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Comparative study of motivational drivers behind players’ selection of Palworld
No full textopen access articlePalworld, released in January 2024, rapidly sold over 12 million copies on Steam in ten days, prompting comparisons to the Pokémon franchise due to its creature-taming aesthetic. This study investigates whether Palworld’s commercial and critical success stems from superficial homage or from deeper motivational affordances that resonate with an aging fan base. Drawing on Self-Determination Theory (SDT), we conducted a comprehensive analysis comprising a comparative feature review of Palworld versus mainline Pokémon and a survey of 322 Chinese Palworld players (252 of whom also had Pokémon experience). Results show that, while both games satisfy competence needs, players report significantly higher autonomy and relatedness in Palworld, citing its open-ended base-building, combat freedom, and cooperative multiplayer systems as key differentiators. Quantitative analyses confirmed that primary motives for playing Palworld–such as relaxation, achievement, and socialization–were rated higher than for Pokémon, especially in adult groups. We conclude that Palworld’s success is not merely due to its visual style but is rooted in its reinterpretation of the creature-taming genre through mature gameplay systems that address an aging fan base’s desire for creative freedom and deeper social engagement. The study provides a valuable data survey to support the field of game psychology
FELIDS: Federated Learning-based Intrusion Detection System for Agricultural Internet of Things
No full textThe file attached to this record is the author's final peer reviewed version.In this paper, we propose a federated learning-based intrusion detection system, named FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects data privacy through local learning, where devices benefit from the knowledge of their peers by sharing only updates from their model with an aggregation server that produces an improved detection model. In order to prevent Agricultural IoTs attacks, the FELIDS system employs three deep learning classifiers, namely, deep neural networks, convolutional neural networks, and recurrent neural networks. We study the performance of the proposed IDS on three different sources, including, CSE-CIC-IDS2018, MQTTset, and InSDN. The results demonstrate that the FELIDS system outperforms the classic/centralized versions of machine learning (non-federated learning) in protecting the privacy of IoT devices data and achieves the highest accuracy in detecting attacks
Hype, fear, and everything in between: A critical typology of responses to AI and their implications for language teacher education
No full textopen access articleThis article proposes a critical typology of five emerging responses to artificial intelligence (AI) in language education, from prohibition and hype to critical engagement, highlighting the assumptions, tensions, and possibilities each orientation embodies. This typology serves as a reflective tool to examine how educators and institutions are engaging with AI and what this means about the priorities shaping language teacher education (LTE). Building on this analysis, the article identifies seven implications for LTE, centred on reasserting pedagogical purpose: rethinking L2 writing, questioning neutrality, developing teacher discernment and transparency, problematising teacher preparedness, and engaging more deeply with digital pedagogy, teacher educator development, and ethical dilemmas. These directions call for sustained inquiry into how LTE can support teachers to navigate AI with clarity, agency, and purpose
Deep Learning Based Captioning of Toys in a Smart Monitoring System
No full textThe domain of image captioning has attracted increased interest in recent times due to advancements in computer vision technology and the incorporation of deep learning models, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These developments empower the creation of more precise and contextually comprehensive descriptions of images. This research aims to adapt deep learning to address the challenge of image captioning particularly for toys. A new dataset is curated in the research by sourcing copyright free images from websites featuring diverse categories of toys. Through augmentation techniques, the images are enhanced to promote dataset generalization and robustness, culminating in a comprehensive collection of images spanning distinct classes, each meticulously annotated with manually crafted captions. Feature extraction was performed using pre-trained VGG16, DenseNet201, ResNet50, and ResNet101. These models were fine-tuned to achieve optimal performance on the collected dataset. The language model utilized was LSTM. For extending the image captioning methodology to video captioning, YOLO was implemented to detect objects within video frames. Additionally, to assist visually impaired children and create a more inclusive environment, the captions were translated to audio using Google Text-to-Speech. The approach was evaluated with BLEU score and ResNet101+LSTM yielded the highest BLEU-1 score of 0.975825 outperforming the other proposed approaches
Anglo-Saxonism
No full textThis chapter examines the rise of Anglo-Saxonism as a political and cultural movement in the nineteenth century, and examines the ways in which it influenced the poetry of Gerard Manley Hopkins, paying particular attention to his use of formal and metrical devices associated with Anglo-Saxon poetry as these were understood at the time
pytopicgram: A library for data extraction and topic modeling from Telegram channels
