Search Results (298)

One of the most important issues in the coming years will be the decarbonisation of the European Union member states’ energy systems. The majority of the abstract requires modification. I propose that the first sentence of the abstract in the manuscript should better emphasize the formulation of the problem. The remaining part and any corrections were made by the author. Scenarios of the importance of CCS in the process of transformation of energy systems in Poland. One of the most important issues in the coming years will be the transformation of the energy systems of the European Union’s member states, which will require the development of appropriate technological solutions. The research presented here analyses the importance of CCS in energy transformation. This article proposes adapting the energy transformation method to the structure of the energy mix and conditions prevailing in a specific country. Poland was adopted as an example for analysis due to its exceptionally complicated situation, taking into account the structure of energy production. For this purpose, an expert opinion survey was conducted. Both measurable variables, such as the volume of CO₂ emissions and EU ETS prices, and a qualitative variable, i.e., the impact of the political environment on the development of CCS, were introduced to the constructed model. The model allowed us to construct three scenarios describing alternative visions for the future development of CCS: optimistic, pessimistic, and neutral, taking into account different conditions in which CCS can develop. The use of fuzzy sets allowed us to eliminate the most serious drawback of planning scenarios based on expert knowledge, which is the subjectivity of their judgments. This research showed that stable conditions of the political environment and predictable legal regulations will be crucial for the application of CCS in the Polish energy sector. The prepared scenarios will enable a quick response and accurate decisions under various conditions of the turbulent environment. This will facilitate the preparation of energy strategies. The scenarios indicate what combinations of variables, under given environmental conditions, of CCS will be of great importance in the energy transformation, and when it may give way to other technologies. In addition, the scenarios, and especially their visualisation, are extremely valuable for stakeholders, because they will allow them to observe the potential development of the situation under known conditions of the political environment, prices, and CO₂ emissions. They enable understanding the dependence of the importance of CCS in the changing environment. They also enable the detection of critical points for the development of CCS, which, as a result of recent geopolitical events, may be of key importance in the near future for ensuring the energy and military security of Poland and the EU. Full article

Although verb clusters in Continental West Germanic varieties are a well-researched topic, their derivation and the possible functions of their variants are still not yet fully understood. Both issues are discussed in the present paper, which is based on the translations of 46 English, Spanish, or Portuguese stimulus sentences by 321 North and South American speakers of Mennonite Low German. In order to analyze the variation in clause-final two-verb clusters, we focus on three structural factors, namely (i) the auxiliary verb, (ii) the structural link between the auxiliary verb and the main verb, and (iii) the type of the subordinate clause in which the cluster occurs. Regarding the first and the second factor, we will employ the cartographic approach in order to explain the impact of different auxiliary verbs. Regarding the third factor, it is somewhat surprising that the potential effect of the subordinate clause on the distribution of different cluster variants has received little attention in the research literature. Clause type will be shown to have such an effect and, therefore, we will assume that the speakers of MLG use different variants deliberately to indicate different degrees of clausal integration. Full article

With the increasing adoption of digital governance and big data analytics, the quality of government hotline data significantly affects urban governance and public service efficiency. However, existing methods for assessing data record standards focus predominantly on structured data, exhibiting notable inadequacies in handling the complexities inherent in unstructured or semi-structured textual hotline records. To address these shortcomings, this study develops a comprehensive scoring method tailored for evaluating multi-dimensional data record standards in government hotline data. By integrating advanced deep learning models, we systematically analyze six evaluation indicators: classification predictability, dispatch accuracy, record correctness, address accuracy, adjacent sentence similarity, and full-text similarity. Empirical analysis reveals a significant positive correlation between improved data record standards and higher work order completion rates, particularly highlighting the crucial role of semantic-related indicators (classification predictability and adjacent sentence similarity). Furthermore, the results indicate that the work order field strengthens the positive impact of data standards on completion rates, whereas variations in departmental data-handling capabilities weaken this relationship. This study addresses existing inadequacies by proposing a novel scoring method emphasizing semantic measures and provides practical recommendations—including standardized language usage, intelligent analytic support, and targeted staff training—to effectively enhance urban governance. Full article

