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Résumé : La ville d’Algérie a connu une histoire de succès depuis sa fondation Elle possédait des bases et des qualifications solides Cela l’a exposée aux raids des envahisseurs Depuis l’Antiquité pour contrôler ses débouchés, pour devenir alors en concurrence avec les plus grandes villes Le long du Moyen Âge À travers ses institutions scientifiques et intellectuelles, un grand nombre d’érudits, de juristes, de cheikhs, de mystiques, de juges, d’enseignants et de prédicateurs y ont excellé. Laissant des empreintes digitales proéminentes et diverses d’être l’auteur et d’exercer de nombreuses fonctions Cela a contribué à revitaliser la vie culturelle au Maghreb central, que ce soit dans le domaine des sciences mentales ou des sciences des transports. Nous allons essayer dans cette recherche Fournir une étude historique statistique Pour les savants de la ville d’Algérie En établissant un décompte précis de ses scientifiques Avec une classification serrée de leurs compositions Les deux ont été imprimés Ou manuscrit Ou même les manquants, en indiquant les emplois qu’ils occupaient. Mots-clés : Banu Mezghana - Scientifiques - Ville Algérie - Institutions scientifiques- Maghreb central.

Abstract This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.

Abstract In this study, we propose a framework that enhances breast cancer classification accuracy by preserving spatial features and leveraging in situ cooling support. The framework utilizes real-time thermography video streaming for early breast cancer detection using Deep Learning models. Inception v3, Inception v4, and a modified Inception Mv4 were developed using MATLAB 2019. However, the thermal camera was connected to a mobile phone to capture images of the breast area for classification of normal and abnormal breast. This study’s training dataset included 1000 thermal images, where a FLIR One Pro thermal camera connected to a mobile device was used for the imaging process. Of the 1000 images obtained, 700 images were considered for the normal breast thermography class while the 300 images were suitable for the abnormal class. We evaluate Deep Convolutional Neural Network models, such as Inception v3, Inception v4, and a modified Inception Mv4. Our results demonstrate that Inception Mv4, with real-time video streaming, efficiently detects even the slightest temperature contrast in breast tissue sequences achieving a 99.748% accuracy in comparison to a 99.712% and 96.8% for Inception v4 and v3, respectively. The use of in situ cooling gel further enhances image acquisition efficiency and detection accuracy. Interestingly, increasing the tumor surface temperature by 0.1% leads to an average 7% improvement in detection and classification accuracy. Our findings support the effectiveness of Inception Mv4 for real-time breast cancer detection, especially when combined with in situ cooling gel and varying tumor temperatures. In conclusion, future research directions should focus on incorporating thermal video clips into the thermal images database, utilizing high-quality thermal cameras, and exploring alternative Deep Learning models for improved breast cancer detection.

Abstract Cataracts are common eye disorders characterized by the clouding of the lens, preventing light from passing through and impairing vision. Various factors, including changes in the lens’s hydration or alterations in its proteins, may contribute to their development. Regular eye examinations conducted by an ophthalmologist or optometrist are imperative for detecting cataracts and other ocular conditions early on. Manual checks by caregivers pose several problems, including subjectivity, human error, and a lack of expertise. Biomedical fusion involves combining or linking various characteristics specific to certain diseases from different medical imaging resources. The primary objectives of this approach in disease classification are to reduce the error rate and increase the number of retrieved features. The aim of this study is to evaluate the outcomes associated with fusing visual features related to left and right eye cataract characteristics. Additionally, we investigate the impact of limited variability in deep learning models, specifically in the classification of cataract fundus versus normal fundus images. To address this issue, this study introduces CataractNetDetect, an innovative multi-label deep learning classification system that fuses feature representations from pairs of fundus images (e.g., left and right eyes) for the automatic diagnosis of various ocular disorders. Our focus is on achieving improved performance by stacking discriminative deep feature representations to combine two fundus images into a unified feature representation. Several deep learning architectures are utilized as feature descriptors, including ResNet-50, DenseNet-121, and Inception-V3, enhancing the resilience and quality of representations. Fine-tuning of these DL architectures is conducted using the ImageNet dataset, followed by an integrated stacking approach combining ResNet-50, DenseNet-121, and Inception-V3 models. The model is trained on the publicly available ODIR-5k dataset, which includes 5000 left/right eye images depicting eight different ocular conditions, ranging from healthy states to uncommon ailments such as cataracts, glaucoma, age-related macular degeneration (AMD), diabetes, hypertension, and myopia abnormalities. Moreover, extensive preprocessing of the images is performed, including data augmentation, noise reduction, contrast enhancement, scaling, and circular border cropping. The CataractNetDetect system demonstrates F1-scores, AUC, and maximum validation scores of 98.0%, 97.9%, and 100%, respectively. This ensemble-based model distinguishes itself by surpassing the performance of conventional established methodologies, including ResNet-50, DenseNet-121, and Inception-V3, thereby underscoring its efficacy in diagnostic applications.

Abstract Entity Resolution (ER) is the problem of semi-automatically determining when two entities refer to the same underlying entity, with applications ranging from healthcare to e-commerce. Traditional ER solutions required considerable manual expertise, including domain-specific feature engineering, as well as identification and curation of training data. Recently released large language models (LLMs) provide an opportunity to make ER more seamless and domain-independent. Because of LLMs’ pre-trained knowledge, the matching step in ER can be made easier by just prompting. However, it is also well known that LLMs can pose risks, that the quality of their outputs can depend on how prompts are engineered, and that the cost of using LLMs can be significant. Unfortunately, a systematic experimental study on the effects of different prompting methods and their respective cost for solving domain-specific entity matching using LLMs, like ChatGPT, has been lacking thus far. This paper aims to address this gap by conducting such a study. We consider some relatively simple and cost-efficient ER prompt engineering methods and apply them to perform product matching on two real-world datasets widely used in the community. We select two well-known e-commerce datasets and provide extensive experimental results to show that an LLM like GPT3.5 is viable for high-performing product matching and, interestingly, that more complicated and detailed (and hence, expensive) prompting methods do not necessarily outperform simpler approaches. We provide brief discussions on qualitative and error analysis, including a study of the inter-consistency of different prompting methods to determine whether they yield stable outputs. Finally, we consider some limitations of LLMs when used as a product matcher in potential real-world e-commerce applications.

