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Abstract Background The pharmaceutical industry is a cornerstone in the global healthcare system, renowned for its pioneering discoveries and unwavering commitment to addressing complex health challenges. Over decades, it has proven its resilience in ensuring the availability of life-saving treatments. However, this stalwart industry is not without its challenges, from high costs and intricate regulations to the complexities of intellectual property protection, drug pricing, and the ever-looming spectre of emerging health threats, (as witnessed in recent pandemics). Amidst these diverse challenges, technological advancements in Artificial Intelligence (AI) emerge as a beacon of hope, positioned to transform the industry. Study This article explores the transformative role of AI within the pharmaceutical industry, unravelling its multifaceted impact across crucial domains. From revolutionizing drug discovery and development to optimizing clinical trials, enabling personalized medicine, enhancing manufacturing and quality control, ensuring pharmacovigilance, streamlining supply chain management, and even influencing drug marketing and sales, AI’s influence is pervasive. The integration of AIs into the pharmaceutical industry signifies a transformative moment in the course of healthcare evolution. Conclusion In summary, as we navigate this era of unprecedented technological advancement, the pharmaceutical landscape is poised for a profound and drastic change, signalling a leap toward enhanced patient outcomes and a more resilient healthcare ecosystem. The integration of AI offers numerous benefits, including faster and more cost-effective drug discovery, enhanced precision in personalized medicine, improved clinical trials through better data analysis etc. However, AI also faces limitations such as the need for high-quality, unbiased datasets, regulatory and ethical concerns regarding transparency and data privacy, and the inherent complexity of biological systems that can sometimes outstrip AI’s current capabilities. In light of these opportunities and challenges, the future of AI in pharmaceuticals hinges on careful implementation, continuous innovation, ethical considerations and a collaborative approach to addresses these challenges.

Abstract This study examines higher education through data-mining methodologies, aiming to uncover key themes and sentiments in global discourse. Utilizing sentiment analysis and topic modeling, the research analyzes 157,943 tweets from 84,423 unique users over a five-month period (January to May 2023). This period was selected, coinciding with the rise of artificial intelligence (AI) tools, particularly ChatGPT. The study investigates the discussions, emotional tones, and dominant topics shaping the global narrative of higher education within X (Twitter) data. Key findings include the geographical distribution of tweets and the most frequent positive and negative perceptions. It also addresses critical issues such as affordability, accessibility, and funding in higher education. Furthermore, the data shows public reactions to AI in higher education are initially negative, while higher education tweets are primarily characterized by positivity and optimism. The higher education tweets are mainly posted on the weekend, with decreased activity during weekdays. This research provides insights into the evolving higher education landscape amid rapid technological advancements.

Abstract The high-cost pressure caused by the level of competition poses major challenges for breweries. While microbreweries can develop local strengths and brewery groups develop synergies, this does not represent a decisive improvement. The application of machine learning, on the other hand, could give breweries a significant advantage in their brewing process. Several approaches to the application of machine learning in the brewing process have already been proposed in the literature. To guide possible areas of applications and the respective available solution approaches to improve the brewing process based on machine learning, a systematic review of the application of machine learning in the brewing process is presented in this paper. In this systematic review, all potentially relevant publications were included at first. Subsequently, irrelative publications were filtered out by using a clustering approach. Afterward, the remaining 21 publications were analyzed and synthesized. Based on a developed framework considering the brewing process steps, areas of improvement, machine learning tasks, and machine learning algorithms, these publications were classified. Upon the classification, a descriptive analysis was performed to identify common approaches in the existing literature. One result was that research on artificial intelligence in brewing lags significantly behind the general trend of artificial intelligence research. Additionally, there is very limited research into the association between the recipe and the desired chemical properties of the beer. Furthermore, it was noticeable that machine learning tasks utilizing artificial neural networks or support vector machines were preferred over others.

Abstract Introduction This study assesses the effectiveness of AI tools, ChatGPT and Google Gemini, in educating the public about neurological conditions such as Bell’s palsy, Tetanus, and Headaches, and assess chatbot-generated patient guides based on readability and ease of understanding. Methodology In March 2024, the authors conducted a cross-sectional study to develop patient education brochures for Bell’s palsy, Tetanus vaccination, and Persistent Headache, leveraging AI models ChatGPT and Google Gemini. The brochures’ quality was assessed through readability, similarity, and a modified DISCERN score for reliability. Statistical analysis, performed in R software, compared responses from both AI models using unpaired T-tests. The correlation between ease score and reliability was explored using Pearson’s Coefficient of Correlation. Results The study revealed no significant variances in word count, sentence count, or average words per sentence between the two AI tools. However, materials generated by ChatGPT exhibited significantly higher ease scores, highlighting its proficiency in creating more understandable content (p

Abstract In the domain of Extended Reality (XR), particularly Virtual Reality (VR), extensive research has been devoted to harnessing this transformative technology in various real-world applications. However, a critical challenge that must be addressed before unleashing the full potential of XR in practical scenarios is to ensure robust security and safeguard user privacy. This paper presents a systematic survey of the utility of biometric characteristics applied in the XR environment. To this end, we present a comprehensive overview of the different types of biometric modalities used for authentication and representation of users in a virtual environment. We discuss different biometric vulnerability gateways in general XR systems for the first time in the literature along with taxonomy. A comprehensive discussion on generating and authenticating biometric-based photorealistic avatars in XR environments is presented with a stringent taxonomy. We also discuss the availability of different datasets that are widely employed in evaluating biometric authentication in XR environments together with performance evaluation metrics. Finally, we discuss the open challenges and potential future work that need to be addressed in the field of biometrics in XR.

Abstract The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5.

Abstract This paper argues that ethical AI cannot be fostered in a vacuum, challenging the perspective that AI ethics research should be isolated from technological advancements and industry collaborations. It refutes the argument presented by Gerdes (Discov Artif Intell. 2022;2(25)), which suggests that industry involvement inherently undermines the integrity of AI ethics research. Through an exploration of historical and contemporary examples of successful academia-industry collaborations, the paper advocates for a synergistic approach that harnesses industry resources and insights to advance ethical AI development. Emphasising the importance of diverse funding, the value of industry insights, and the impracticality of separating AI ethics from computer science, the paper contends that a collaborative, transparent, and inclusive model of AI ethics research is essential for developing practical, relevant, and ethically sound AI technologies aligned with societal values and norms.

