教学课程资源库 更多>>

关键词： Computer science education   Female under-representation   Gender   Ph.D. career path   Ph.D. challenges and opportunities  

摘要： Context: Computer science, even now, attracts a small number of women, and the proportion of women in the field decreases through advancing career stages. Consequently, few women progress to Ph.D. studies in computer science after completing master’s studies. Empowering women at this stage in their careers is essential, not just for equality reasons, but to unlock untapped potential for society, industry and academia. Objective: This paper aims to identify students’ career assumptions and information related to Ph.D. studies focused on gender-based differences. We propose a program to inform female master students about Ph.D. studies that explains the process, clarifies misconceptions, and alleviates concerns. Method: An extensive survey was conducted to identify factors that encourage and discourage students from undertaking Ph.D. studies. The analysis identified statistically significant differences between those who undertook Ph.D. studies and those who did not, as well as statistically significant gender differences. A catalogue of questions to initiate discussions with potential Ph.D. students which allowed them to explore these factors was developed. These were structured into a Women’s Career Lunch program where students can explore and discuss the benefits of Ph.D. study. Results: Encouraging factors towards Ph.D. study include interest and confidence in research arising from a research involvement during earlier studies; enthusiasm for and self-confidence in computer science in addition to an interest in an academic career; encouragement from external sources; and a positive perception towards Ph.D. studies which can involve achieving personal goals. Discouraging factors include uncertainty and lack of knowledge of the Ph.D. process, a perception of lower job flexibility, and the requirement for long-term commitment. Gender differences highlighted that female students who pursue a Ph.D. have less confidence in their technical skills than males but a higher

关键词： Flipped classroom   Hands-on learning   Interactive learning environments   Life sciences education   Self-directed learning   Student engagement  

摘要： This study assesses the impact of interactive learning environments and flipped classroom models on student engagement and learning outcomes in life sciences. We focused on the “Introduction to Laboratory Mice” course, utilizing the MITAKA SUPPLY Mimicky^® Mouse model alongside IT tools like Google Classroom, Kahoot, Mentimeter, Quizizz, and Zuvio for declarative knowledge instruction. The instructional approach involved: (1) delivering knowledge via flipped classroom with IT tools, (2) training in laboratory mice handling, (3) applying flipped classroom techniques with videos and presentations, and (4) fostering interactive environments through peer assessment. The study used pre-tests and post-tests, structured flipped classroom setups, and outcome assessments. Results showed that the flipped classroom model, paired with hands-on activities, markedly enhanced student engagement and comprehension. Attitudinal surveys reflected a positive shift, with students expressing greater confidence in their knowledge and skills in life sciences. This study highlights the role of clear objectives and goals for self-directed learning. Future research will examine optimal resource distribution and the long-term effects of these educational strategies. Findings provide key insights for educators in implementing and improving interactive learning within life sciences curricula. © The Author(s) 2025.

关键词： 计算机   字母词   字母词素   分支领域  

摘要： 文章采用计量分析方法,对《计算机科学技术名词》三个版本收录的字母词进行了详细分析,并对字母词的词素和字母词的分布领域做了深入研究。研究发现,字母词数量和比例都呈现明显上升趋势,字母词主要出现于计算机语言、网络、通信等新兴领域。研究为计算机领域字母词规范化提供了数据支持,并为字母词提供了新的研究视角。

关键词： COVID-19   Emergency remote teaching   Instructors’ experience   Online education   Survey study  

摘要： In May 2020, about 2 months after countless institutions across the world resorted to moving all their courses online in response to the COVID-19 pandemic, we conducted a survey to evaluate the impact of this transition on a group of computer science students. That first survey highlighted mostly negative effects, with students struggling to perform many class-related activities. About a year later, after a full year of remote teaching, we wanted to see if and how the students’ sentiment had changed. To assess students’ perceptions of remote teaching, we conducted a new survey composed of 41 multiple choice, Likert scale and open-ended questions. Additionally, we interviewed instructors of computer science courses, to learn about their experience and how they adapted to the new teaching modality. 137 students and 10 instructors shared their feedback regarding their positive and negative experiences in the new learning format. Our results show that the students’ experience improved significantly, to the point that many of them expressed interest in continuing learning online, at least partially, but some populations (e.g., early years students) may still be at a disadvantage in this learning format. At the same time, the instructors manifested concerns that this may not produce the best learning outcomes for the students. The results and considerations included in this report may benefit the conversation on how to conduct computer science higher education in a post-pandemic world. © The Author(s) 2025.

关键词： Condition monitoring   Remaining useful life prediction   Health prognostics   Deep learning   Degradation model  

摘要： Accurately predicting the remaining useful life of lithium-ion batteries is crucial for ensuring the operational safety and reliability of energy systems. However, conventional models assume constant degradation patterns under varying operational conditions and lack an interactive mechanism to update model structure or parameters using newly acquired battery data. To address this issue, this paper proposes an interactive prognostics framework integrating a deep learning model, a statistical process, and an interactive mechanism to jointly predict the remaining useful life of lithium-ion batteries. A deep learning model is developed based on a multiscale cross-attention network, which employs a dual-channel network integrated through a cross-attention mechanism to estimate the capacity of the lithium-ion batteries. Meanwhile, the capacity degradation of lithium-ion batteries is modeled as a statistical process governed by a switching state-space model, whose parameters and capacity states are dynamically updated via an expectation-maximization algorithm. Afterward, the interactive prognostics mechanism iteratively integrates capacity estimation from both the multi-scale crossattention network and switching state-space model models. Each model's capacity estimates are used to update the parameters of the other, to recursively update the remaining useful life prognostics of lithium-ion batteries. The proposed approach is validated by two case studies, which demonstrate the effectiveness and superiority of the presented model.