Abstract The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks.

Abstract Myocarditis is a significant public health concern because of its potential to cause heart failure and sudden death. The standard invasive diagnostic method, endomyocardial biopsy, is typically reserved for cases with severe complications, limiting its widespread use. Conversely, non‐invasive cardiac magnetic resonance (CMR) imaging presents a promising alternative for detecting and monitoring myocarditis, because of its high signal contrast that reveals myocardial involvement. To assist medical professionals via artificial intelligence, the authors introduce generative adversarial networks ‐ multi discriminator (GAN‐MD), a deep learning model that uses binary classification to diagnose myocarditis from CMR images. Their approach employs a series of convolutional neural networks (CNNs) that extract and combine feature vectors for accurate diagnosis. The authors suggest a novel technique for improving the classification precision of CNNs. Using generative adversarial networks (GANs) to create synthetic images for data augmentation, the authors address challenges such as mode collapse and unstable training. Incorporating a reconstruction loss into the GAN loss function requires the generator to produce images reflecting the discriminator features, thus enhancing the generated images' quality to more accurately replicate authentic data patterns. Moreover, combining this loss function with other regularisation methods, such as gradient penalty, has proven to further improve the performance of diverse GAN models. A significant challenge in myocarditis diagnosis is the imbalance of classification, where one class dominates over the other. To mitigate this, the authors introduce a focal loss‐based training method that effectively trains the model on the minority class samples. The GAN‐MD approach, evaluated on the Z‐Alizadeh Sani myocarditis dataset, achieves superior results (F‐measure 86.2%; geometric mean 91.0%) compared with other deep learning models and traditional machine learning methods.

Abstract Diagnosing individuals with autism spectrum disorder (ASD) accurately faces great challenges in clinical practice, primarily due to the data's high heterogeneity and limited sample size. To tackle this issue, the authors constructed a deep graph convolutional network (GCN) based on variable multi‐graph and multimodal data (VMM‐DGCN) for ASD diagnosis. Firstly, the functional connectivity matrix was constructed to extract primary features. Then, the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel sizes. Furthermore, the authors brought the non‐imaging information into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between subjects. After extracting the deeper features of population graphs using the deep GCN(DeepGCN), the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD patients. The proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I (ABIDE I) dataset, achieving an accuracy of 91.62% and an area under the curve value of 95.74%. These results demonstrated its outstanding performance compared to other ASD diagnostic algorithms.

Abstract Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution. However, obtained information of these convolutional neural networks cannot completely express predicted high‐quality images for complex scenes. A dynamic network for image super‐resolution (DSRNet) is presented, which contains a residual enhancement block, wide enhancement block, feature refinement block and construction block. The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution. To enhance robustness of obtained super‐resolution model for complex scenes, a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes. To prevent interference of components in a wide enhancement block, a refinement block utilises a stacked architecture to accurately learn obtained features. Also, a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem. Finally, a construction block is responsible for reconstructing high‐quality images. Designed heterogeneous architecture can not only facilitate richer structural information, but also be lightweight, which is suitable for mobile digital devices. Experimental results show that our method is more competitive in terms of performance, recovering time of image super‐resolution and complexity. The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.

Abstract Transportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Abstract Cancer is one of the leading causes of death in the world, with radiotherapy as one of the treatment options. Radiotherapy planning starts with delineating the affected area from healthy organs, called organs at risk (OAR). A new approach to automatic OAR segmentation in the chest cavity in Computed Tomography (CT) images is presented. The proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder, which is the baseline adopted in this work. The new two‐branch CS‐SA U‐Net architecture is proposed, which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function (CS‐SA) blocks are inserted between the encoder and decoder, which enabled the use of consistency regularisation. The proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient (oesophagus—0.8714, heart—0.9516, trachea—0.9286, aorta—0.9510) and Hausdorff distance (oesophagus—0.2541, heart—0.1514, trachea—0.1722, aorta—0.1114) and significantly outperforms the baseline. The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.

Abstract Achieving high‐precision extraction of sea islands from high‐resolution satellite remote sensing images is crucial for effective resource development and sustainable management. Unfortunately, achieving such accuracy for sea island extraction presents significant challenges due to the presence of extensive background interference. A more widely applicable noise‐tolerant matched filter (NTMF) scheme is proposed for sea island extraction based on the MF scheme. The NTMF scheme effectively suppresses the background interference, leading to more accurate and robust sea island extraction. To further enhance the accuracy and robustness of the NTMF scheme, a neural dynamics algorithm is supplemented that adds an error integration feedback term to counter noise interference during internal computer operations in practical applications. Several comparative experiments were conducted on various remote sensing images of sea islands under different noisy working conditions to demonstrate the superiority of the proposed neural dynamics algorithm‐assisted NTMF scheme. These experiments confirm the advantages of using the NTMF scheme for sea island extraction with the assistance of neural dynamics algorithm.

Abstract Offline reinforcement learning (RL) aims to learn policies entirely from passively collected datasets, making it a data‐driven decision method. One of the main challenges in offline RL is the distribution shift problem, which causes the algorithm to visit out‐of‐distribution (OOD) samples. The distribution shift can be mitigated by constraining the divergence between the target policy and the behaviour policy. However, this method can overly constrain the target policy and impair the algorithm's performance, as it does not directly distinguish between in‐distribution and OOD samples. In addition, it is difficult to learn and represent multi‐modal behaviour policy when the datasets are collected by several different behaviour policies. To overcome these drawbacks, the authors address the distribution shift problem by implicit policy constraints with energy‐based models (EBMs) rather than explicitly modelling the behaviour policy. The EBM is powerful for representing complex multi‐modal distributions as well as the ability to distinguish in‐distribution samples and OODs. Experimental results show that their method significantly outperforms the explicit policy constraint method and other baselines. In addition, the learnt energy model can be used to indicate OOD visits and alert the possible failure.