Abstract Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm’s methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains.

Abstract Purpose Tuberculosis (TB) is a widespread infectious disease that requires early detection for effective treatment and control. This study aims to improve TB detection using cough audio analysis, comparing the performance of capsule networks to other deep learning models. Methods We used cough audio recordings from 1105 individuals with a new or worsening cough for at least two weeks, totaling 9772 recordings. These recordings were processed into spectral images, and HOG features were extracted. Various models, including Capsule Networks + FCNN, CNN, VGG16, and ResNet50 were trained and evaluated. Results Capsule Networks + FCNN achieved the best performance with an accuracy of 0.97, sensitivity of 0.98, specificity of 0.96, F1 score of 0.97, and precision of 0.97, outperforming other models. This attribute is due to the model’s ability to learn complex features from spectral images. Conclusions This study concludes that Capsule Networks are more effective than typical CNN-based models in diagnosing TB from cough audio. This suggests that advanced deep learning frameworks could significantly enhance TB screening accuracy, especially in resource-limited areas.

Abstract The complexity and diversity brought by the distributed architecture of the new generation electric information collection system have deepened the difficulty of constructing evaluation verification index systems and quality evaluation models. Moreover, the presence of differentiated components has made fair and scientific verification challenging. Therefore, leveraging graph neural networks and siamese networks, a integrated construction quality evaluation system based on comprehensive weighted assessment in multiple scenarios was developed. Firstly, a graph neural network was constructed based on terminal data of the branches electricity usage information collection system and the link topology structure. Subsequently, this network was deployed on the headquarters side to directly acquire terminal data and generate mirror network input from the branches data, enabling real-time acquisition of various system operational indicators. Finally, the similarity between the headquarters and branches data was calculated using siamese networks to compute accuracy compensation weights for checking and evaluating various indicators, thereby obtaining comprehensive weighted quality evaluation indicators of the branches new generation electric information collection system. We use three types of services including electricity data collection, load forecasting, and task scheduling as experimental scenarios. The results showed that the multidimensional comprehensive weighted quality assessment combined with accuracy compensation obtained from the siamese network resulted in business construction quality assessment values of 97.92%, 95.95%, and 99.96% in branch. This value is approximately equal to the quality evaluation value of manual work, so the method can effectively verify the construction quality of new systems in the branch.

Abstract Background Cervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for nearly 90% of cervical cancer deaths. While Western countries are leveraging the powers of artificial intelligence (AI) in the health sector, most countries in sub-Saharan Africa are still lagging. In Uganda, cytologists manually analyze Pap smear images for the detection of cervical cancer, a process that is highly subjective, slow, and tedious. Machine learning (ML) algorithms have been used in the automated classification of cervical cancer. However, most of the MLs have overfitting limitations which limits their deployment, especially in the health sector where accurate predictions are needed. Methods In this study, we propose two kernel-based algorithms for automated detection of cervical cancer. These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. The SVM model proposed uses an optimized radial basis kernel while the DGP model uses a hybrid kernel of periodic and local periodic kernel. Results Experimental results revealed accuracy of 100% and 99.48% for an optimized SVM model and DGP model respectively. Results on precision, recall, and F1 score were also reported. Conclusions The proposed models performed well on cervical cancer detection and classification, and therefore suitable for deployment. We plan to deploy our proposed models in a mobile application-based tool. The limitation of the study was the lack of access to high-performance computational resources.

Abstract Concerns have arisen regarding the potential negative impact of long-term ChatGPT use on creative thinking. This study investigates these effects using a one-group, pretest–posttest design involving 31 undergraduates over a 10-week intervention period. Participants used ChatGPT to complete academic assignments focused on designing teaching units on bacterial growth, chronic disease, and the human body. Creativity was assessed using the CREA-Creative Intelligence test, a valid and reliable instrument. Data were analyzed using both frequentist and Bayesian methods. The results showed that 25% of participants experienced decreased creativity, 21.43% remained unchanged, and 53.57% showed improvement. Overall, the study found no evidence that ChatGPT negatively impacts creative thinking.

Abstract The surge in 3D modelling has led to a pronounced research emphasis on the field of 3D shape retrieval. Numerous contemporary approaches have been put forth to tackle this intricate challenge. Nevertheless, effectively addressing the intricacies of cross‐modal 3D shape retrieval remains a formidable undertaking, owing to inherent modality‐based disparities. The authors present an innovative notion—termed “geometric words”—which functions as elemental constituents for representing entities through combinations. To establish the knowledge graph, the authors employ geometric words as nodes, connecting them via shape categories and geometry attributes. Subsequently, a unique graph embedding method for knowledge acquisition is devised. Finally, an effective similarity measure is introduced for retrieval purposes. Importantly, each 3D or 2D entity can anchor its geometric terms within the knowledge graph, thereby serving as a link between cross‐domain data. As a result, the authors’ approach facilitates multiple cross‐domain 3D shape retrieval tasks. The authors evaluate the proposed method's performance on the ModelNet40 and ShapeNetCore55 datasets, encompassing scenarios related to 3D shape retrieval and cross‐domain retrieval. Furthermore, the authors employ the established cross‐modal dataset (MI3DOR) to assess cross‐modal 3D shape retrieval. The resulting experimental outcomes, in conjunction with comparisons against state‐of‐the‐art techniques, clearly highlight the superiority of our approach.

Abstract Counting high‐density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence. Despite many advancements in this field, many of them are not widely known, especially in terms of research data. The authors proposed a three‐tier standardised dataset taxonomy (TSDT). The Taxonomy divides datasets into small‐scale, large‐scale and hyper‐scale, according to different application scenarios. This theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific fields. Additionally, the authors proposed a new evaluation index for the clarity of the dataset: average pixel occupied by each object (APO). This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image resolution. Moreover, the authors classified the crowd counting methods from a data‐driven perspective: multi‐scale networks, single‐column networks, multi‐column networks, multi‐task networks, attention networks and weak‐supervised networks and introduced the classic crowd counting methods of each class. The authors classified the existing 36 datasets according to the theory of three‐tier standardised dataset taxonomy and discussed and evaluated these datasets. The authors evaluated the performance of more than 100 methods in the past five years on different levels of popular datasets. Recently, progress in research on small‐scale datasets has slowed down. There are few new datasets and algorithms on small‐scale datasets. The studies focused on large or hyper‐scale datasets appear to be reaching a saturation point. The combined use of multiple approaches began to be a major research direction. The authors discussed the theoretical and practical challenges of crowd counting from the perspective of data, algorithms and computing resources. The field of crowd counting is moving towards combining multiple methods and requires fresh, targeted datasets. Despite advancements, the field still faces challenges such as handling real‐world scenarios and processing large crowds in real‐time. Researchers are exploring transfer learning to overcome the limitations of small datasets. The development of effective algorithms for crowd counting remains a challenging and important task in computer vision and AI, with many opportunities for future research.