Background: Cognitive stimulation programs typically consist of task collections (“bundles”) designed to cover various aspects of a cognitive domain and/or sustain user engagement. However, task order is often overlooked, despite variations in difficulty based on structure or mode of implementation. This study examined users’ performance accuracy across the eight tasks that comprise the BOX semantic-based program, adapted for the Cerup/CQ online platforms. Our ultimate goal was to map the tasks onto increasing levels of challenge within thematic clusters to provide guidance for personalized task selection. Methods: After adapting the program into Portuguese using original materials based on BOX task descriptions, we made Cerup and CQ (which share the same content but have different layouts) available as free web-based tools. Participants, primarily older adults without dementia, were invited to use these platforms for cognitive stimulation. We analyzed accuracy data as a function of activity-related characteristics (complexity scores, sentence- vs. word-level) as well as participants’ spontaneous task selection. Results: Task characteristics influenced performance accuracy, indicating different levels of challenge across activities. However, spontaneous task selection did not follow any discernible pattern beyond the spatial contiguity of activity buttons, which was unrelated to participants’ likelihood of success. Based on these findings, we defined optimal navigation paths for the eight tasks. Conclusions: Challenge-based, active guidance for task selection appears justified and necessary within the BOX/Cerup/CQ programs. Additionally, the method we developed may help other programs enhance user experience and optimize task progression. Full article

Remote Sensing Image Change Captioning (RSICC) has emerged as a cross-disciplinary technology that automatically generates sentences describing the changes in bi-temporal remote sensing images. While demonstrating significant potential for urban planning, agricultural surveillance, and disaster management, current RSICC methods exhibit two fundamental limitations: (1) vulnerability to pseudo-changes induced by illumination fluctuations and seasonal transitions and (2) an overemphasis on spatial variations with insufficient modeling of temporal dependencies in multi-temporal contexts. To address these challenges, we present the Frequency–Spatial–Temporal Fusion Network (FST-Net), a novel framework that integrates frequency, spatial, and temporal information for RSICC. Specifically, our Frequency–Spatial Fusion module implements adaptive spectral decomposition to disentangle structural changes from high-frequency noise artifacts, effectively suppressing environmental interference. The Spatia–Temporal Modeling module is further developed to employ state-space guided sequential scanning to capture evolutionary patterns of geospatial changes across temporal dimensions. Additionally, a unified dual-task decoder architecture bridges pixel-level change detection with semantic-level change captioning, achieving joint optimization of localization precision and description accuracy. Experiments on the LEVIR-MCI dataset demonstrate that our FSTNet outperforms previous methods by 3.65% on BLEU-4 and 4.08% on CIDEr-D, establishing new performance standards for RSICC. Full article

Generative Artificial Intelligence (AI) has transformed personal and professional domains by enabling creative content generation and problem-solving. However, the influence of users’ grammatical abilities on AI-generated responses remains unclear. This exploratory study examines how language and grammar abilities in Spanish affect the quality of responses from ChatGPT (version 3.5). Despite the robust performance of Large Language Models (LLMs) in various tasks, they face challenges with grammatical moods specific to non-English languages, such as the subjunctive in Spanish. Higher education students were chosen as participants due to their familiarity with AI and its potential use in learning. The study assessed ChatGPT’s ability to process instructions in Chilean Spanish, analyzing how linguistic complexity, grammatical variations, and informal language impacted output quality. The results indicate that varied verbal moods and complex sentence structures significantly influence prompt evaluation, response quality, and response length. Based on these findings, a framework is proposed to guide higher education communities in promoting digital literacy and integrating AI into teaching and learning. Full article

Geometry problem understanding (GPU) is a fundamental task in machine intelligence for problem-solving, requiring more accurate and complete information extraction than general natural language understanding tasks. This paper proposes a knowledge and semantic fusion method to achieve high-quality, interpretable, and scalable GPU. It extracts text-level and knowledge-level entities and relationships from problem texts and transforms them into a semantic knowledge graph. First, a dual-layer semantic-enhanced knowledge ontology model (SGKO) tailored for the geometry domain is constructed. By separating the ontology and data layers and combining the strengths of both the knowledge system type ontology and the semantic network type ontology, it enables bidirectional association between conceptual-level knowledge and object-level textual data. Second, a dynamically generated modular relationship matching template is introduced, which is decomposed into reusable atomic components and dynamically assembled through knowledge base queries, significantly reducing template quantity while enhancing adaptability to complex text structures. Additionally, a state-machine-based semantic information extraction model (IDIM-T) is designed that achieves efficient and interpretable semantic extraction through categorized relationship description types. This is combined with a rule-based method (IDIM-K) to complete knowledge-level entity relationship extraction. To validate the method, a dataset was constructed from authoritative sources, including past middle school exam questions, textbooks, and exercise books, covering unary, binary, and ternary relationships, as well as single-clause, cross-clause, and multi-relationship conjunction expressions. Experiments on 230 problems with complex relational descriptions showed that the proposed method achieved fully accurate two-level relationship parsing for 91.87% of the problems. Compared with four baseline methods (sentence template-based, Bi-LSTM-based, Transformer-based, and $S^2$-based), the method achieved the highest F1 score (0.974) for 1832 relationships, outperforming the highest F1 score (0.900) of the baselines. Full article

