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关键词： Artificial intelligence (AI)   Social science   Computer science   Students' perception   Education   Foreign language learning  

摘要： Artificial intelligence (AI) has gained acceptance in the field of education. Nevertheless, existing research on AI in education, particularly in foreign language (FL) learning and teaching, is notably limited in scope and depth. In the present study, we addressed this research gap by investigating social and computer science students' perceptions of the integration and use of AI-based technologies in education, focusing specifically on foreign language teaching. Using an online questionnaire, we analysed factors such as students' field of study, gender differences, and the type of AI used. The questionnaire included statements categorised into thematic clusters, with responses measured on a five-point Likert scale. Statistical analysis, including chi-square tests and Cohen's d, revealed that individuals studying computer science, males, and supporters of generative AI are more likely to use AI tools for educational purposes. They perceive fewer barriers to the integration of AI into FL education. Social science students and women are less likely to use AI tools in FL education and express scepticism about their potential to improve academic outcomes. They tend to be more critical or cautious regarding the role of AI in FL education. They view AI as a valuable tool that enhances the learning experience but, at the same time, recognise the irreplaceable role of human teachers. The study highlights the need for targeted educational initiatives to address gender and disciplinary gaps in AI adoption, promote informed discussions on AI in education, and develop balanced AI integration strategies to improve FL learning. These findings suggest educators and policymakers should implement comprehensive AI training programs and ethical guidelines for responsible AI use in (FL) education.

关键词： Generative artificial intelligence(GAI)   Elementary education   Concept explanations   Computer science education   Perceptual differences  

摘要： The educational application of Generative AI (GAI) has garnered significant interest, sparking discussions about the pedagogical value of GAI-generated content. This study investigates the perceived effectiveness of concept explanations produced by GAI compared to those created by human teachers, focusing on programming concepts of sequence, selection, and iteration. The research also explores teachers' and students' ability to discern the source of these explanations. Participants included 11 teachers and 70 sixth-grade students who were presented with concept explanations created or generated by teachers and ChatGPT. They were asked to evaluate the helpfulness of the explanations and identify their source. Results indicated that teachers found GAI-generated explanations more helpful for sequence and selection concepts, while preferring teacher-created explanations for iteration (χ2(2, N = 11) = 10.062, p = .007, ω = .595). In contrast, students showed varying abilities to distinguish between AI-generated and teacher-created explanations across concepts, with significant differences observed (χ2(2, N = 70) = 22.127, p < .001, ω = .399). Notably, students demonstrated difficulty in identifying the source of explanations for the iteration concept (χ2(1, N = 70) = 8.45, p = .004, φ = .348). Qualitative analysis of open-ended responses revealed that teachers and students employed similar criteria for evaluating explanations but differed in their ability to discern the source. Teachers focused on pedagogical effectiveness, while students prioritized relatability and clarity. The findings highlight the importance of considering both teachers' and students' perspectives when integrating GAI into computer science education. The study proposes strategies for designing GAI-based explanations that cater to learners' needs and emphasizes the necessity of explicit AI literacy instruction. Limitations and future research directions are discussed, underlining the need for larger-

关键词： 党建工作   业务工作   立德   强能   创境  

摘要： 山东建筑大学计算机科学与技术学院党委将党建工作与业务工作深度融合,传承学院文化并拓展延伸,致力打造“E路同行”立体式特色党建体系,分为“E航天下”立德体系、“E心双环”强能体系、“E海无涯”创境体系三大模块进行框架设计。其中,“E航天下”立德体系是核心,旨在将基层党建与思想政治教育相结合;“E心双环”强能体系是保障,旨在将基层党建与学院科研教学水平、师生能力培养相融合;“E海无涯”创境体系是支撑,旨在打造学院育人环境。三者相辅相成,相互促进,共同构成“E路同行”品牌特色。

关键词： Sustainable development  

摘要： Goals number 2, 11 and 12 of the 17 sustainable development goals, enacted by the United Nations as part of the 2030 Agenda for sustainable development, aim to end hunger as a priority, create sustainable cities and above all encourage responsible consumption and production. With increasing world population and higher demand for food, we need to find ways of producing cheap sources of protein and lipid that may in turn be used as animal or aquaculture feed. A multitrophic system involving mealworm larvae (MWL, Tenebrio molitor) and black soldier fly larvae (BSFL, Hermetia illucens) was developed to transform fruit and vegetable kitchen waste into usable biomass. MWL, fed mainly on kitchen waste, reached an average prepupal length of 2.4 cm, fresh weight of 0.12 g and dry matter protein and lipid contents of 44.2% and 16.5% respectively, with an average specific growth rate (SGR) of 2.2%/day and a feed conversion ratio (FCR) of 7.9. Conversely, BSFL fed on a variety of feeds, including MWL frass, kitchen waste and oats, had an average prepupal length of 1.3 cm, fresh weight of 0.16 g and dry matter protein and lipid contents of 41.4% and 26.3% respectively, with an average SGR and FCR of 4.3%/day and 8.9 respectively. The BSFL fed MWL frass obtained some of highest SGR and lowest FCR values, with one group achieving 7.5%/day and 2.9 respectively. This investigation has demonstrated the feasibility of a multi-trophic production system using kitchen waste to feed MWL whose frass was in turn used to feed BSFL thereby producing protein- and lipid-rich biomass that can serve as animal or aquaculture feed. © The Author(s) 2024.