Abstract Underwater object detection (UOD) is crucial for marine economic development, environmental protection, and the planet's sustainable development. The main challenges of this task arise from low‐contrast, small objects, and mimicry of aquatic organisms. The key to addressing these challenges is to focus the model on obtaining more discriminative information. The authors observe that the edges of underwater objects are highly unique and can be distinguished from low‐contrast or mimicry environments based on their edges. Motivated by this observation, an Edge‐guided Representation Learning Network, termed ERL‐Net is proposed, that aims to achieve discriminative representation learning and aggregation under the guidance of edge cues. Firstly, an edge‐guided attention module is introduced to model the explicit boundary information, which generates more discriminative features. Secondly, a hierarchical feature aggregation module is proposed to aggregate the multi‐scale discriminative features by regrouping them into three levels, effectively aggregating global and local information for locating and recognising underwater objects. Finally, a wide and asymmetric receptive field block is proposed to enable features to have a wider receptive field, allowing the model to focus on smaller object information. Comprehensive experiments on three challenging underwater datasets show that our method achieves superior performance on the UOD task.

Abstract Generating co‐speech gestures for interactive digital humans remains challenging because of the indeterministic nature of the problem. The authors observe that gestures generated from speech audio or text by existing neural methods often contain less movement shift than expected, which can be viewed as slow or dull. Thus, a new generative model coupled with memory networks as dynamic dictionaries for speech‐driven gesture generation with improved diversity is proposed. More specifically, the dictionary network dynamically stores connections between text and pose features in a list of key‐value pairs as the memory for the pose generation network to look up; the pose generation network then merges the matching pose features and input audio features for generating the final pose sequences. To make the improvements more accurately measurable, a new objective evaluation metric for gesture diversity that can remove the influence of low‐quality motions is also proposed and tested. Quantitative and qualitative experiments demonstrate that the proposed architecture succeeds in generating gestures with improved diversity.

Abstract Human‐machine cooperative control has become an important area of intelligent driving, where driver intention recognition and dynamic control authority allocation are key factors for improving the performance of cooperative decision‐making and control. In this paper, an online learning method is proposed for human‐machine cooperative control, which introduces a priority control parameter in the reward function to achieve optimal allocation of control authority under different driver intentions and driving safety conditions. Firstly, a two‐layer LSTM‐based sequence prediction algorithm is proposed to recognise the driver's lane change (LC) intention for human‐machine cooperative steering control. Secondly, an online reinforcement learning method is developed for optimising the steering authority to reduce driver workload and improve driving safety. The driver‐in‐the‐loop simulation results show that our method can accurately predict the driver's LC intention in cooperative driving and effectively compensate for the driver's non‐optimal driving actions. The experimental results on a real intelligent vehicle further demonstrate the online optimisation capability of the proposed RL‐based control authority allocation algorithm and its effectiveness in improving driving safety.

Abstract The Equilibrium Optimiser (EO) has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems. Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research. To alleviate these limitations, an EO variant named adaptive elite‐guided Equilibrium Optimiser (AEEO) is introduced. Specifically, the adaptive elite‐guided search mechanism enhances the balance between exploration and exploitation. The modified mutualism phase reinforces the information interaction among particles and local optima avoidance. The cooperation of these two mechanisms boosts the overall performance of the basic EO. The AEEO is subjected to competitive experiments with state‐of‐the‐art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite. Experimental results demonstrate that AEEO outperforms several well‐performing EO variants, DE variants, PSO variants, SSA variants, and GWO variants in terms of convergence speed and accuracy. In addition, the AEEO algorithm is used for the edge server (ES) placement problem in mobile edge computing (MEC) environments. The experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

Abstract Image clustering has received significant attention due to the growing importance of image recognition. Researchers have explored Riemannian manifold clustering, which is capable of capturing the non‐linear shapes found in real‐world datasets. However, the complexity of image data poses substantial challenges for modelling and feature extraction. Traditional methods such as covariance matrices and linear subspace have shown promise in image modelling, and they are still in their early stages and suffer from certain limitations. However, these include the uncertainty of representing data using only one Riemannian manifold, limited feature extraction capacity of single kernel functions, and resulting incomplete data representation and redundancy. To overcome these limitations, the authors propose a novel approach called join multiple Riemannian manifold representation and multi‐kernel non‐redundancy for image clustering (MRMNR‐MKC). It combines covariance matrices with linear subspace to represent data and applies multiple kernel functions to map the non‐linear structural data into a reproducing kernel Hilbert space, enabling linear model analysis for image clustering. Additionally, the authors use matrix‐induced regularisation to improve the clustering kernel selection process by reducing redundancy and assigning lower weights to identical kernels. Finally, the authors also conducted numerous experiments to evaluate the performance of our approach, confirming its superiority to state‐of‐the‐art methods on three benchmark datasets.

Abstract Continuous emotion recognition is to predict emotion states through affective information and more focus on the continuous variation of emotion. Fusion of electroencephalography (EEG) and facial expressions videos has been used in this field, while there are with some limitations in current researches, such as hand‐engineered features, simple approaches to integration. Hence, a new continuous emotion recognition model is proposed based on the fusion of EEG and facial expressions videos named residual multimodal Transformer (RMMT). Firstly, the Resnet50 and temporal convolutional network (TCN) are utilised to extract spatiotemporal features from videos, and the TCN is also applied to process the computed EEG frequency power to acquire spatiotemporal features of EEG. Then, a multimodal Transformer is used to fuse the spatiotemporal features from the two modalities. Furthermore, a residual connection is introduced to fuse shallow features with deep features which is verified to be effective for continuous emotion recognition through experiments. Inspired by knowledge distillation, the authors incorporate feature‐level loss into the loss function to further enhance the network performance. Experimental results show that the RMMT reaches a superior performance over other methods for the MAHNOB‐HCI dataset. Ablation studies on the residual connection and loss function in the RMMT demonstrate that both of them is functional.

