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•The Sydney Declaration revisits the essence of forensic science.•A definition and 7 fundamental principles provide a renewed foundational basis.•The trace is pivotal as a vestige, or remnant, of an investigated activity.•The case-based and retrodictive nature of forensic science is emphasised.•The principles underpin the practice and guide education and research. Unlike other more established disciplines, a shared understanding and broad acceptance of the essence of forensic science, its purpose, and fundamental principles are still missing or mis-represented. This foundation has been overlooked, although recognised by many forensic science forefathers and seen as critical to this discipline's advancement. The Sydney Declaration attempts to revisit the essence of forensic science through its foundational basis, beyond organisations, technicalities or protocols. It comprises a definition of forensic science and seven fundamental principles that emphasise the pivotal role of the trace as a vestige, or remnant, of an investigated activity. The Sydney Declaration also discusses critical features framing the forensic scientist’s work, such as context, time asymmetry, the continuum of uncertainties, broad scientific knowledge, ethics, critical thinking, and logical reasoning. It is argued that the proposed principles should underpin the practice of forensic science and guide education and research directions. Ultimately, they will benefit forensic science as a whole to be more relevant, effective and reliable.

Logical reasoning is of great societal importance and, as stressed by the twenty-first century skills framework, also seen as a key aspect for the development of critical thinking. This study aims at exploring secondary school students’ logical reasoning strategies in formal reasoning and everyday reasoning tasks. With task-based interviews among 4 16- and 17-year-old pre-university students, we explored their reasoning strategies and the reasoning difficulties they encounter. In this article, we present results from linear ordering tasks, tasks with invalid syllogisms and a task with implicit reasoning in a newspaper article. The linear ordering tasks and the tasks with invalid syllogisms are presented formally (with symbols) and non-formally in ordinary language (without symbols). In tasks that were familiar to our students, they used rule-based reasoning strategies and provided correct answers although their initial interpretation differed. In tasks that were unfamiliar to our students, they almost always used informal interpretations and their answers were influenced by their own knowledge. When working on the newspaper article task, the students did not use strong formal schemes, which could have provided a clear overview. At the end of the article, we present a scheme showing which reasoning strategies are used by students in different types of tasks. This scheme might increase teachers’ awareness of the variety in reasoning strategies and can guide classroom discourse during courses on logical reasoning. We suggest that using suitable formalisations and visualisations might structure and improve students’ reasoning as well.

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

Previous research has demonstrated that reasoners’ Feeling of Rightness (FOR) for a quick, intuitive responses predicts the amount of analytic thinking they give to slower, more considered responses operationalized in terms of the length of thinking time and the probability of answer changes (Thompson et al., Cognitive Psychology, 63 (3), 107–140, 2011). In this experiment, we tested the novel hypothesis that FORs can also signal the direction in which answers will change when participants reason about a sequence of similar inferences. 289 participants responded to two blocks of belief-logic conflict syllogisms, with the first under an initial time constraint and the second in a no-constraint condition. Of particular interest were those participants who gave a mixed pattern of validity- and belief-based responses under time constraints, because they had the opportunity to shift their responses towards either belief-based or validity-based responses in the unconstrained condition. Consistent with our hypothesis, reasoners giving low FORs to their belief-based responses shifted their responses towards validity-based ones in the unconstrained condition, whereas those giving high FORs shifted towards belief-based responses. Thus, intuitive FORs generated during a sequence of inferential problems predicted both the probability and direction of answer change.