Abstract Mahjong, a complex game with hidden information and sparse rewards, poses significant challenges. Existing Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI capabilities. The authors propose a transformer‐based Mahjong AI (Tjong) via hierarchical decision‐making. By utilising self‐attention mechanisms, Tjong effectively captures tile patterns and game dynamics, and it decouples the decision process into two distinct stages: action decision and tile decision. This design reduces decision complexity considerably. Additionally, a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning hands. Tjong consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 GPUs. The action decision achieved an accuracy of 94.63%, while the claim decision attained 98.55% and the discard decision reached 81.51%. In a tournament format, Tjong outperformed AIs (CNN, MLP, RNN, ResNet, VIT), achieving scores up to 230% higher than its opponents. Furthermore, after 3 days of reinforcement learning training, it ranked within the top 1% on the leaderboard on the Botzone platform.

Abstract The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks.

Abstract Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations, thus the model complexity will grow quadratically with the number of input images. To alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers, we propose a dual pooling transformer tracking framework, dubbed as DPT, which consists of three components: a simple yet efficient spatiotemporal attention model (SAM), a mutual correlation pooling Transformer (MCPT) and a multiscale aggregation pooling Transformer (MAPT). SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time dimensions. MCPT aims to capture multi‐scale pooled and correlated contextual features, which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking prediction. DPT tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on TrackingNet while maintaining a shorter sequence length of attention tokens, fewer parameters and FLOPs compared to existing state‐of‐the‐art (SOTA) Transformer tracking methods. Extensive experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

Abstract Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Abstract Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents. However, in high‐dimensional continuous spaces, the non‐stationary environment can provide outdated experiences that hinder convergence, resulting in ineffective training performance for multi‐agent systems. To tackle this issue, a novel reinforcement learning scheme, Mutual Information Oriented Deep Skill Chaining (MioDSC), is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency. These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state. In addition, MioDSC can generate cooperative policies using the options framework, allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning. MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels. The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

Abstract Deep learning has recently become a viable approach for classifying Alzheimer's disease (AD) in medical imaging. However, existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness classification. To address these issues, a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is proposed. An upgraded primary C3D network is utilised to create rougher low‐level feature maps. It introduces a new convolution block that focuses on the structural aspects of the magnetic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map output. Then, several fully connected layers are used to achieve multi‐task learning, generating three outputs, including the primary classification task. The other two outputs employ backpropagation during training to improve the primary classification job. Experimental findings show that the authors’ proposed method outperforms current approaches for classifying AD, achieving enhanced classification accuracy and other indicators on the Alzheimer's disease Neuroimaging Initiative dataset. The authors demonstrate promise for future disease classification studies.

Abstract Sentiment analysis is a fine‐grained analysis task that aims to identify the sentiment polarity of a specified sentence. Existing methods in Chinese sentiment analysis tasks only consider sentiment features from a single pole and scale and thus cannot fully exploit and utilise sentiment feature information, making their performance less than ideal. To resolve the problem, the authors propose a new method, GP‐FMLNet, that integrates both glyph and phonetic information and design a novel feature matrix learning process for phonetic features with which to model words that have the same pinyin information but different glyph information. Our method solves the problem of misspelling words influencing sentiment polarity prediction results. Specifically, the authors iteratively mine character, glyph, and pinyin features from the input comments sentences. Then, the authors use soft attention and matrix compound modules to model the phonetic features, which empowers their model to keep on zeroing in on the dynamic‐setting words in various positions and to dispense with the impacts of the deceptive‐setting ones. Experiments on six public datasets prove that the proposed model fully utilises the glyph and phonetic information and improves on the performance of existing Chinese sentiment analysis algorithms.

Abstract Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional semantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal features are not only salient but also complementary to sentiment words directly. Experimental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

Abstract Laminectomy is one of the most common posterior spinal operations. Since the lamina is adjacent to important tissues such as nerves, once damaged, it can cause serious complications and even lead to paralysis. In order to prevent the above injuries and complications, ultrasonic bone scalpel and surgical robots have been introduced into spinal laminectomy, and many scholars have studied the recognition method of the bone tissue status. Currently, almost all methods to achieve recognition of bone tissue are based on sensor signals collected by high‐precision sensors installed at the end of surgical robots. However, the previous methods could not accurately identify the state of spinal bone tissue. Innovatively, the identification of bone tissue status was regarded as a time series classification task, and the classification algorithm LSTM‐FCN was used to process fusion signals composed of force and cutting depth signals, thus achieving an accurate classification of the lamina bone tissue status. In addition, it was verified that the accuracy of the proposed method could reach 98.85% in identifying the state of porcine spinal laminectomy. And the maximum penetration distance can be controlled within 0.6 mm, which is safe and can be used in practice.

Abstract A comparative study of two force perception skill learning approaches for robot‐assisted spinal surgery, the impedance model method and the imitation learning (IL) method, is presented. The impedance model method develops separate models for the surgeon and patient, incorporating spring‐damper and bone‐grinding models. Expert surgeons' feature parameters are collected and mapped using support vector regression and image navigation techniques. The imitation learning approach utilises long short‐term memory networks (LSTM) and addresses accurate data labelling challenges with custom models. Experimental results demonstrate skill recognition rates of 63.61%–74.62% for the impedance model approach, relying on manual feature extraction. Conversely, the imitation learning approach achieves a force perception recognition rate of 91.06%, outperforming the impedance model on curved bone surfaces. The findings demonstrate the potential of imitation learning to enhance skill acquisition in robot‐assisted spinal surgery by eliminating the laborious process of manual feature extraction.

Abstract Multi‐object tracking in autonomous driving is a non‐linear problem. To better address the tracking problem, this paper leveraged an unscented Kalman filter to predict the object's state. In the association stage, the Mahalanobis distance was employed as an affinity metric, and a Non‐minimum Suppression method was designed for matching. With the detections fed into the tracker and continuous ‘predicting‐matching’ steps, the states of each object at different time steps were described as their own continuous trajectories. We conducted extensive experiments to evaluate tracking accuracy on three challenging datasets (KITTI, nuScenes and Waymo). The experimental results demonstrated that our method effectively achieved multi‐object tracking with satisfactory accuracy and real‐time efficiency.