Abstract Vertical federated learning (VFL) can learn a common machine learning model over vertically partitioned datasets. However, VFL are faced with these thorny problems: (1) both the training and prediction are very vulnerable to stragglers; (2) most VFL methods can only support a specific machine learning model. Suppose that VFL incorporates the features of centralised learning, then the above issues can be alleviated. With that in mind, this paper proposes a new VFL scheme, called FedBoost, which makes private parties upload the compressed partial order relations to the honest but curious server before training and prediction. The server can build a machine learning model and predict samples on the union of coded data. The theoretical analysis indicates that the absence of any private party will not affect the training and prediction as long as a round of communication is achieved. Our scheme can support canonical tree‐based models such as Tree Boosting methods and Random Forests. The experimental results also demonstrate the availability of our scheme.

Abstract Support vector machine (SVM) is a binary classifier widely used in machine learning. However, neglecting the latent data structure in previous SVM can limit the performance of SVM and its extensions. To address this issue, the authors propose a novel SVM with discriminative low‐rank embedding (LRSVM) that finds a discriminative latent low‐rank subspace more suitable for SVM classification. The extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies. A detailed derivation of the authors’ iterative algorithms are given that is essentially for solving the SVM on the low‐rank subspace. Additionally, some theorems and properties of the proposed models are presented by the authors. It is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis (LDA) problems. This indicates that the projection subspaces obtained by the authors’ algorithms are more suitable for SVM classification compared to those from the LDA method. The convergence analysis for the authors proposed algorithms are also provided. Furthermore, the authors conduct experiments on various machine learning data sets to evaluate the algorithms. The experiment results show that the authors’ algorithms perform significantly better than other algorithms, which indicates their superior abilities on classification tasks.

Abstract Drug‐drug interaction (DDI) prediction is a crucial issue in molecular biology. Traditional methods of observing drug‐drug interactions through medical experiments require significant resources and labour. The authors present a Medical Knowledge Graph Question Answering (MedKGQA) model, dubbed MedKGQA, that predicts DDI by employing machine reading comprehension (MRC) from closed‐domain literature and constructing a knowledge graph of “drug‐protein” triplets from open‐domain documents. The model vectorises the drug‐protein target attributes in the graph using entity embeddings and establishes directed connections between drug and protein entities based on the metabolic interaction pathways of protein targets in the human body. This aligns multiple external knowledge and applies it to learn the graph neural network. Without bells and whistles, the proposed model achieved a 4.5% improvement in terms of DDI prediction accuracy compared to previous state‐of‐the‐art models on the QAngaroo MedHop dataset. Experimental results demonstrate the efficiency and effectiveness of the model and verify the feasibility of integrating external knowledge in MRC tasks.

Abstract One of the most widely used fuzzy linear programming models is the multi‐objective interval type‐2 fuzzy linear programming (IT2FLP) model, which is of particular importance due to the simultaneous integration of multiple criteria and objectives in a single problem, the fuzzy nature of this type of problems, and thus, its closer similarity to real‐world problems. So far, many studies have been done for the IT2FLP problem with uncertainties of the vagueness type. However, not enough studies have been done regarding the multi‐objective interval type‐2 fuzzy linear programming (MOIT2FLP) problem with uncertainties of the vagueness type. As an innovation, this study investigates the MOIT2FLP problem with vagueness‐type uncertainties, which are represented by membership functions (MFs) in the problem. Depending on the localisation of vagueness in the problem, that is, vagueness in the objective function vector, vagueness in the technological coefficients, vagueness in the resources vector, and any possible combination of them, various problems may arise. Furthermore, to solve problems with MOIT2FLP, first, using the weighted sum method as an efficient and effective method, each of the MOIT2FLP problems is converted into a single‐objective problem. In this research, these types of problems are introduced, their MFs are stated, and different solution methods are suggested. For each of the proposed methods, the authors have provided an example and presented the results in the corresponding tables.

Abstract Pre‐trained multilingual language models (PMLMs) such as mBERT and XLM‐R have shown good cross‐lingual transferability. However, they are not specifically trained to capture cross‐lingual signals concerning sentiment words. This poses a disadvantage for low‐resource languages (LRLs) that are under‐represented in these models. To better fine‐tune these models for sentiment classification in LRLs, a novel intermediate task fine‐tuning (ITFT) technique based on a sentiment lexicon of a high‐resource language (HRL) is introduced. The authors experiment with LRLs Sinhala, Tamil and Bengali for a 3‐class sentiment classification task and show that this method outperforms vanilla fine‐tuning of the PMLM. It also outperforms or is on‐par with basic ITFT that relies on an HRL sentiment classification dataset.

Abstract Image classification algorithms are commonly based on the Independent and Identically Distribution (i.i.d.) assumption, but in practice, the Out‐Of‐Distribution (OOD) problem widely exists, that is, the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the i.i.d. assumption are limiting generalisation. Causal inference is an important method to learn the causal associations which are invariant across different environments, thus improving the generalisation ability of the model. However, existing methods usually require partitioning of the environment to learn invariant features, which mostly have imbalance problems due to the lack of constraints. In this paper, we propose a balanced causal learning framework (BCL), starting from how to divide the dataset in a balanced way and the balance of training after the division, which automatically generates fine‐grained balanced data partitions in an unsupervised manner and balances the training difficulty of different classes, thereby enhancing the generalisation ability of models in different environments. Experiments on the OOD datasets NICO and NICO++ demonstrate that BCL achieves stable predictions on OOD data, and we also find that models using BCL focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalisation ability.