This study investigates the cross-linguistic priming effect in the syntactic written output of late bilingual Levantine Arabic speakers who learn English as a second language. In particular, we examined priming sentence type (simple vs. complex sentences) and priming language condition (Levantine Arabic vs. English). Forty-nine bilinguals (Mean age = 33.3, SD = 8.5), who learned Levantine Arabic as their L1 and English as their L2, were primed with a short paragraph presented on the computer screen in either English or Levantine Arabic and asked to produce a written response in the counterpart language. Logistic regression analysis revealed a significant cross-linguistic priming effect, suggesting that the syntactic structure of the prime in the participants’ first language (Levantine Arabic) predicts the participants’ written output in the second language (English), and the reverse is also true. However, there was no significant effect of priming sentence type (simple vs. complex) on the likelihood of producing primed res ponses, indicating that both priming conditions yielded similar levels of priming. In contrast, there was a significant effect of the priming language condition, with participants significantly more likely to produce syntactically primed responses when the priming language was Arabic compared to English. In addition, there was a significant interaction between the priming language condition and priming sentence type: Arabic priming led to more simple sentence production in English, whereas English priming did not significantly affect sentence complexity in Arabic. These findings align with the shared syntax account but highlight the need to consider factors such as language dominance in bilingual syntactic processing. Full article

Background: Improving prediction models to timely detect lung cancer is paramount. Our aim is to develop and validate prediction models for early detection of lung cancer in primary care, based on free-text consultation notes, that exploit the order and context among words and sentences. Methods: Data of all patients enlisted in 49 general practices between 2002 and 2021 were assessed, and we included those older than 30 years with at least one free-text note. We developed two models using a hierarchical architecture that relies on attention and bidirectional long short-term memory networks. One model used only text, while the other combined text with clinical variables. The models were trained on data excluding the five months leading up to the diagnosis, using target replication and a tuning set, and were tested on a separate dataset for discrimination, PPV, and calibration. Results: A total of 250,021 patients were enlisted, with 1507 having a lung cancer diagnosis. Included in the analysis were 183,012 patients, of which 712 had the diagnosis. From the two models, the combined model showed slightly better performance, achieving an AUROC on the test set of 0.91, an AUPRC of 0.05, and a PPV of 0.034 (0.024, 0.043), and showed good calibration. To early detect one cancer patient, 29 high-risk patients would require additional diagnostic testing. Conclusions: Our models showed excellent discrimination by leveraging the word and sentence structure. Including clinical variables in addition to text slightly improved performance. The number needed to treat holds promise for clinical practice. Investigating external validation and model suitability in clinical practice is warranted. Full article

Background/objectives: A child’s working memory needs to be efficient in order to perform well at school, because its manipulative function needs to work properly in order to compose and decompose words, a skill that is necessary for reading. Therefore, if a child with an alteration in this type of memory reads a more complex sentence, they will have difficulty storing it until other cognitive processes involved in language comprehension and production take place, leading to impaired reading comprehension. The aim of this study was to develop and verify the applicability of a remediation program for working memory and reading in students with learning difficulties from the third to fifth grades of primary school. Methods: The study was carried out in two phases: phase 1 developed the program on the basis of a literature review, and phase 2 verified the applicability of the program in a pilot study with 21 schoolchildren divided into two groups. The subjects were subjected to tests of metalinguistic and reading skills and the Brief Child Neuropsychological Assessment Instrument. Results: The working memory and reading remediation program consisted of 11 tasks developing phonological and visuospatial working memory. From the results of the application of the Remediation Program With Working Memory and Reading (RP-WMR) in a pilot study, it was possible to verify the applicability of the program; in other words, the strategies developed for students with learning difficulties can be generalised and applied to students who have deficits in working memory and reading. Conclusions: The result of this research indicates that the structured program for remediation of working memory difficulties has proven to be applicable and can help education professionals as a tool for intervening in working memory deficits and reading decoding skills presented by students with learning difficulties. Full article

Prosody is crucial for resolving phrasal ambiguities. Recent research suggests that gestures can enhance this process, which may be especially useful for children with Developmental Language Disorder (DLD), who have impaired structural language. This study investigates how children with DLD use prosodic and gestural cues to interpret phrasal ambiguities. Catalan-speaking children with and without DLD heard sentences with two possible interpretations, a high (less common) and low (more common) attachment interpretation of the verb clause. Sentences were presented in three conditions: baseline (no cues to high-attachment interpretation), prosody-only (prosodic cues to high-attachment interpretation), and multimodal (prosodic and gestural cues to high-attachment interpretation). Offline target selection and online gaze patterns were analysed across linguistic (DLD vs. TD) and age groups (5–7 vs. 8–10 years old) to see if multimodal cues facilitate the processing of the less frequent high-attachment interpretation. The offline results revealed that prosodic cues influenced all children’s comprehension of phrasal structures and that gestures provided no benefit beyond prosody. Online data showed that children with DLD struggled to integrate visual information. Our findings underscore that children with DLD can rely on prosodic cues to support sentence comprehension and highlight the importance of integrating multimodal cues in linguistic interactions. Full article