Abstract: Thoughtful sequels had been generated during and after COVID-19 pandemic in Algerian higher education. Learning environment changed greatly, principally, in a reduction in the time spent for learning, and the use of digitalized notes posted online through Moodle platform. Therefore, students can study from there without having to resort to teacher’s assistance or sheets of paper. However, the majority of students seem not to participate in virtual education, and prefer the face to face teaching -despite less time spent for learning achievement- but with the guarantee to take the written texts of their courses. Thus, the focus of the present paper aims to examine the impact of two axes of learning process; first, it will examine the question of time as a factor quality of achievement in education in general and the higher education in particular. Second, it will peruse the importance of paper usage in written content learning, juxtaposed at the same time to online learning necessity. It is all about teacher and student scholastic performance within this dualism. Keywords: Paper, educational Time, learning quality, achievement, online drawbacks.

Abstract Plant diseases significantly threaten global agriculture, impacting crop yield and food security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and classification of plant diseases through computer vision techniques have become imperative for timely intervention. However, popular plant disease datasets often suffer from data imbalance, with certain classes underrepresented, hindering the performance of machine learning models. Traditional data augmentation methods, such as rotation and flipping, are limited in their effectiveness, especially when faced with imbalanced datasets. To address this limitation, we explore advanced data augmentation techniques, including Generative Adversarial Networks (GANs) such as CycleGAN and LeafGAN, which have shown promise in generating synthetic images. However, we propose an innovative approach of Object-based single Style Transfer on a single neural network for augmenting the plant disease dataset. This technique focuses on mitigating data imbalance issues within datasets, which can adversely affect the model’s ability to generalize across diverse classes. The proposed method is compared with state-of-the-art data augmentation techniques, highlighting its superiority in addressing data imbalance issues. Our approach aims to produce more realistic and diverse synthetic images, leading to improved model generalization and accuracy in plant disease classification tasks validated using different classifiers. The efficiency of our approach is validated through extensive experimentation and benchmarking against existing methods.

Abstract Adaptive instructional systems (AISs) refer to educational interventions designed to accommodate individual learner differences. These systems employ various approaches, such as artificial intelligence (AI), machine learning (ML), and data analytics, to analyze student performance and personalize the learning experience. This article presents a review of the current state-of-the-art of AI methods used in the development of AISs for maritime safety training. The main objective of this systematic literature review is to determine the use of AI/ML techniques in AIS and how they can contribute to the development of AIS for maritime education and training (MET) applications in addressing small data problems. Answering the research questions of the review identifies the fundamental purposes of using AI/ML techniques in developing AIS for MET. Further, the review highlights several crucial research areas, including AI techniques for modelling student and instructor knowledge, as well as ML algorithms for predicting student performance in situations with limited datasets.

Abstract The Internet has become a vital part of our daily lives, serving as a hub for global connectivity and a facilitator for seamless communication and information exchange. However, the rise of malicious domains presents a serious challenge, undermining the reliability of the Internet and posing risks to user safety. These malicious activities exploit the Domain Name System (DNS) to deceive users, leading to harmful activities such as spreading drive-by-download malware, operating botnets, creating phishing sites, and sending spam. In response to this growing threat, the application of Machine Learning (ML) techniques has proven to be highly effective. These methods excel in quickly and accurately detecting, classifying, and analyzing such threats. This paper explores the latest developments in using transfer learning for the classification of malicious domains, with a focus on image visualization as a key methodological approach. Our proposed solution has achieved a remarkable testing accuracy rate of 98.67%, demonstrating its effectiveness in detecting and classifying malicious domains.

Abstract Twitter is a rich resource for analyzing the contents of social media and extracting the age groups of users can be beneficial for recommender systems, marketing and advertising. Age detection task is an aspect of demographic information of users. In this study a large-scale corpus of Arabic Twitter users including 181k user profiles with diverse age groups consisting of −18, 18–24, 25–34, 35–49, 50–64, +65 is presented. The corpus is created by four methods: (1) collecting publicly available birthday announcement tweets using the Twitter Search application programming interface, (2) augmenting data, (3) fetching verified accounts, and (4) manual annotation. To have a best age detection model on the presented corpus, different evaluations are tested to find the model with highest accuracy and efficiency. Number of tweets, regression vs. classification, using metadata of users and tweets, using LSTM+CNN model vs. BERT are some parts of examinations done. Presented methodology is based on language and metadata features and final model is fine-tuned with BERT on 70k users and evaluated on 8200 manually annotated users. We show that our best model, compared with LSTM+CNN model and BERT-based similar model yields an improvement of up to 9% in F1-score and increment of 5% in accuracy, respectively. The model achieved macro-averaged F1-score of 44 on six age groups, and F1-score of 58 on three age groups of −25, 25–34, +35. The link of our proposed data is provided here: www.github.com/exaco/ExaAUAC .

Abstract Prominent works in the space of Natural Language Processing (NLP) have long attempted to create new innovative models by improving upon previous model training approaches, altering model architecture, and developing more in-depth datasets to better their performance. However, with the quickly advancing field of NLP comes increased greenhouse gas emissions, posing concerns over the environmental damage caused by training LLMs. Gaining a comprehensive understanding of the various costs, particularly those pertaining to environmental aspects, that are associated with artificial intelligence serves as the foundational basis for ensuring safe AI models. Currently, investigations into the CO2 emissions of AI models remain an emerging area of research, and as such, we evaluate the CO2 emissions of well-known large language models, which have an especially high carbon footprint due to their significant amount of model parameters. We argue for the training of LLMs in a way that is responsible and sustainable by suggesting measures for reducing carbon emissions. Furthermore, we discuss how the choice of hardware affects CO2 emissions by contrasting the CO2 emissions during model training for two widely used GPUs. Based on our results, we present the benefits and drawbacks of our proposed solutions and make the argument for the possibility of training more environmentally safe AI models without sacrificing their robustness and performance.