Abstract Recovering 3D human meshes from monocular images is an inherently ill‐posed and challenging task due to depth ambiguity, joint occlusion, and truncation. However, most existing approaches do not model such uncertainties, typically yielding a single reconstruction for one input. In contrast, the ambiguity of the reconstruction is embraced and the problem is considered as an inverse problem for which multiple feasible solutions exist. To address these issues, the authors propose a multi‐hypothesis approach, multi‐hypothesis human mesh recovery (MH‐HMR), to efficiently model the multi‐hypothesis representation and build strong relationships among the hypothetical features. Specifically, the task is decomposed into three stages: (1) generating a reasonable set of initial recovery results (i.e., multiple hypotheses) given a single colour image; (2) modelling intra‐hypothesis refinement to enhance every single‐hypothesis feature; and (3) establishing inter‐hypothesis communication and regressing the final human meshes. Meanwhile, the authors take further advantage of multiple hypotheses and the recovery process to achieve human mesh recovery from multiple uncalibrated views. Compared with state‐of‐the‐art methods, the MH‐HMR approach achieves superior performance and recovers more accurate human meshes on challenging benchmark datasets, such as Human3.6M and 3DPW, while demonstrating the effectiveness across a variety of settings. The code will be publicly available at https://cic.tju.edu.cn/faculty/likun/projects/MH‐HMR.

Abstract Hardware‐based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with R2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate‐based optimisation will provide a platform for plant‐level implementation to realise the concept of industry 4.0 in the refinery.

Abstract Although deep convolution neural network (DCNN) has achieved great success in computer vision field, such models are considered to lack interpretability in decision‐making. One of fundamental issues is that its decision mechanism is considered to be a “black‐box” operation. The authors design the binary tree structure convolution (BTSC) module and control the activation level of particular neurons to build the interpretable DCNN model. First, the authors design a BTSC module, in which each parent node generates two independent child layers, and then integrate them into a normal DCNN model. The main advantages of the BTSC are as follows: 1) child nodes of the different parent nodes do not interfere with each other; 2) parent and child nodes can inherit knowledge. Second, considering the activation level of neurons, the authors design an information coding objective to guide neural nodes to learn the particular information coding that is expected. Through the experiments, the authors can verify that: 1) the decision‐making made by both the ResNet and DenseNet models can be explained well based on the "decision information flow path" (known as the decision‐path) formed in the BTSC module; 2) the decision‐path can reasonably interpret the decision reversal mechanism (Robustness mechanism) of the DCNN model; 3) the credibility of decision‐making can be measured by the matching degree between the actual and expected decision‐path.

Abstract Exploiting the hierarchical dependence behind user behaviour is critical for click‐through rate (CRT) prediction in recommender systems. Existing methods apply attention mechanisms to obtain the weights of items; however, the authors argue that deterministic attention mechanisms cannot capture the hierarchical dependence between user behaviours because they treat each user behaviour as an independent individual and cannot accurately express users' flexible and changeable interests. To tackle this issue, the authors introduce the Bayesian attention to the CTR prediction model, which treats attention weights as data‐dependent local random variables and learns their distribution by approximating their posterior distribution. Specifically, the prior knowledge is constructed into the attention weight distribution, and then the posterior inference is utilised to capture the implicit and flexible user intentions. Extensive experiments on public datasets demonstrate that our algorithm outperforms state‐of‐the‐art algorithms. Empirical evidence shows that random attention weights can predict user intentions better than deterministic ones.

Abstract Background Artificial intelligence (AI) uses in healthcare changed dramatically with large language models. While work is underway to integrate generative AI safely and effectively into medical practice, little is known about how resident physicians are already using these tools in their work. Objectives To characterize the current usage patterns and attitudes towards large language models among internal medicine residents in North Carolina. Methods Cross-sectional survey-based study of Internal medicine residents in North Carolina. Results 152 of 523 total residents completed the survey (29% response rate). Many identified a role for LLMs in both clinical (56%) and non-clinical medicine (72%) today, although only 26% reported current use. More perceived a future role in clinical (71%) and non-clinical (80%) medicine. None had received formal training, but 94% expressed interest in learning more and 87% thought residencies maybe or definitely should have formal curriculum. Top clinical uses include forming a differential diagnosis, researching treatments, and creating educational materials. Top non-clinical uses included self-directed learning, scientific writing, and summarizing literature. Conclusions Medical educators and administrators should be aware that residents are using generative AI in professional settings. Residents believe in a current and future role for AI in medicine and desire curriculum and learning opportunities during training.

Abstract Medical images often require segmenting into different regions in the first analysis stage. Relevant features are selected to differentiate various regions from each other, and the images are segmented into meaningful (anatomically significant) regions based on these features. The purpose of this study is to present a model for segmenting and identifying the local tumor formation in MR images of the human brain. The proposed system operates in an unsupervised manner to minimize the intervention of expert users and to achieve an acceptable speed in the tumor classification process. The proposed method includes several steps of preprocessing for different brain image classify that Perform the normalization task. These preprocessing steps lead to more accurate results in high-resolution images and ultimately improve the accuracy and sensitivity of tumor separation from brain tissue. The output of this stage is applied to a self-encoding neural network for image zoning. By nature of self-encoding networks, leads to reduce the dimensionality of tumor pixels from the surrounding healthy environment, which significantly helps remove regions incorrectly extracted as tumors. Finally, by extracting features from the previous stage's output through Otsu thresholding, the surrounding area and type of tumor are also extracted. The proposed method was trained and tested using the BRATS2020 database and evaluated by various performance metrics. The results based on the Dice Similarity Coefficient (DSC) show an accuracy of 97% for the entire MR image and improved detection accuracy compared to other methods, as well as a reduction in the cost of the diagnostic process.

Abstract With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.

Abstract The rapid expansion of artificial intelligence (AI) across various fields has significantly impacted how individuals construct and present their identities, particularly on social media. This brief communication explains the concept of digital twinning within the public sphere, where AI-generated images create virtual twins. We examine the phenomenon of attachment to AI-generated self-image, proposing that a perceived sense of emptiness predicts attachment to AI-generated self-image. Using the empty self-theory as a theoretical foundation, we argue that individuals experiencing feelings of emptiness are more likely to form deep emotional bonds with their AI-generated self-image. They may find solace by associating their sense of emptiness with AI-generated self-images, similar to idealised images in advertising. This attachment can manifest as either subjective, where users believe the AI-generated image truly represents them, or objective, where the image is seen as a possession. In future research, it would be valuable to investigate the various factors that contribute to an individual's inclination to form an attachment to AI-generated self-images. Deep exploration of the difference between objective and subjective attachment and quantifying this proposed relationship through empirical studies could provide valuable insights into the interplay between technology and human psychology.