Logical reasoning tasks are more challenging than traditional machine reading comprehension tasks. The machine must recognize the logical relationships implicit in the text and use logical reasoning to derive an answer. Logical reasoning tasks currently face two major challenges. The first challenge is the difficulty of capturing the logical relationships implicit in the text. The second involves connecting the divide between distinct logical structures and the continuous space of text embeddings. In this study, we present a contrastive network based on multiple graphs designed to tackle these issues through the combination of both implicit and explicit logical connections, thereby enhancing reasoning capabilities. We use different construction strategies to create logical relationship graphs and logical hypergraph graphs. These graphs are integrated into a multi-graph contrastive network to learn higher-order logical representations, which are then used as inputs to a decoder for final prediction. The evaluation of our designed models was performed on datasets designed to assess reasoning ability, including ReClor, LogiQA and the extended iteration LogiQA 2.0. The experimental results show that our proposed method outperforms the state-of-the-art models. In particular, the results obtained on the LogiQA 2.0 dataset, which contains a larger number of samples, are particularly outstanding. Our model achieved an accuracy rate of 59.16%, outperforming most of the baseline comparisons by at least one percentage point, demonstrating its superior potential in complex reasoning tasks.

Smart wearable devices detection and recording of people’s everyday activities is critical for health monitoring, helping persons with disabilities, and providing care for the elderly. Most of the research that is being conducted uses a machine learning-based methodology; however, these approaches frequently have issues with high computing resource consumption, burdensome training data gathering, and restricted scalability across many contexts. This research suggests a behaviour detection technology based on multi-source sensing and logical reasoning to address these problems. In order to realize the natural fusion of signal processing and logical reasoning in behavior recognition research, this work designs a lightweight behavior recognition solution using the pertinent theories of ontology reasoning in classical artificial intelligence. Machine learning technology is also employed for behavior recognition using the same data set. Once the best model has been chosen, the cross-person recognition results after testing and modification of parameters are 90.8% and 92.1%, respectively. This technology was used to create a behaviour recognition system, and several tests were run to assess how well it worked. The findings demonstrate that the suggested strategy achieves over 90% recognition accuracy for 11 different daily activities, including jogging, walking, and stair climbing. Additionally, the suggested strategy dramatically minimises the quantity of user-provided training data needed in comparison to machine learning-based behaviour identification techniques.

Artificial intelligence, since its inception, has had two major subfields, namely: logical reasoning and machine learning. Despite this, the interactions between these two fields have been relatively limited. In this paper, we highlight the need for closer integration of logical reasoning and machine learning. In our approach, logical reasoning tools such as probabilistic modal logic, are employed to provide qualitative feedback on the extracted descriptive metadata. The logical system we consider emerges from combining of S5 modal logic with the formulas of the infinite-valued Łukasiewicz logic and the unary modality P that describes the behaviour of probability functions. The result is a well-motivated system of probabilistic modal logic, that defines a probability distribution over possible worlds of the truth value of metadata extracted from precision medicine approach to Alzheimer’s disease articles through machine learning systems.

To date, despite the great debate regarding the best seating arrangement for learning in classrooms, no empirical studies have examined the direct effects of different seating arrangements on children’s cognitive processes. This is particularly important nowadays that the COVID-19 measures include maintaining distance in the classroom. Aim of this study was experimentally investigating the effect of changing the seating arrangement (clusters vs. single desks), on logical reasoning, creativity and theory of mind, in children attending primary school. Furthermore, some individual characteristics (e.g., gender, loneliness, popularity) were analysed as potential moderators. Results on 77 participants showed that, when children were seated in single desks, their score in logical reasoning was globally higher. Furthermore, when seated in single desks, girls showed a better performance in the theory of mind, and lonelier children performed better in theory of mind and creativity. This on field experimental study suggests the importance of considering both the nature of the task and children’s individual characteristics when deciding on a seating arrangement in the classroom.

Truth-seeking is a critical thinking disposition that plays an essential role in solving mathematical literacy problems. However, many elementary school students lack truth-seeking skills. Therefore, action is needed to encourage students to develop truth-seeking skills. This study aims to describe the truth-seeking characteristics of elementary school students based on logical reasoning in solving mathematical literacy problems. A descriptive qualitative approach was employed. Data were collected through interviews and student responses to mathematical literacy problems. The data analysis involved reviewing, summarising, selecting, and validating authentic data using triangulation. The results indicated that four students demonstrated truth-seeking tendencies. The characteristics of truth-seeking observed include: (1) information analysis; (2) curiosity & questioning; (3) strategy development; (4) evaluation & verification; and (5) conclusion & reflection.

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system’s situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.