Abstract Sustainability has become a critical global concern, focusing on key environmental goals such as achieving net-zero emissions by 2050, reducing waste, and increasing the use of recycled materials in products. These efforts often involve companies striving to minimize their carbon footprints and enhance resource efficiency. Artificial intelligence (AI) has demonstrated significant potential in tackling these sustainability challenges. This study aims to evaluate the various aspects that must be considered when deploying AI for sustainability solutions. Employing a SWOT analysis methodology, we assessed the strengths, weaknesses, opportunities, and threats of 70 research articles associated with AI in this context. The study offers two main contributions. Firstly, it presents a detailed SWOT analysis highlighting recent advancements in AI and its role in promoting sustainability. Key findings include the importance of data availability and quality as critical enablers for AI’s effectiveness in sustainable applications, and the necessity of AI explainability to mitigate risks, particularly for smaller companies facing financial constraints in adopting AI. Secondly, the study identifies future research areas, emphasizing the need for appropriate regulations and the evaluation of general-purpose models, such as the latest large language models, in sustainability initiatives. This research contributes to the growing body of knowledge on AI’s role in sustainability by providing insights and recommendations for researchers, practitioners, and policymakers, thus paving the way for further exploration at the intersection of AI and sustainable development.

Abstract Automated Short Answer Grading (ASAG) has been an active area of machine-learning research for over a decade. It promises to let educators grade and give feedback on free-form responses in large-enrollment courses in spite of limited availability of human graders. Over the years, carefully trained models have achieved increasingly higher levels of performance. More recently, pre-trained Large Language Models (LLMs) emerged as a commodity, and an intriguing question is how a general-purpose tool without additional training compares to specialized models. We studied the performance of GPT-4 on the standard benchmark 2-way and 3-way datasets SciEntsBank and Beetle, where in addition to the standard task of grading the alignment of the student answer with a reference answer, we also investigated withholding the reference answer. We found that overall, the performance of the pre-trained general-purpose GPT-4 LLM is comparable to hand-engineered models, but worse than pre-trained LLMs that had specialized training.

Abstract ChatGPT is one example of an artificial intelligence (AI) tool that is gaining attention in the field of language education due to its potential as a digital language learning assistant. ChatGPT allows for customized interactions and real-time feedback to improve language learning. Yet, there is a study void on English as a Foreign Language (EFL) teachers and students’ use of ChatGPT in language learning. To fill this void, this a cross-sectional survey study investigates the perceptions and preferences of the 80 EFL teachers and the 46 EFL students in East Java, Indonesia. The study’s primary objective is to gain insight into how they perceive about using ChatGPT to improve their language learning. The results revealed possible benefits, such as better language competency and individualized learning experiences, as well as problems, such as language accuracy and technological dependence. The study also highlights the importance of pedagogical support, curricular alignment, user-friendly interfaces, and compelling interactive activities for successful integration. In light of these results, this study suggests ways in which teachers might better include ChatGPT as a digital language learning assistant. This study contributes to the body of knowledge of ChatGPT’s function in EFL instruction and will guide the improvement of other digital tools for language learning. Graphical Abstract

Résumé: La communication entre les principaux acteurs du département des Lettres et de langue françaises est incontournable et indispensable pour la continuité et la réussite du processus d’enseignement-apprentissage. Toutefois, pour ce faire, force est de constater que l’utilisation du réseau social Facebook est devenue inévitable, malgré les divers canaux officiels de communication mis en disposition par la tutelle. À travers cet article, basé sur une enquête de terrain, nous allons tenter de montrer les réelles raisons de l’abandon des plateformes universitaires officielles de communication au profit de Facebook, à savoir la formation requise pour la manipulation des premières et facultative pour le dernier. Nous mettons aussi en lumière les limites inhérentes à l’usage de ce réseau social ainsi que l'initiative de la numérisation du système de communication de l'université. Mots-clés:Communication, étudiants, enseignant, Facebook, plateforme

Résumé : La rencontre d’étrangers, le voyage ou l’exil sont des éléments qui influencent souvent la plume des écrivains. Ainsi, le poème « Etranges étrangers » de Jacques Prévert est inspiré par les vagues migratoires qu’a connu la France du XVIIIe au XXe siècle. Le poète, en tant que membre de la société française du XXe siècle et un contemporain d’une vague migratoire importante, évoque la place des migrants dans leur société d’accueil, leur condition de vie, leur contribution à l’essor du pays. Mots-clés : Poésie, lyrisme, migration, étrangers, France.

Résumé : Co-fondateur du Ballet théâtre Lemba, Michel Rafa est l’un des pionniers et fervent défenseur de la culture kongo par le biais de la musique traditionnelle. Il s’est fait connaître au monde au-delà des frontières congolaises et africaines grâce à sa troupe musicale. D’essence folklorique, le Ballet théâtre Lemba constitue un vecteur artistique qui promeut la culture traditionnelle congolaise en particulier et celle de l’Afrique en général. Au moyen de spectacles féeriques et hauts en couleur faits de danses, de chants, de mimes auxquels, il intègre des instruments de musique traditionnels kikongo, la troupe met en lumière l’identité culturelle congolaise. Une performance artistique et une prouesse musicale qui font de Michel Rafa un véritable chantre lumineux de la musique ancestrale enfouie dans l’âme immortelle des peuples africains. L’objectif de ce travail qui prend appui sur la sémiologie théâtrale consiste à montrer que la création scénique du ballet théâtre qui se fonde sur des matériaux scéniques traditionnels du terroir africain est un moyeu artistique pour la promotion et la valorisation de l’identité culturelle congolaise et africaine contemporaine. Mots-clés : création scénique, Ballet théâtre Lemba, vecteur artistique, ressorts scéniques, identité culturelle africaine contemporaine.