Abstract Quantum technologies (QTs) hold the promise to transform a wide range of industries, such as computing, communications, finance, healthcare, defense, space, and beyond. Of the various QTs, the most relevant is presently quantum computing (QC), of significant projected market potential, with some estimates forecasting it to reach many billion dollars over the next few years. There are, however, risks to factor, as highlighted in this perspective, the most relevant technical, economic, and societal risks. Those financial and societal are projected to become increasingly relevant phased with the progressing solution of the technical issues. The synergy with artificial intelligence comes with further opportunities as development can be faster and more effective, but also increases risks. AI presents numerous opportunities, but it also comes with several risks and challenges. Some of the major risks associated with AI include bias and fairness, transparency and explainability, job displacement, security concerns, ethical concerns, privacy issues, lack of regulation and standards, and exponential growth and unintended consequences. Balancing the advantages of AI-driven quantum technologies with associated risks presents a significant challenge. It necessitates careful consideration of ethical, economic, and technical aspects to ensure that these technologies are developed and deployed in a manner that is beneficial and equitable for everyone. Graphical Abstract

Abstract Prostate segmentation is a substantial factor in the diagnostic pathway of suspicious prostate lesions. Medical doctors are assisted by computer-aided detection and diagnosis systems systems and methods derived from artificial intelligence deep learning-based systems have to be trained on existing data. Especially freely available labeled prostate magnetic resonance imaging data is rare. With regard to this problem, we show a method to combine two existing small datasets to form a bigger one. We present a data processing pipeline consisting of a cascaded network architecture that is able to perform multi-label semantic segmentation, hence, capable of classifying each pixel in a $$\textrm{T}_{2}$$ T 2 -weighted image not only to one class but to a subset of the prostate zones and classes of interest. This delivers richer information such as overlapping zones that are key to medical radiological examination. Additionally, we describe how to integrate expert knowledge in our deep learning system. To increase data variety for training and evaluation we use image augmentation on our two datasets—a freely available dataset and our new open-source dataset. To combine the datasets we denoise the contourings in our dataset by using an effective yet simple algorithm based on standard computer vision methods only. The performance of the presented methodology is compared and evaluated using the dice score metric and 5-fold cross-validation on all datasets. Although we trained on tiny datasets our method achieves excellent segmentation quality and is even able to detect prostate cancer. Our method to combine the two datasets reduces segmentation errors and increases data variety. The proposed architecture significantly improves performance by including expert knowledge via feature-map concatenation. On the initiative for collaborative computer vision benchmarking dataset we achieve on average dice scores of approximately 91% for the whole prostate gland, 67% for the peripheral zone and 75% for the prostate central gland. We find that image augmentation except contrast limited adaptive histogram equalisation did not have much influence on the segmentation quality. Derived and enhanced from existing methods we present an approach that is able to deliver multi-label semantic segmentation results for prostate magnetic resonance imaging. This approach is simple and could be applied to other applications of deep learning as well. It improves the segmentation results by a large margin. Once tweaked to the data, our denoising and combination algorithm delivers robust and accurate results even on data with segmentation errors.

ABSTRACT: Female leadership and its impact on organizational management have garnered increasing interest over the decades. With the evolution of workplace environments and societal expectations, research increasingly focuses on the traits that characterize women leaders and their management styles. Empathy is a critical element among these traits, often associated with more inclusive and people-oriented leadership approaches. However, despite growing recognition of the importance of empathy in management, few studies have specifically examined the profile of female managers who actively integrate empathy into their management style. This research gap creates a lack of thorough understanding of how empathy influences the management practices of women leaders. Our study aims to address this gap by closely examining the profile of female managers who demonstrate a high level of empathy in their management approach. Previous research on female leadership has often addressed general aspects of women's management styles, but few have specifically examined the role of empathy. Additionally, some studies have been limited by small sample sizes or weak methodologies, hindering the generalization of results. Furthermore, the specific impact of empathy in the management of women leaders is often underestimated or misunderstood. Our study contributes significantly to the literature by identifying and analyzing the characteristics of female managers who actively integrate empathy into their management style. We can provide more profound and generalizable insights into this issue by using rigorous methodology and expanding our sample to include diversity across sectors and hierarchical levels. Our results highlight several key aspects of the profile of empathic female managers, including their ability to create inclusive work environments, foster collaboration, and promote employee well-being. Additionally, we identify specific strategies these managers use to integrate empathy into their decision-making and problem-solving. Ultimately, our article offers valuable insights for practitioners and researchers interested in developing female leadership and enhancing organizational management practices. Keywords: Female leadership; Empathy; Organizational management; Team dynamics; Organizational performance.

Résumé : Le tabagisme a de plus en plus un visage féminin en Côte d’ivoire. A côté des stéréotypes qui accompagnent la fumeuse considérée comme une femme de mauvaise moralité, il semble que le tabagisme sous toutes ses formes, entraine de plus en plus de femmes dans une trajectoire de toxicomanie aux drogues illicites. L’objectif de cette étude est d’identifier les facteurs d’initiation, de maintien et de développement d’une dépendance à certaines drogues chez des femmes usagères de tabac. L’étude s’inscrit dans une démarche compréhensive des significations et des finalités de l’action de l’usage de tabac chez les femmes au-delà des approches épidémiologiques. L’enquête s’est déroulée à Abidjan et a concerné 92 femmes fumeuses de tabac et usagères de drogues illicites, depuis au moins un an avant l’enquête. Elles ont été sélectionnées à partir d’un choix raisonné et de la boule de neige. La collecte des données s’est effectuée à partir d’un d’entretien semi directif et d’une observation non participante. Les pratiques de consommations déclarées ont été saisies grâce à la mesure de la fréquence, du mode d’usage et du type de produits utilisés. Les résultats montrent que ces femmes connaissent une trajectoire de consommation de drogues illicites qui n’est pas toujours linéaire et successive. Les produits consommés concomitamment au tabac sont le cannabis, l’héroïne, la cocaïne et les psychotropes (stimulants et dépresseurs). Les consommations de drogues reposent sur des aspects symboliques variables, construites en fonction des rapports aux produits consommés, des risques qu’elles représentent et des effets attendus par chaque usagère. De telles pratiques répondent au besoin de potentialiser les effets dépendogènes des produits du tabac à travers des substances illicites d’une part et de résister à la stigmatisation et au rejet social lié au tabagisme d’autre part. Mots-clés : Tabagisme féminin - drogues dures- significations - stigmatisation