With the rapid advancement of human–machine dialogue technology, sentiment analysis has become increasingly crucial. However, deep learning-based methods struggle with interpretability and reliability due to the subjectivity of emotions and the challenge of capturing emotion–cause relationships. To address these issues, we propose a novel sentiment analysis framework that integrates structured commonsense knowledge to explicitly infer emotional causes, enabling causal reasoning between historical and target sentences. Additionally, we enhance sentiment classification by leveraging large language models (LLMs) with dynamic example retrieval, constructing an experience database to guide the model using contextually relevant instances. To further improve adaptability, we design a semantic interpretation task for refining emotion category representations and fine-tune the LLM accordingly. Experiments on three benchmark datasets show that our approach significantly improves accuracy and reliability, surpassing traditional deep-learning methods. These findings underscore the effectiveness of structured reasoning, knowledge retrieval, and LLM-driven sentiment adaptation in advancing emotion–cause-based sentiment analysis. Full article

Existing studies have revealed that the reading comprehension ability of readers can be adversely affected by their psychosocial stress. Yet, the detailed impact of stress on various stages of text processing is understudied. This study aims to explore how the higher-level text processing ability, including syntactic parsing, sentence integration, and global text processing, of first language (L1) and second language (L2) English readers is affected under stress situations. In addition, the roles of trait anxiety, the central executive function moderating stress effects, in text processing were also examined. Twenty-two L1 readers and twenty-one L2 readers were asked to perform reading comprehension tasks under different stress situations. Eye-tracking technology was adopted to record participants’ visual behaviors while reading, and ten eye-movement measurements were computed to represent the effect of different types of text processing. The results demonstrate that the stress reduced the efficiency of syntactic parsing and sentence integration in both L1 and L2 groups, but only impaired global text processing in L2 readers. Specifically, L2 readers focused more on the topic structure of text to facilitate comprehension under stress situations. Moreover, only L1 readers’ higher-level text processing was affected by trait anxiety, while L2 readers’ processing was mainly related to their reading proficiency level. Future studies and applications were discussed. The findings advance our understanding of stress effects on different stages of higher-level text processing. They also have practical implications for developing interventions to help language learners suffering from stress disorders. Full article

Sentiment analysis in Chinese microblogs is challenged by complex syntactic structures and fine-grained sentiment shifts. To address these challenges, a Contextually Enriched Graph Neural Network (CE-GNN) is proposed, integrating self-supervised learning, context-aware sentiment embeddings, and Graph Neural Networks (GNNs) to enhance sentiment classification. First, CE-GNN is pre-trained on a large corpus of unlabeled text through self-supervised learning, where Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) are leveraged to obtain contextualized embeddings. These embeddings are then refined through a context-aware sentiment embedding layer, which is dynamically adjusted based on the surrounding text to improve sentiment sensitivity. Next, syntactic dependencies are captured by Graph Neural Networks (GNNs), where words are represented as nodes and syntactic relationships are denoted as edges. Through this graph-based structure, complex sentence structures, particularly in Chinese, can be interpreted more effectively. Finally, the model is fine-tuned on a labeled dataset, achieving state-of-the-art performance in sentiment classification. Experimental results demonstrate that CE-GNN achieves superior accuracy, with a Macro F-measure of 80.21% and a Micro F-measure of 82.93%. Ablation studies further confirm that each module contributes significantly to the overall performance. Full article

Efficient language analysis techniques and models are crucial in the artificial intelligence age for enhancing cross-lingual question answering. Transfer learning with state-of-the-art models has been beneficial in this regard, but the performance of low-resource African languages with morphologically rich grammatical structures and unique typologies has shown deficiencies linkable to evaluation techniques and scarce training data. To enhance the former, this paper proposes an evaluation pipeline leveraging the semantic answer similarity method enhanced with automatic answer annotation. The pipeline uses the Language-agnostic BERT Sentence Embedding model integrated with an adapted vector measure to perform cross-lingual text analysis after answer prediction. Experimental results from the multilingual-T5 and AfroXLMR models on nine languages of the AfriQA dataset surpassed existing benchmarks deploying string-based methods for question answer evaluation. The results are also superior to the F1-score-based GPT4 and Llama-2 performances on the same downstream task. The automatic answer annotation technique effectively reduced the labelling time while maintaining a high performance. Thus, the proposed pipeline is more efficient than the prevailing string-based F1 and Exact Match metrics in mixed answer type question–answer evaluations, and it is a more natural performance estimator for models targeting real-world deployment. Full article