Résumé : On ne saurait imaginer une société sans culture, sans tradition, sans langue de communication. Les questions que l’on se pose sont les suivantes : quelle est la relation qui existe entre tradition, coutume et langue ? quelle est la place des langues dans le dialogue des cultures au Burkina Faso ? Dans un regard socio-ethnolinguistique, l’article tente de répondre à ces questions dans le double objectif d’établir le lien entre coutumes, traditions et langues et de proposer des cadres de dialogue interculturel et linguistique dans un pays cosmopolite qu’est le Burkina Faso. Pour y parvenir, une analyse documentaire soutenue par un entretien auprès des dépositaires des savoirs traditionnels nous a été d’une grande nécessité en vue de vérifier l’hypothèse selon laquelle la langue fait dialoguer les cultures, donc les traditions qu’elle pérennise pour la cohésion du monde présent et futur en corrélation avec l’histoire des peuples et des habitudes sociales. Finalement, il serait illusoire de dissocier langue et tradition qui se nourrissent mutuellement d’un dialogue à la fois générationnel et dynamique tout en constituant l’essence de l’existence même des sociétés humaines. Mots-clés : langues-cultures, coutumes, sociétés humaines

Abstract: This paper explores major issues in ethics. It considers relevant concepts related to the core branches and theories of ethics. The paper looks at notions such as bad and good as well as character as they apply to communal societies such as African traditional societies. African traditional societies are intrinsically communal in nature. Their communitarian and altruistic approaches to life often under-estimated need genuine attention. As a result, this paper argues that a synoptic look at ethics not only treats ethics from a global and cosmopolitan perspective, it also brings to bear the fundamentals of humanness, love, peace, integrity and character that ground most African Traditional Societies. Keywords : African Traditional Societies, Character, Communitarian, Ethics, Humanness.

Résumé : Cet article explore l'impact des langues étrangères sur les interactions en ligne au Maroc à travers une approche quantitative. Un questionnaire structuré a été administré à 600 participants pour recueillir des données sur l'utilisation des langues étrangères sur les réseaux sociaux, leurs préférences linguistiques, et la perception des barrières linguistiques. Les questions abordent l'utilisation fréquente ou occasionnelle des langues étrangères, les langues les plus couramment employées, les contextes d'utilisation (tels que les jeux vidéo, les réseaux sociaux, et les communications personnelles), ainsi que l'importance perçue de ces langues pour les interactions en ligne. Le questionnaire examine également les perceptions de pression pour utiliser ces langues et les associations avec le statut social ou la modernité. Les résultats fournissent une vue d'ensemble de l'influence des langues étrangères sur les pratiques et perceptions des jeunes dans un environnement numérique, permettant d'identifier les tendances majeures et les défis liés à la diversité linguistique en ligne. Mots-clés : Représentations sociales ; Langues étrangères ; Réseaux sociaux ; Attitudes des jeunes.

Résumé : Cet article met en évidence et analyse les pratiques funéraires chez les Beembe, un groupe sociolinguistique qui se trouve au Sud du Congo-Brazzaville, peu connu au regard des études anthropologiques, jusqu’ici effectués. Nos enquêtes réalisées dans les différents départements nous révèlent que les populations que nous avions rencontrées ont une manière spécifique de célébrer leurs morts. Ainsi, notre étude s’est basée à la fois sur les populations beembe qui se trouvent à Brazzaville, Mouyondzi et ses environs. En effet, chez les Beembe, les rites funéraires constituent un des éléments les plus émouvants dans la vie humaine, car cette société accorde une place de choix dans les pratiques funéraires. Mots-clés : rites, funéraires, Beembe, mort, célébrer.

Статтю присвячено висвітленню та систематизації сучасних теорій дослідження комічного як культурно-специфічного явища у рамках міждисциплінарних студій. Комічне як цікаве та багатогранне явище викликає інтерес у дослідників ще з античності. Проте визначення поняття “комічного” та його приналежність до певної наукової дисципліни і на сьогодні становить для науковців певні труднощі. У світлі сучасних студій дослідники надають перевагу вивченню даного феномену на перетині декількох наук із залученням теоретичної бази таких дисциплін як лінгвістика, культурологія, когнітивістика, прагматика тощо. У статті проаналізовано та визначено доцільність застосування підходу сучасних теорій у дослідженні комічного з огляду на його комплексність та культурну обумовленість саме на матеріалі художніх текстів та їх перекладів. З’ясовано, що для найбільш плідного дослідження механізмів виникнення та специфіки відтворення даного явища у художньому перекладі необхідне проведення досліджень на перетині перекладознавства та інших дисциплін, об’єктом дослідження яких є комічне. Обґрунтовано доцільність застосування до вивчення даного комплексного та культурно обумовленого явища у художніх текстах міждисциплінарного підходу та окреслено основні його переваги. У статі висловлено припущення, що результати такої міждисциплінарної співпраці можуть стати підґрунтям для більш глибинного перекладознавчого аналізу комічного. Такий аналіз дає можливість охопити різні рівні дослідження цього явища і вийти на нові перспективи вивчення природи комічного в художніх текстах та стратегій його відтворення у художньому перекладі. У зв’язку з тим, що труднощі, які постають перед перекладачем художнього тексту при відтворенні гумору на сьогодні науковці не розглядають як суто мовну проблему, виникає необхідність розгляду комічного у художніх текстах із врахуванням механізмів творення даного феномену і його культурної специфіки. Дослідження становить внесок у розвиток сучасної теорії художнього перекладу, результати дослідження можуть бути використані у викладанні теоретичних та практичних курсів перекладу.

У поетикальному розмаїтті західноєвропейського бароко світоглядною домінантою постає пошук єдності у протиріччях буття, що майже завжди занурює тогочасну людину у теологічний контекст. Особливо виразно це простежується у творах англійських метафізиків XVII ст., унікальний стиль яких заведено розглядати у річищі становлення європейсько-континентальної поетичної традиції. Водночас звернення до французького варіанта бароко як своєрідного літературного імператива у компаративних студіях зі «школою» метафізиків в Англії зумовлюється специфікою історико-мистецького процесу, що продукує певні художні стратегії поетизації авторського досвіду / сакралізації потаємних (інтимних) почуттів у поетиці розглянутих напрямів. Тож у статті експлікується оригінальність засобів поетичного оприявлення власних світоглядних принципів митцями французького бароко й англійської «метафізичної школи», що набувають виразності у тісному зв’язку з авторськими парадигмами гедоністично-релігійного vs духовного освоєння дійсності. Досліджувані засоби актуалізуються на образно-сюжетному й структурному рівнях, що визначає методологічні засади розвідки, які базуються на концептуальних підходах компаративного аналізу з елементами герменевтичного, імагологічного, лінгвостилістичного, а також культурно-історичного методів. Акцентовано, що парадоксальне введення у любовні вірші з гедоністичними мотивами власних глибокодумних міркувань про витоки світобудови наближає лірику французьких авторів (Теофіль) до пасажів англійців, які навіть у любовних віршах порушують серйозні питання людського буття, релігії, світоустрою (Е. Марвелл). Водночас релігійна лірика французів (д’Обіньє, Спонд, Ла Сеппед) контрастує з метафізичними уявленнями про трансцендентний світ (Дж. Донн, Дж. Герберт, Ф. Кверлз, Г. Воен) саме трактуванням поширених у бароко тем швидкоплинності земного буття, смертності всього сущого, слабкості людської істоти, які у французів порушуються стилістично досить одноманітно. Натомість метафізична напруженість і емотивність вивільнення найпотаємніших порухів душі підносять духовну лірику англійських літераторів на рівень споглядальної поезії.

