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The paper aims to enhance Arabic machine translation (MT) by proposing novel approaches: (1) a dimensionality reduction technique for word embeddings tailored for Arabic text, optimizing efficiency while retaining semantic information; (2) a comprehensive comparison of meta-embedding techniques to improve translation quality; and (3) a method leveraging self-attention and Gated CNNs to capture token dependencies, including temporal and hierarchical features within sentences, and interactions between different embedding types. These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.

Recent works on MT in general and Arabic MT in particular often pick one type of word embedding model. In this paper, we present a novel approach to enhance Arabic MT by addressing three key aspects. Firstly, we propose a new dimensionality reduction technique for word embeddings, specifically tailored for Arabic text. This technique optimizes the efficiency of embeddings while retaining their semantic information. Secondly, we conduct an extensive comparison of different meta-embedding techniques, exploring the combination of static and contextual embeddings. Through this analysis, we identify the most effective approach to improve translation quality. Lastly, we introduce a novel method that leverages self-attention and Gated convolutional neural networks (CNNs) to capture token dependencies, including temporal and hierarchical features within sentences, as well as interactions between different types of embeddings. Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance. It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches, with an average improvement of 4.6 points across all evaluation metrics.

The proposed approaches significantly enhance Arabic MT performance. The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information. Comprehensive comparison identifies effective meta-embedding techniques, with the contextualized dynamic meta-embeddings (CDME) model showcasing competitive results. Integration of Gated CNNs with the transformer model surpasses baseline performance, leveraging both architectures' strengths. Overall, these findings demonstrate substantial improvements in translation quality, with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics, outperforming state-of-the-art approaches.

The paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task. Instead, it introduces modifications to the internal architecture of the transformer, integrating Gated CNNs to enhance translation performance. This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement, offering unique insights into improving translation quality without solely relying on pre-existing architectures. The originality in dimensionality reduction lies in the tailored approach for Arabic text. While dimensionality reduction techniques are not new, the paper introduces a specific method optimized for Arabic word embeddings. By employing independent component analysis (ICA) and a post-processing method, the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Practitioners should develop argumentation strategies based on contextual dependencies and the optimal strategy that aligns with the political cause.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

While servitization has been recognised for its potential to augment organizational revenue and fortify competitive advantage, the exploration of alternative servitization trajectories to the classical servitization model has been little explored in literature. Recent literature introduces the “service paradox” and presents different trajectories to the classical model, but it does not explain why a company chooses one trajectory instead of another. Therefore, this study aims to provide a model that, based on the contextual factors present, recommends which servitization trajectory the company should choose.

This study uses a combination of design science research (DSR) and context, intervention, mechanisms and outcomes (CIMO) to propose the model. An initial contextual factors list was created based on the literature, refined by the company’s employees and evaluated in three selected initiatives in the focal company. Furthermore, based on the understanding of the CIMO logic elements, four design propositions were elaborated to summarize the main findings of the study.

The study has demonstrated that the choice of a servitisation trajectory is intricately tied to a multitude of contextual factors, prompting organisations to deviate from conventional models towards alternative paths. Furthermore, the research sheds light on the underlying mechanisms and contextual drivers that shape servitisation decisions within the context of a consumer goods manufacturer. The analysis underscores the pivotal role of market dynamics and strategic adaptability in shaping servitisation strategies, underscoring the importance of customized approaches that cater to the distinct circumstances of each organisations.

The research contributes to both theory and practice by offering profound insights into the complex nature of servitisation, advocating for continuous adaptation and strategic alignment with market demands. For practitioners and decision-makers, the study provides valuable guidance on enhancing service offerings and navigating the complexities of servitisation within specific sectors, fostering a culture of learning and adaptation to drive sustainable growth.

Managerial discourses tend to portray work-related mobility practices in a positive light, presenting mobility assignments as a place of stimulus and differentiation. A conception of mobility as an opportunity, may contrast, in specific economies and business settings, with lived personal experiences. This article reports the results of a three-year study, aimed to question how multinational companies (MNCs) located in a small and developing European economy (Portugal) are building talent pools for expatriate assignments. Interaction effects, as proposed by the job demands-resources (JD-R) theory, are considered as lens to understand the interplay of company expatriate policies, willingness profiles and psychological contracts of expatriates. By using a Portuguese sample, the study examines whether prior findings in mature economies and consolidated MNCs can be generalized to less developed international business settings.

A three-year study, encompassing 24 expatriate cases observed in five multinational firms born or located in Portugal. Two techniques of empirical data collection were used: statistical sources and documental analysis and in-depth interviews. A total of 37 interviews were conducted, both in-person and remotely, of which 13 were with company managers and representatives, and 24 with expatriates (as defined and referred like this by the companies under study).

Heterogeneous company policies, ranging from juvenile, functionalist to more dynamic and flow-based approaches, are presented as qualifying resources of willingness levels and psychological contracts of expatriates. Observed interaction effects between policies, willingness and psychological contracts, empirically mirrored in three profiles (conformist, protean and disrupted expatriates) suggest that incentive effects (emanating from company policies) and job demand-resource balance, factored as terms of social and economic trade, are non-linear and asymmetric, influencing firm propensity to succeed while using international work to support company expansion goals. As job resources, expatriate policies are presented as operating as pull or push factors: functionalist HR approaches seem to act as push factors generating more conformist or compelled willingness profiles.

Generalization of study's outcomes has limitations. Future studies are encouraged to use comparative and longitudinal research designs. Furthermore, future research should include business expatriates with entry-level positions, and increase the number of interviewees, as results can also be considered as limited by sample size.

It is suggested that further strategic work is needed to present expatriation development value, formally screen and consider willingness level as selection criteria, and enlarge the pool (from internal to external) of candidates, in peripheral economic settings such as Portugal. A shift to more dynamic and job resource-dense policies are suggested as beneficial, as pathway to optimize social and economic value from expatriation assignments and work experiences.

By putting the interplay between macro and micro-level processes into perspective, the study provides empirical evidence on how company expatriate policies have come to promote unforeseen differentiation of employee willingness and psychological contracts at the heart of MNCs. This is particularly relevant in developing economies such as Portugal, challenging the need to build talent pools for international work assignments. Empirical data illustrating company policies interactive effects with different willingness profiles and psychological contracts of expatriates is provided.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.

The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.

The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.

This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

In this chapter, practice-theoretical perspectives on the production of gender and childhood are extended by the theory of new materialism. A practice-theoretical view of masculinity(ies) radicalises the concept of doing gender and thereby makes it possible to show that gender is always co-produced as part of other complexes of praxes. Thus, the connection between masculinity(ies) and youth cultural praxes can be discussed. The chapter first elaborates theoretically the connections between masculinity and childhood research. We will explore how these theoretical and methodological thoughts might be used in empirical research on masculinity(ies) and boyhood by referring to our own study on children and young people riding stunt scooters in a medium-sized city in north-west Germany.