No full textTelegram is a popular platform for communication, generating large volumes of messages through its open channels. pytopicgram is a Python library designed to help researchers efficiently collect, organize, and analyze Telegram messages, addressing the increasing demand to understand online discourse. Key functionalities include efficient message retrieval, computation of engagement metrics, and advanced topic modeling. By automating the data extraction and analysis pipeline, pytopicgram simplifies the investigation of how content spreads, how topics evolve, and how audiences interact on Telegram. The library’s modular architecture ensures flexibility and scalability, making it suitable for diverse applications. This paper describes the design, main features, and illustrative examples that demonstrate pytopicgram’s practical effectiveness for studying public conversations
RIS-Aided Unsourced Multiple Access (RISUMA): coding strategy and performance limits
No full textThis paper considers an unsourced random access (URA) set-up equipped with a passive reconfigurable intelligent surface (RIS), where a massive number of unidentified users (only a small fraction of them being active at any given time) are connected to the base station (BS). We introduce a slotted coding scheme for which each active user chooses a slot at random for transmitting its signal, consisting of a pilot part and a randomly spread polar codeword. The proposed decoder operates in two phases. In the first phase, called the RIS configuration phase, the BS detects the transmitted pilots. The detected pilots are then utilized to estimate the corresponding users’ channel state information, using which the BS suitably selects RIS phase shift employing the proposed RIS design algorithms. The proposed channel estimator offers the capability to obtain the channel coefficients of the users whose pilots interfere with each other without prior access to the list of transmitted pilots or the number of active users. In the second phase, called the data phase, transmitted messages of active users are decoded. Moreover, we establish an approximate achievability bound for the RIS-based URA scheme, providing a valuable benchmark. Computer simulations show that the proposed scheme outperforms the state-of-the-art RIS-aided URA
Numerical investigation of premixed and non-premixed hydrogen flames using large-eddy simulations and flamelet models
No full textA numerical investigation of premixed and nonpremixed hydrogen flames is performed with the main aim of assessing the capability of large-eddy simulations (LES) and flamelet models to predict the main characteristics of the reactive field. Two different burners are investigated: (i) a premixed bluff body burner fueled with a lean hydrogen–air mixture (ϕ=0.4) and (ii) a nonpremixed dual-swirl coaxial injector for which experiments show both anchored and lifted flames for the same global equivalence ratio (ϕg=0.45). Simulations of the premixed burner are performed using the Flamelet Generated Manifold (FGM) approach, whereas the two types of flames realized in the nonpremixed burner are studied using the Steady Diffusion Flamelet (SDM) and the Flamelet Generated Manifold. Numerical results are compared with the available experimental data for flow and flame characterization. As far as the velocity field is concerned, the investigated flamelet models have demonstrated capability to properly predict the location and magnitude of the velocity peaks and the shape of the inner recirculation zone (IRZ) in both the premixed and nonpremixed cases. Moreover, the computational framework used in this study has demonstrated good accuracy in the prediction of the dynamic behavior of the flow. Regarding the structure of the flame, the models have shown a good capability to predict both the shape of the flame and the location of regions of high-intensity heat release rate (HRR). The present investigation offers a comprehensive assessment of large-eddy simulations and flamelet methods in the prediction of the behavior of two archetypes of hydrogen flames of industrial interest. The assessment shown here can provide support for the choice of methods to study cases with increased level of complexity
Numerical investigation of premixed and non-premixed hydrogen flames using large-eddy simulations and flamelet models
No full textA numerical investigation of premixed and non-premixed hydrogen flames is performed with the main aim of assessing the capability of large-eddy simulations and flamelet models to predict the main characteristics of the reactive field. Two different burners are investigated: (i) a premixed bluff body burner fuelled with a lean hydrogen-air mixture (ϕ = 0.4) and (ii) a non-premixed dual-swirl coaxial injector for which experiments show both anchored and lifted flames for the same global equivalence ratio (ϕg = 0.45). Simulations of the premixed burner are performed using the Flamelet Generated Manifold approach, whereas the two types of flames realized in the non-premixed burner are studied using the Steady Diffusion Flamelet and the Flamelet Generated Manifold. Numerical results are compared with the available experimental data for flow and flame characterization. As far as the velocity field is concerned, the investigated flamelet models have demonstrated capability to properly predict the location and magnitude of the velocity peaks and the shape of the inner recirculation zone in both the premixed and non-premixed cases. Moreover, the computational framework used in this study has demonstrated good accuracy in the prediction of the dynamic behavior of the flow. Regarding the structure of the flame, the models have shown a good capability to predict both the shape of the flame and the location of regions of high-intensity heat release rate. The present investigation offers a comprehensive assessment of large-eddy simulations and flamelet methods in the prediction of the behavior of two archetypes of hydrogen flames of industrial interest. The assessment shown here can provide support for the choice of methods to study cases with increased level of complexity