fault-fissure connection, a thick caliche-rich vadose zone with weak mechanical strength allows for the initiation and propagation of fissures. The numerical analysis a) investigates the geometrical and hydromechanical features of the zone of influence surrounding the Eglington Fault; b) identifies the zones of accumulated stress on the surface and at depth that can lead to fissuring; and c) simulates the onset and propagation of tensile-induced fissures. Three-dimensional numerical simulations of this fault indicate that a 100-meter wide fault-zone composed by sand-like material best reproduces the conditions of stress that may lead to fissuring in the vicinity of the fault. Additionally, two-dimensional models reveal that two main mechanisms promote the accumulation of stress in the vicinity of the fault zone: one is the counterclockwise rotation of the unsaturated portion of the fault zone; the other is the differential compaction caused by the difference in the accumulated thickness of compressible layers. Tensile stress is concentrated on the surface in the hanging wall, but maximum shear stress zones are simulated to occur on both sides of the fault at the contact between the saturated aquifer and the vadose zone. A final analysis of the initiation and propagation of tensile-induced fissures demonstrates that fissures commence and propagate only within the vadose zone, and that the propagation path is influenced by the mechanical properties of the medium and the location of the main load, which in this case is pumping.
Evaluating the Impact of Different Routing Protocols on VANET Performance

Vehicular Ad Hoc Networks (VANETs) are the backbone of Intelligent Transportation Systems, aiming to enhance road safety, traffic efficiency, and comfort in driving. One of the critical factors influencing VANET performance is the routing protocol implemented. The current study evaluates the impact of three different routing protocols, i.e., Ad-hoc On-demand Distance Vector (AODV), Destination-Sequenced Distance Vector (DSDV), and Dynamic MANET On-demand (DYMO) on VANET performance in urban scenarios. Through comprehensive simulations using a realistic city scenario, we assess each protocol's performance based on several critical metrics such as packet delivery ratio, end-to-end delay, and network overhead. Our findings indicate significant differences in the performance of these protocols under various urban network conditions (AODV), being the best among all.
Convolutional neural network optimization method based on mixed feature attention

In order to improve the accuracy of traditional convolutional neural networks in image classification task, this optimization method proposes a mixed-feature Attention mechanism (MFA). The aim of this mechanism is to find the connections between the feature maps extracted by convolutional neural network (CNN) by the multi-head attention mechanism, and the model will judge the picture type by these connections. In addition, data augmentation experiments are carried out on the experimental data, and the influence of different data inputs on the experimental results under the shared parameter network is studied. Experimental results show that the improved ResNet-34 model improves the accuracy of ImageNet 100 dataset by 1.92% and CIFAR-10 dataset by 2.38%, compared with the original model.