With the deterioration of the environment and the depletion of fossil fuel energy, renewable energy has attracted worldwide attention because of its continuous availability from nature. Despite this continuous availability, the uncertainty of intermittent power is a problem for grid dispatching. This paper reports on a study of the scheduling and optimization of microgrid systems for photovoltaic (PV) power and electric vehicles (EVs). We propose a mathematical model to address the uncertainty of PV output and EV charging behavior, and model scheduling optimization that minimizes the economic and environmental cost of a microgrid system. A semi-infinite dual optimization model is then used to deal with the uncertain variables, which can be solved with a robust optimization algorithm. A numerical case study shows that the security and stability of the solution obtained by robust optimization outperformed that of stochastic optimization.

The environmental crisis has prompted the development of electric vehicles as a green and environmentally friendly mode of travel. Since a reasonable layout of electric vehicle (EV) charging stations is the prerequisite for developing the EV industry, obtaining an optimal and efficient EV charging station planning scheme is a key issue. Although the Chinese government has carried out a plan to build EV charging piles in residential and working places, it cannot properly fulfill the task of matching the charging needs for public transportation vehicles such as electric taxis (ETs). How to evaluate the performance of fast charging stations (FCSs) and how to help find the optimal ET charging station planning scheme are new challenges. In this paper, an improved destination selection model is proposed to simulate the ET operation system and to help find the optimal ET charging station size with statistical analysis based on the charging need prediction. A numerical case study shows that the proposed method can address ET charging behavior well and can help to statistically determine the size of each ET charging station, which should satisfy the constraints on the preset proportion of the ET charging service requests.

The rise and development of digital humanities have brought new challenges and possibilities to the archival narrative. This article aims to explore the storified narrative path to awake photo archives in digital humanities. The authors clarify plot and plot unit definitions first, then focus on photo archives to analyze the path realization of the storified narrative. The authors also take the photo archives of Chinese Archivist Mr Wu Baokang, an example for a more specific explanation, which Mrs Wu Jiaqing authorizes. The plot-driven storified narrative path of photo archives involves two parts: story elements deconstruction and plot units representation, narrative clues construction, and plot generation. The plot-driven, storified narrative path can fully play the advantages of storifying and promoting the critical development of archival narrative under the research paradigm of digital humanities. By exploring the storified narrative path of archives, it is helpful to promote the activation, in-depth utilization, and widespread dissemination of archives, promote the integration of archival narrative with digital humanities, and provide support for digital humanities research.

As a critical material manifestation of Chinese culture, silk has both important and significant cultural representational value as well as multidimensional information worth. Silk archives are an important carrier of silk culture and memory. The Archives of Suzhou Silk are the most complete archives of silk in existence and document the heritage of China and even the world beyond. To better tell the story of the Archives of Suzhou Silk in a digital environment, this article adopts a case study method, taking the Taffeta Archives of the Suzhou Dongwu Silk Factory as an example. It approaches these archives from the perspective of archival science and narratology, discusses the digital storytelling framework of the Archives of Suzhou Silk, and builds a narrative-oriented "story material discovery–story element organization–digital story generation" path and an object-oriented "digitization–datamation–graphing–storytelling" path to build content-based and structure-based narrative graphs and narrative space applications. The construction of the digital storytelling framework can fully show the value of the archival documentary heritage of Suzhou silk, allowing it to better tell its story and providing new thinking for the sustainable development of the Memory of the World Documentary Heritage.

Purpose: This study examines emerging views in Chinese archival science on the value forms of “data element value” in records. It explores how this value is manifested and realized, which is key to identifying and utilizing archival value in new contexts. Although interest in archival data elements is growing, clear definitions and practical approaches remain limited, and theoretical research is still in its early stages. Design/methodology/approach: This study adopts a literature analysis approach and case study methodology, grounded in the records continuum theory and the characteristics of record value realization. It constructs a theoretical framework for releasing the value of archival data elements, which is further illustrated through multiple narrative case studies and empirical evidence. Findings: Building upon a clarified conceptual understanding of the release of the value of archival data elements, and drawing on theories related to records movement and value movement, this study constructs a cyclical model for the release of the value of archival data elements. From the perspective of “records movement”, it analyzes the three stages of the elementization of archival data; from the perspective of “value movement”, it identifies three distinct pathways through which archival data become value-laden. Furthermore, it explores key initiatives and functional objectives of archival data elements within three major operational mechanisms: intra-departmental circulation, intra-organizational (small) circulation, and societal (large) circulation. Originality/value: The study clarifies the multifaceted value of archival data elements and offers a reference for their practical application.

As Multimodal Large Language Models (MLLMs) expand the scope of retrieval-augmented generation, recommendation, and multimedia search, audio retrieval is expected to become a dependable retrieval component. Yet existing systems struggle to reconcile lexical precision, non-verbal acoustic evidence, and efficient, transparent retrieval. Current approaches mainly follow two paradigms. Cascaded pipelines transcribe audio with automatic speech recognition (ASR) and retrieve over text. They inherit the strengths of lexical matching, but amplify recognition errors and systematically discard non-verbal cues such as acoustic events and speaking style. Dense retrievers bypass transcription, yet they compress each clip into a single embedding, making relevance difficult to inspect and incurring non-trivial indexing and query-time overhead as collections grow. To address these limitations, we propose LSAR (Learned Sparse Audio Retrieval), the first learned sparse retrieval framework for audio that maps audio directly into a sparse lexical space compatible with inverted-index search. LSAR employs cross-modal sparse alignment and multiple complementary branches to model spoken content, acoustic context, and logic associations, producing an indexable representation with explicit term activations. At inference, retrieval proceeds directly from audio without an ASR transcription stage. Experiments on diverse benchmarks covering speech, captioning, and audio question answering show that LSAR effectively maps continuous audio representations into a sparse lexical space. Acting as a robust first-stage retriever, it enables high-recall candidate retrieval with low latency to significantly narrow the search space, while offering keyword-level interpretability. Overall, LSAR introduces a new retrieval paradigm for audio, establishing an interpretable, index-friendly primitive for real-time multimodal RAG and beyond.