关键词： Mercury   Deforestation   Water Pollution   Health   Cyanide   Sustainable Development Goals (SDGs)  

摘要： This paper critically examines how military interventions aimed at eradicating informal or illegal mining unintentionally expand the environmental footprint of mining activities. In recent years, governments in many informal mining regions have increasingly relied on military forces to curb the environmental damage attributed to these unregulated practices. Although military action has gained popularity as a rapid-response strategy, its effectiveness in addressing informal mining remains limited. Drawing on the theory of unintended consequences and field data from southwestern Ghana, this study provides new evidence that military interventions, rather than curtailing informal mining, inadvertently drive miners to relocate to undisturbed areas, exacerbating environmental degradation. Miners adapt to military actions by moving to new sites, with mechanized operations significantly accelerating ecological harm. To illustrate this complex dynamic, the study introduces the Intervention and Environmental Expansion (IEE) Framework, demonstrating how ongoing military interventions can inadvertently intensify mining activities and environmental damage.

关键词： Quantum Technology   Physics   Engineering   Computer Science   Interdisciplinary   Workforce Development   Industry  

摘要： In this article, we investigate a growing trend in the worldwide Quantum Technology (QT) education landscape, that of the development of master's programs, intended to provide graduates with the knowledge and skills to take a job in the quantum industry, while serving a much shorter timeline than a doctoral degree. Through a global survey, we identified 86 master's programs, with substantial growth since 2021. Over time master's have become increasingly interdisciplinary, organised by multiple faculties or through joint degree programs, and offer more hands-on experiences such as internships in companies. Information from program organisers and websites suggests that the intended career destinations of their graduates are a diverse range of industries, and therefore master's programs may be a boon to the industrialisation of quantum technologies. Finally, we identify a range of national efforts to grow the quantum workforce of many countries, "quantum program enhancements", which augment the content of existing study programs with quantum content. This may further contribute to the growth and viability of master's programs as a route to the quantum industry.

关键词： Prompt design   German   Oncology   Large language models   Retrival augmented generation   Natural language processing   Cancer registry   Pathology   Privacy   Computer science   Medical informatics   Knowledge extraction  

摘要： Structured oncological documentation is vital for data-driven cancer care, yet extracting clinical features from unstructured pathology reports remains challenging-especially in German healthcare, where strict data protection rules require local model deployment. This study evaluates open-source large language models (LLMs) for extracting oncological attributes from German pathology reports in a secure, on-premise setting. We created a gold-standard dataset of 522 annotated reports and developed a retrieval-augmented generation (RAG) pipeline using an additional 15,000 pathology reports. Five instruction-tuned LLMs (Llama 3.3 70B, Mistral Small 24B, and three SauerkrautLM variants) were evaluated using three prompting strategies: zero-shot, fewshot, and RAG-enhanced few-shot prompting. All models produced structured JSON outputs and were assessed using entity-level precision, recall, accuracy, and macro-averaged F1-score. Results show that Llama 3.3 70B achieved the highest overall performance (F1 > 0.90). However, when combined with the RAG pipeline, Mistral Small 24B achieved nearly equivalent performance, matching Llama 70B on most entity types while requiring significantly fewer computational resources. Prompting strategy significantly impacted performance: few-shot prompting improved baseline accuracy, and RAG further enhanced performance, particularly for models with fewer than 24B parameters. Challenges remained in extracting less frequent but clinically critical attributes like metastasis and staging, underscoring the importance of retrieval mechanisms and balanced training data. This study demonstrates that open-source LLMs, when paired with effective prompting and retrieval strategies, can enable high-quality, privacy-compliant extraction of oncological information from unstructured text. The finding that smaller models can match larger ones through retrieval augmentation highlights a path toward scalable, resource-efficient deployment in German clinical s

关键词： Waste tire   Pyrolysis   Plastic waste   BTX compounds   D-limonene  

摘要： The accumulation of waste tires (WT) poses a significant environmental challenge due to the rubbery structure, which makes them difficult to store, digest, or recycle in typical waste recycling facilities. Thermal degradation of WT has often been investigated as a potential waste-to-energy approach, but products obtained from the pyrolysis of tires render them not suitable for direct use. Therefore, this research highlights products and degradation behavior of WT pyrolysis and their co-pyrolysis with plastic wastes such as polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET). The integrated TGA-IR-MS system effectively mimics real-life thermal degradation and gas evolution processes by sequentially analyzing the decomposition behavior, gaseous emissions, and final pyrolysis byproducts, which resemble those found in large-scale industrial thermal recycling systems. The results revealed that plastic waste alters the thermal profile of WT, reduces residue formation, and promotes the conversion of D-limonene into BTX compounds (benzene, toluene, xylene) via radical mechanisms. Moreover, the experimental setup was modeled using COMSOL to understand the temperature profile. Outcomes reported herein address multiple Sustainable Development Goals (SDGs), specifically related to affordable and clean energy and sustainable cities (7, 11, and 13).