The paper addresses language units that constitute semantic space "catastrophe" within alternative English-mediated worldviews. Designation units synonymic or contextually related to catastrophe / disaster, were chosen from the Web 2021 (enTenTen21) corpus suggested by the Sketch Engine and from the custom corpus of present-day rock lyrics processed via the Ant.Conc 3.5.8 tools. The content of the respective concept is considered through the prism of a logical model that addresses an entropic irrevocable transformation of an open system. He paper focuses on the comparative analysis of the space's composition in two worldview variants. The structure of the semantic space is identified as a field, i.e. a dynamic volumetric continuum that sports multi-level organization as well as each level's zonal segmentation. Special attention is paid to functional-semantic and semiotic properties of the space-field's components. The article employs an interdisciplinary approach that encompasses the myth-oriented semiosis theory and broad inter-systemic analogies ("M-logic"). Key words: system, alternative world, semantic space, corpus, myth Стаття розглядає мовні одиниці, які складають семантичний простір "катастрофа" у структурі альтернативних картин світів, актуалізованих засобами англійської мови. Номінативні одиниці, синонімічні або контекстуально пов’язані з "катастрофою", були відібрані з корпусу Web 2021 (enTenTen21), запропонованого Sketch Engine, та з користувацького корпусу сучасних рок-пісень, оброблених за допомогою інструментів Ant.Conc 3.5.8. Зміст відповідного поняття розглядається крізь призму логічної моделі, яка стосується ентропійної безповоротної трансформації відкритої системи, що призводить до її зламу. У статті здійснено порівняльний аналіз композиції простору у двох варіантах картини світу. Структура семантичного простору ідентифікується як поле, тобто динамічний об’ємний континуум, який має багаторівневу організацію, а також зональну сегментацію кожного рівня. Особливу увагу приділено функціонально-семантичним і семіотичним властивостям компонентів простору-поля. У статті використано міждисциплінарний підхід, який охоплює міфоорієнтовану теорію семіозису та широкі міжсистемні аналогії («М-логіка»). Ключові слова: система, альтернативний світ, семантичний простір, корпус, міф

The study is devoted to the analysis of forms of intertextuality in postmodern drama on the example of Wendy Wasserstein's play “Old Money”. Postmodernism, as a cultural paradigm, is characterized by complex relations with its own history and traditions. The article explores how the use of various texts, allusions, and cultural references in “Old Money” contributes to the creation of a multilayered and complex text characteristic of postmodern drama. By analyzing the intertextual relations in this play, the purpose of the article is to reveal the peculiarities of Wasserstein's postmodern creative approach and their influence on the perception and interpretation of the playwright's text. The results of the study may be useful for further understanding of intertextual strategies in postmodern literature and studying postmodern aspects in the work of Wendy Wasserstein. The study of intertextuality in “Old Money” allowed us to better understand and evaluate the artistic world of the play, to reveal the relationship between texts and contexts that interact in creating meaning. We have identified the features and significance of each of the intertextual allusions, as well as their impact on the perception and interpretation of the play. In addition, the analysis of intertextual connections shed light on the specificity and value of the author's own creative method, revealing his cultural and intellectual preferences. Key words: intertextuality, postmodernism, allusion, drama.

This paper presents the results of a retrospective analysis of the scientific views on the energetics of human communicative and speech behavior. The etymology of the lexeme ‘energy’ is looked into; the definitions of the notion of ‘energy’ available in 17 lexicographic sources are analyzed, and semantic units incorporated into its lexical meaning are extracted. The analysis of their content makes it possible to define four essential features of the general scientific notion of ‘energy’ and synthesize its definition. As the energetics of human communicative behavior is traditionally seen as divided into the psychic and the physical components, its adequate description is possible only with the instruments of the integrative approach. The issues of mutual conversion of several types of energy to cater for various individual’s behavioral programs, as well as physical and physiological aspects of speech energetics have already been researched to a certain extent. Less attention has been paid to the psychic energy of a person, which nonetheless is intuitively considered to be a significant motive of any form of human activity. A concise account of historical views on the human psychic energy, nominated “prana”, “chi”, “soul”, “spirit”, “entelechy”, “energeia”, “psychic pneuma”, “animal spirits”, etc. is provided in the paper. Milestones in the research of the neurophysiological aspect of the energy supply of human communicative and speech behavior are reviewed. Special attention is paid to Freud’s description of the human psychic energy “economy” as the first full-fledged theory, capable of forming the basis for modeling the energetics of human communicative and speech behavior. Mention is made of the modern concept of “allostasis” oriented towards an anticipatory forecasting of energy needs by the organism rather than its posterior restoration upon some significant psychic events. Due to a lack of theoretical and experimental linguistic research, taking into consideration energy dynamics of the communicants’ psychic processes, a universal stochastic model of utterance generation, developed by A. Kalyta and O. Klymeniuk appears specifically promising. It could be effectively used in the development of the methodology of an experimental-phonetic study of conflict talks, typically marked with a significant level of emotional tension.