Résumé : Cet article vise à explorer les dynamiques du commerce transfrontalier entre le Tchad et le Cameroun depuis l'inauguration du Pont Nguéli en 1985. Il relie N'Djamena (Tchad) à Kousséri (Cameroun), et constitue un axe stratégique pour les échanges commerciaux entre les deux pays, en particulier pour les importations du Tchad qui passent par le Port de Douala au Cameroun. Peu d'études approfondies existent sur son efficacité réelle et crucial dans l’économie de ces deux pays d’Afrique centrale. Ce Pont facilite non seulement les échanges formels, mais il est aussi un point de passage pour de nombreux commerces informels, qui constituent une part importante de l'économie locale. Les échanges étaient limités par des infrastructures sous-développées et des conflits internes au Tchad. La construction du Pont Nguéli en 1985, a progressivement augmenté les échanges, entre les deux pays, avec une diversification des produits échangés (bétail, produits agricoles, et biens manufacturés). Cette étude s’appuie sur les techniques documentaires collectées dans les différents centres de recherche, sur les œuvres écrites, orales ainsi qu’à travers les différents entretiens. Il en ressort que le Pont Nguéli joue un rôle clé dans l'intégration économique entre le Tchad et le Cameroun, mais la nécessité d'améliorer la sécurité et la fluidité des procédures douanières pourrait maximiser les bénéfices économiques pour les deux pays. Mots clés : Dynamique, Commerce-informel, Tchad, Cameroun, Pont Nguéli,

Résumé : La problématique relative à l’existence d’une presse libre au Congo Démocratique suscite encore des questions sans réponses appropriées dans les clubs des hommes de la presse. Allons-nous, dans cet article, essayer de relever les phénomènes qui torpillent toute tentative amorcée dans ce but ? En effet, nul n’ignore le rôle que peuvent jouer les médias dans le développement socioculturel d’un pays. Le système global congolais de l’information et de la communication quant à lui reste par le pouvoir. Certains observateurs le qualifient de courant Sakombiste (ancien ministre du régime Mobutu). Car, au départ, cette effervescence de libéraliser le secteur des médias en République Démocratique du Congo, avait des repères aux premiers pas posés par les idéologies des pères de l’indépendance dont la presse a fait son travail d’éveil patriotique. La vision du feu Mobutu et l’influence diplomatique des étrangers à l’époque a plongé les professionnels des médias dans une situation contraste devant défendre l’éthique et la déontologie de leur métier. Il sied de noter que, le manque de bonne politique de gestion du secteur médiatique après la libéralisation nous pousse d’avoir une idée particulière, d’étaler les qualités d’un journaliste professionnel et celui qui exerce de l’amateurisme organisé, bien que les origines du journalisme font montre que ce métier était tout simplement de colporter les nouvelles parfois qui n’ont pas des sources certaines ajoutent les hommes de lettres de l’époque. Mais avant cela, nous faisons un bref aperçu historique de cet événement afin parler quelques réalités qui ont amené la confusion dans le secteur de média de masse congolais. Mots-clés : journaliste professionnel, mouton noir, journaliste amateur.

Résumé : L’objet de notre travail de recherche est de démontrer comment les métiers innovants de la littérature orale peuvent contribuer à booster le développement d’un pays dans le contexte actuel de la culturalité allogène de l’Afrique. Tributaire de la tradition, la littérature orale africaine a pour fondement initial l'art oratoire (la parole ou le verbe) dans toutes ses formes esthétiques et éthiques. Riche de sens et de profondeur, elle nous apparaît comme un corps de métiers qui participe au progrès multiforme d’un pays. Sur ce, dans quelle mesure les métiers de ladite littérature peuvent-ils contribuer au développement d’un pays dans le contexte de sa mise en perspective ? Quels sont les enjeux que constituent ces métiers innovants dans une société en pleine mutation ? Telles sont les questions qui constitueront l’ossature de notre réflexion. À cet effet, dans le cadre de cette étude, nous employons la méthode éclectique. Premièrement, il s’agira des Cultural Studies d’Armand Mattelart et d’Erik Neveu (2003), une approche théorique qui permet de revaloriser les cultures africaines et enfin, l’épistémocritique de Michel Pierssens (1990) afin de déconstruire tous clichés réductionnistes à propos des métiers de la littérature orale africaine dans leur rôle de contributeur au développement d’un pays. Mots-clés : littérature orale, développement, métiers oratoires, genre oral,

Abstract : Proverbs play a crucial role in enhancing cultural understanding and communication, particularly in the context of language learning. This paper explores the significance of French proverbs in the teaching and learning of French at Barekese Senior High School, Ghana. Proverbs, being deeply rooted in culture, provide insights into values, beliefs, and social attitudes, making language learning more relatable and engaging. French is rich in proverbs, and incorporating them into lessons can simplify complex linguistic concepts while fostering a deeper understanding of both language and culture. The study examines how French proverbs can improve students' communication skills, enrich their vocabulary, and enhance their oral competence. By engaging students in creative activities using proverbs, the learning experience becomes more interactive and enjoyable. The research involved 125 students and four French teachers, utilizing questionnaires, interviews, and lesson observations to gather data. Findings indicate that French teachers seldom use proverbs in class, despite their potential to make lessons more effective and interesting. The study suggests integrating French proverbs into the curriculum to facilitate better learning outcomes. Teachers are encouraged to employ proverbs in diverse classroom activities, such as grouping proverbs by theme and using them to teach grammar. The inclusion of French proverbs not only promotes cultural awareness but also makes language lessons more accessible and enjoyable for students. Keywords: Facilitate, Teaching, Learning, French Language, Proverbs

Abstract : This study investigates the impact of multilingualism on language proficiency and cognitive development among fifth-grade pupils in Algerian primary schools. Utilizing a mixed-method research design, the study integrates quantitative data from teacher questionnaires and pupil language proficiency tests with qualitative data from classroom observations. The sample comprised 326 pupils and 45 teachers from primary schools in Biskra, Batna, and El Oued, selected through convenience sampling. The findings reveal that multilingual education significantly enhances pupils' linguistic and cognitive abilities, with notable improvements in vocabulary acquisition, grammar structure, and pronunciation across Arabic, French, and English. Classroom observations highlighted the positive influence of multilingualism on cognitive skills such as problem-solving, critical thinking, and cognitive flexibility. Teacher attitudes toward multilingual education were largely positive, emphasizing the benefits for linguistic development and cognitive growth, although challenges related to resources and training were noted. Recommendations include providing teachers with training in multilingual pedagogy, ensuring access to multilingual resources, and developing comprehensive language proficiency assessments. Keywords: Multilingualism; language proficiency; cognitive development; Algerian primary schools; teacher attitudes; mixed-method research.

