Early adulthood students are at the peak of their rapid psychological development and face an important transition from "mere cognition" to "social cognition" and experiences or guidance during this period are particularly important for psychological quality. From a positive psychology perspective, this study examines the impact of art and design competencies in college students on their positive psychological qualities and analyzes the mediating roles of teacher support and learning adaptability. Using the Art and Design Competencies Scale for College Students, the Chinese College Students' Positive Psychological Qualities Scale, the Learning Adaptability Scale, and the Teacher Support Scale, a survey was conducted among 950 college students majoring in art. (1) Art and design competencies significantly predicts both teacher support and learning adaptability (p < 0.01). Art and design competencies, teacher support, and learning adaptability can also simultaneously predict positive psychological qualities significantly (p < 0.01). (2) Teacher support has a mediating effect between art and design competencies and positive psychological qualities, with an effect ratio of 17.35 %. (3) Learning adaptability has a mediating effect between art and design competencies and positive psychological qualities, with an effect ratio of 4.89 %. (4) Both teacher support and learning adaptability mediate the relationship between art and design competencies and positive psychological qualities with a combined effect ratio of 22.02 %. College students' art and design competencies not only directly influences their positive psychological qualities but also indirectly affects them through the mediating roles of teacher support and learning adaptability. These findings highlight the critical role of art and design education in fostering students' psychological well-being and resilience. By enhancing teacher support and learning adaptability, educational institutions can better prepare students to face the challenges of early adulthood, contributing to their overall development and success. At the same time, the research informs the optimization plan for the art and design program.

Erasure-coded systems require simultaneous updating of data chunks and parity chunks, thereby leading to high input/output (I/O) overhead. Following these updates, the most recent data and parity deltas are dispersed randomly in the log, which results in prolonged recovery time. This study introduces a novel approach named MPLR, which is specifically designed to expedite erasure-coded data updating and recovery. MPLR uses machine learning to categorize files into non-read-only and read-only classes, and then performs updates based on these classifications. For non-read-only files, MPLR applies in-place updates for data chunks and log-based updates for parity chunks, while setting aside disk space adjacent to the parity chunk to minimize disk seeks. For read-only files, MPLR likewise employs a mixture of in-place data updates and log-based parity updates, although without reserving space next to parity chunks. In regard to data recovery, MPLR enhances speed by accessing the parity delta stored in the allocated disk space. To further boost recovery acceleration, MPLR reads the parity delta in parallel and reduces the volume of parity delta leveraging the shareable aspects of the parity delta. Experimental results confirm the efficiency of MPLR in significantly improving update and recovery performances, compared to the state-of-the-art approaches.

In erasure-coded storage systems, updating data necessitates parity updates to maintain data consistency, which leads to I/O amplification due to "write-after-read" operations. Additionally, the scattered storage of parity updates imposes significant disk seek overhead during data repair. To address these challenges, this paper proposes a Cognitive Update and Repair Method (CURM), which uses machine learning to classify files into write-only, read-only, and read-write categories, enabling customized update and repair strategies. For write-only and read-write files, CURM utilizes data difference and fine-grained I/O scheduling to reduce I/O overhead. Furthermore, CURM reserves disk space adjacent to parity chunks for read-write files, enabling efficient parallel reads and minimizing seek cost during repair. We implement CURM in a prototype storage system and evaluate its performance using real-world NFS and MSR workloads on a 25-node cluster. Experimental results show that CURM improves data update throughput by up to 82.52% and reduces data recovery time by up to 47.47%, while achieving lower storage overhead compared to state-of-the-art approaches including FL, PL, PLR, and PARIX. These results demonstrate CURM’s effectiveness in enhancing both update and recovery performance for large-scale erasure-coded storage systems.

Fine-grained image-text matching, which is pivotal to multimodal intelligence, has advanced semantic correspondence inference through inter-modal region-word aggregation. Despite the efficacy of this approach, it remains limited by its inability to accommodate the semantic associations of hard negative samples. To illustrate this point, consider the failure to leverage shared knowledge across multiple samples on analogous topics. This failure results in an inadequate capacity to differentiate hard negative samples. In this study, it is posited that the establishment of sample relationships facilitates the learning of semantic associations between different samples. This, in turn, enables the effective identification of subtle differences between hard negative samples, thereby enhancing the overall embedding process. The proposal of HiCoS-Net is the subject of this paper. The proposed model is a novel hierarchical inter-modal semantic network that learns robust embeddings through local-to-sample semantic interaction propagation. Specifically, at the local level, a dynamic graph attention mechanism is designed to achieve region-lexicon fine-grained interactions; at the sample level, an embedding similarity graph is constructed by combining the relational mapping matrix with the semantic matching matrix to explicitly model the topological associations and semantic coupling strengths of inter-modal samples. A substantial programme of experimentation is undertaken to validate the advantages of the proposed HiCoS-Net method. This has been demonstrated to achieve state-of-the-art image-text matching results on the public benchmark datasets Flickr30K and MS-COCO.