The notion of the “new woman”, which emerged as a feminist ideal in Western consciousness during the late 19th century, resonates throughout both European and US-American literature. The main character in Olga Kobylanska’s novella “Eine Unzivilisierte” (1898) embodies the theme of female liberation in Ukrainian literature of the era. Similarly, the central female figures in Edith Wharton’s short stories, “The Other Two” (1902) and “The Mission of Jane” (1904), highlight the heightened role of women within the patriarchal society of the United States during that period. Applying close reading, block method and typological approach for the study of diachronic aspects of literary relationships the article analyzes the literary embodiment of the concept of “new woman” in short prose of Ukrainian and US-American women authors. In “Eine Unzivilisierte”, the protagonist Paraska actively opposes marrying a man chosen by others and is decisive in her own choice of partners; she does not feel any obligation to be a typical “housewife”, or to correspond to the typical picture of a woman, with all the activity connoted as typically “feminine”. Independent of her husband, the “new woman” at the turn of the centuries, Paraska proudly appeals to her right and ability to find another partner at any given moment. In contrast to Kobylanska’s novella Wharton’s short stories demonstrate rather submissive behavior of their central female characters, both named Alice. Maternity enables both Alices to subvert the established hierarchy achieving some freedom of “new woman”. In Kobylanska’s novella, the rural setting contrasts with the urban backdrop of Edith Wharton’s short stories, symbolizing the societal constraints faced by the “new woman”.

Статтю присвячено аналізу основних понять семіотики та їхнього застосування в мультимодальних рекламних текстах товарів широкого вжитку. Метою статті є розвідка рекламного дискурсу як модельно-семіотичної діяльності в англомовному рекламному дискурсі, а також окреслення основних семіотичних стратегій та лінгвістичних засобів у формуванні англомовних рекламних повідомлень. Основні завдання – розглянути роботи українських та закордонних дослідників із проблеми, виокремити й описати ключові семіотичні стратегії та лінгвістичні засоби англомовних рекламних текстів, а також визначити роль семіотичних стратегій у взаємодії з аудиторією та їхнє відтворення культурних концепцій через рекламну комунікацію. У статті подано всебічний огляд деяких ключових принципів і концепцій семіотики та їхнього значення для сфери реклами, висвітлено конструювання денотативних повідомлень про продукт, та бажаних конотативних асоціацій. Також пояснюється роль дотримання відомих кодів та залучення інтертекстуальних посилань у рекламних оголошеннях як прийомів створення зв'язку з аудиторією. Дана стаття значною мірою спирається на фундаментальні семіотичні теорії, такі як тріадна модель знаку Пірса, відмінність між денотатом і конотацією, роль кодів і культурних конвенцій, а також феномен інтертекстуальності. Стаття показує, як рекламодавці використовують знаки, символи та пов'язані з ними значення для створення переконливих кампаній, що мають культурний резонанс. Актуальність дослідження полягає в тому, щоб розуміти, як реклама формує погляди та стереотипи, а також як сучасні технології змінюють способи взаємодії з аудиторією через мовні та семіотичні стратегії.

The paper researches the peculiarities of the development of the reference to Donald Trump in rap lyrics. The paper focuses on the analysis of stylistic devices and metaphors used by the authors of rap lyrics when referring to the image of Donald Trump as a businessman and politician; as well as the analysis of the sociocultural context of these references, their significance within the genre and American culture in general, and their stylistic effect.

In the article, the linguistic-cognitive aspect of English borrowings in contemporary Italian is considered. The globalization of the English language is one of the driving forces that raises questions about language contacts and interactions in modern linguistics. The presented scientific investigation aims to identify the linguistic-cognitive features of the usage of English borrowings in Italian-language news discourse. The linguistic-cognitive approach allows for analyzing the behavior of speakers and the organization of conceptual space in human consciousness. The article discusses the factors and phenomena to which users of the linguistic code are subordinate, as well as the impulses of internal and external systems that accelerate the penetration of English equivalents into the Italian language. English borrowings are revealed taking into account the universal characteristics of open systems. The article asserts that the analyzed linguistic-cognitive processes are universal-systemic, as they can be applied to the analysis of factors and phenomena of various natures, including linguistic and psychological.

Cтаттю присвячено дослідженню фрейму «Хвороба» як складової концептуального поля «Медицина» політичної метафори в українській мові. Концептуальні поля політичної метафори за поданням сучасної когнітології є однією з основних ментальних процесів, що є способом пізнання, структурування і пояснення навколишнього нас світу. Концептуальні поля політичної метафори не тільки відтворюють фрагменти соціального досвіду певної культурної спільності, а й значною мірою формує цей досвід. Концептуальне поле «Медицина» вербалізується через семантичне поле, яке взагальному вигляді складається з лексичних одиниць, що характеризують стан у цілому. У свою чергу, це концептуальне поле складається із ланцюжка структурованих фреймів, які являють собою не просто сукупність фреймів, ознаки яких впорядковані в певній ієрархії, а систему взаємопов'язаних, взаємоперетинаючихся когнітивних структур, що репрезентуються в мовній картині світу за допомогою різноманітних способів номінації. Політичні метафори фрейм «Хвороба» як складової концептуального поля «Медицина» зазвичай викликають негативні асоціації у читача, адже вони пов’язані з поганим самопочуттям людини та неприродним функціонуванням соціально- політичних систем. Вражений хворобою орган соціального і політичного життя так само, як і заражена людина, «відчуває» страждання, біль і та є досить вразливим. У нашій статті проаналізовано рівень дослідження концептуального поля політичної метафори та досліджено рівень вивчення поняття «фрейм» вітчизняними та закордонними лінгвістами. Визначено лексико-семантичну базу фрейму нашого дослідження в українській мові, розглянуто які саме слоти закладені у поданому фреймі та наведено приклади з аналізом до них. У статті виокремлено дві основні групи хвороб: фізичні та психічні, кожна з яких є складовими фрейму «Хвороба», а також окреслено слоти «Пошкодження організму», «Синдром», «Симптом хвороби» та «Вагітність» з яких побудовано фрейм «Хвороба». Також у статті проведено дослідження щодо частотності використання цього фрейму в українській мові. Ключові слова: метафора, політична метафора, концептуальне поле, концептосфера, фрейм, медицина, хвороба.