Résumé : Cette étude a pour objectif de montrer la relation entre les revenus des ménages et la gestion des déchets et dégager les risques environnementaux qui en découlent. Une enquête transversale descriptive a été réalisée pour évaluer le lien entre les pratiques de gestion des déchets ménagers en fonction des revenus des ménages dans le 7ème arrondissement de la ville de N’Djamena. 1000 ménages ont été choisis dans dix quartiers de la commune. Les coordonnées géographiques des déchets à proximité des habitations ont été relevées. L’analyse statistique descriptive a été appliquée aux données. Les résultats montrent que 49,7% des enquêtés ont le niveau d’étude secondaire, 43,9% le niveau supérieur, 2,9% le niveau primaire et 3,1% sont non instruit. Et que les enfants (83,4%) et/ou les femmes (16,6%) sont chargés de la gestion des déchets. 31,6% des ménages déposent leurs ordures en bordure de rues, 17,8 % dans les marécageuses, certains 51,6 % dans les cours des écoles publiques et ou les dépotoirs sauvages. La pré-collecte est utilisée mais par les ménages ayant des revenus supérieurs à 200000 FCFA, tels qu’Amtoukoi (41,38%), Chagoua (54,6), Dembé (56,96 %), Habena (48,3) et Gassi (39,54%). Le stockage des ordures avant dépôt dans les sites, se fait dans les sceaux (83.1 %), les bacs à ordures (6,5 %), dans les barils (6,3%) en fonction des revenus des ménages. Mots-clés : Economie, Déchets, Environnement, Gestion, N’Djamena, Tchad.

Abstract : Autism is a communication disorder characterized by linguistic and visual communication difficulties. Autistic children often struggle with expressive communication, attention deficits, and problems storing and retrieving information, impacting their ability to remember and focus. To address these challenges, parents and teachers must leverage scientific advancements. Mind maps can activate the visual memory of autistic children, enhancing their learning and communication skills. Using a descriptive approach with explanation and analysis mechanisms, this study explored how mental maps, aligned with brain function, can improve visual communication and focus in autistic children. Examples based on mind maps were created to aid learning and consolidate information. The findings emphasize that learning is a visual, auditory, motor, and voluntary process, supported by mind maps that enhance recall and communication. We recommend specialized training courses for teachers and parents to utilize these technological tools effectively. Keywords: Autism, Attention, Visual Memory, Visual Communication, Mind Maps.

Résumé : Cet article se propose d’analyser de l’introduction du hamallisme et son enracinement en Côte d’Ivoire. Pour ce faire, il aborde dans une première partie les débuts du hamallisme en mettant relief le contexte de la politique de l’administration coloniale marquée par la surveillance des personnalités musulmanes. Le guide hamalliste et son proche collaborateur furent les frais des mesures administratives. En effet suite aux difficultés avec l’administration coloniale, Cheickh Hamahoullah et Yacouba Sylla sont internés en Côte d’Ivoire. Dans une deuxième partie, il analyse la renaissance du hamallisme autour de zawiyya fondées par Yacouba Sylla et Sanoussi Diaby respectivement dans les localités de Gagnoa et de Daloa. Enfin, l’article analyse les activités économiques et politiques des hamallistes lesquelles donnèrent plus de visibilités à la tijania à onze grains. Mots-clés : hamallisme, marabout, tijania, islam, zawiyya

Résumé : L’interaction entre l’homme et la machine a ouvert à la création artistique de nouvelles formes d’expériences théâtrales tendant à redéfinir ou à restructurer le rapport entre le spectacle et le public. Le théâtre ivoirien, qui depuis quelques années sombrait dans une certaine létargie, semble retrouver un nouveau printemps grâce aux potentialités offertes par le digital et par la pratique de la web-comédie. Au-delà du fait de diffuser des scènes vidéos sur leur page Facebook, Séry et Séry apparaissent comme les précurseurs d’un genre théâtral numérique en vogue en Côte d’Ivoire. Cet article vise à explorer les contours cuturels, esthétiques et épistémiques de cet art numérique qui oscille entre le théâtre et le cinéma. Mots-clés: web-comédie, digital, théâtre ivoirien

Résumé: Notre contribution s’inscrit dans le domaine de la pédagogie numérique. Nous voudrions soulever le sujet d’une nouvelle forme d’apprentissage dans les universités algériennes communément appelée E-learning qui signifie un mode d’enseignement en ligne. Nous avons émis la problématique suivante : quelles compétences devraient avoir les enseignants universitaires du FLE pour dispenser un cours à distance via une plateforme numérique. Dans le but de mener notre réflexion, nous avons opté pour un travail touchant à la contribution théorique et fondamentale que nous avons essayé d’améliorer la façon de transférer les connaissances aux étudiants de manière médiatisée et d’assurer la continuité pédagogique. La finalité de cette recherche est de moderniser les méthodes d'enseignement et d'apprentissage et les compétences des enseignants tout en proposant un contenu interactif et dynamique et d'améliorer la qualité de l’enseignement en ligne. Mots-clés : E-learning, formation en ligne, plateforme numérique, la pédagogie, les nouvelles technologies.

Abstract :The cessation of Internet services for only a few hours creates clear confusion, and members of society become living in a state of “social alienation.” This indicates the depth of the psychological and social integration of individuals with this virtual network, which has drawn the attention of many researchers and those interested in researching the phenomenon and revealing its social, political, and media dimensions. The use of social media is no longer limited to urban communities only, but has become widespread even in remote areas with a rural Bedouin character. Our approach to this phenomenon, in its various dimensions, comes as a serious attempt to examine the functional relationship between the status of the virtual world and its outcomes in reshaping the Algerian social reality, by presenting a general problem that states:How did social media contribute to reproducing the representations of members of the Algerian tribal community towards their social reality? Keywords : Social media, Social change, Nomadism, The tribe, Identity.