摘要： To identify collaboration trends with coauthors, this paper elaborates a theoretical framework by introducing a measure to quantify exploration of the author in joining teams of coauthors with respect to the extreme exploration possibilities. Using the clustering coefficient, we gauge the team exploration from the author-centric vista evaluating configuration values of the ego networks. This value is normalized with respect to the maximum exploration possibilities for the author facilitating us to derive a measure, viz., the team exploration score for the team exploration strategy. We further derive a dynamical version of this measure. The average profiles of the exploration strategies are compared for the authors from the USA, England, and India publishing in a rapidly growing and collaboration-extensive field, viz. artificial intelligence in computer science, in the time window from 1990 to 2020. The bibliometric data are sourced from the Clarivate Web of Science. Configuration values are evaluated in the ascending year of publications in year-long time windows to compute the team exploration score for each author. Our analysis shows that the annually averaged profiles of authors corresponding to the three countries are almost constantly increasing toward high team exploration scores. Also, in the career-averaged profiles, authors publishing more than 20 papers have mostly adopted exploratory strategies.

关键词： Chemistry   Biological sciences   Computer science  

摘要： The rise of antibiotic resistance necessitates effective alternative therapies. Antimicrobial peptides (AMPs) are promising due to their broad inhibitory effects. This study focuses on predicting the minimum inhibitory concentration (MIC) of AMPs against whom-priority pathogens: Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853. We developed a comprehensive regression model integrating AMP sequence-based and genomic features. Using eight AI-based architectures, including deep learning with protein language model embeddings, we created an ensemble model combining bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN), and multi-branch model (MBM). The ensemble model showed superior performance with Pearson correlation coefficients of 0.756, 0.781, and 0.802 for the bacterial strains, demonstrating its accuracy in predicting MIC values. This work sets a foundation for future studies to enhance model performance and advance AMP applications in combating antibiotic resistance.

关键词： Applied sciences   Artificial intelligence   Computer science  

摘要： Most of the existing image encryption algorithms encrypt images as meaningless cryptographic images, which can easily attract the attention of attackers during transmission. To address this problem, scholars have proposed to hide the cipher image in a meaningless carrier image. However, larger carrier images are often required, which occupy more bandwidth. In order to solve this problem, this paper realizes embedding the color secret image into the carrier image whose size is equal to or even smaller than the original image by combining the chaotic compressed sensing model. First of all, the original image is sparsely processed using discrete wavelet transform. Then the time varying delay chaotic model is used to generate pseudo random sequence and then the measurement matrix is constructed to compress and encrypt the image. In the end, using singular value decomposition to achieve image embedding, the carrier image carrying information is obtained.

关键词： Applied sciences   Computer science   Engineering  

摘要： This article proposes a secure communication enhancement scheme based on face detection and chaotic partition permutation. The scheme uses edge detection technology to detect facial information, which is then used as an encryption object. The hash value of the plaintext image is extracted as the secret key to the chaotic sequence generated by the chaotic system. Then a series of encryption operations are performed on the face image to obtain the final ciphertext image. In this article, two chaotic systems are used to generate pseudo-random chaotic sequences for different encryption steps. The initial key is computed by combining the hash function of the image and external parameters. The experimental results and security analysis show that the algorithm has excellent encryption effectiveness and security performance against various typical attacks.

摘要： Breast cancer is the leading cause of death and also one of the most invasive types of cancer among women worldwide. This cancer occurs when cells in the breast begin to grow uncontrollably or spread throughout the body. Treatment efforts in the form of early diagnosis are the only salvation to reduce deaths from breast cancer. An expert system that is able to accurately classify breast cancer is an important task in medical diagnosis. Approaches using computing with various approaches and algorithms with different research results have been widely used. The purpose of this study is to review several studies that have been carried out using instruments in the form of aspects and categories to conduct analysis. This study is to see the extent to which the use of computer technology is used to diagnose breast cancer