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is a production code typically associated with Japanese adult media. Within this specific context, it refers to a video title released under the

label, a well-known producer in the "JAV" (Japanese Adult Video) industry. Product Overview Production Label: Madonna (official site) Release Date: Generally released around October 2021. Primary Performer: Features the actress Yua Mikami (one of the industry's most prominent figures). Theme/Genre:

The title typically falls under the "Drama" or "Married Woman" (Inuma) sub-genres common to the Madonna label's aesthetic, which often focuses on high-production values and narrative-driven scenarios. Distribution and Verification

If you are looking for specific details, trailers, or official purchase options, you can verify this code on major Japanese media retail platforms: DMM.co.jp / FANZA

The primary digital storefront for verifying release dates and actress credits. juq373

The international portal for the same content, often providing English-language metadata for specific codes like JUQ-373. Content Advisory As this code refers to Adult Content (18+)

, access to official sites or detailed write-ups on these platforms will require age verification. from the same studio or biographical information about the lead performer?

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Based on the identifier provided, refers to a specific adult film title released by the Japanese production company The film, titled

真夏の義母は禁断の果実 理性が吹き飛んだ母子が汗と愛液にまみれながら中出し相姦に溺れ

(roughly translated as "The Stepmother in Midsummer is Forbidden Fruit"), features the actress Mirei Tojo (also known as Toujou Mirei).

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1. Overview and General Assessment

In this manuscript, the authors propose a time-series foundation model specifically tailored for building energy forecasting. The study addresses a critical challenge in the energy sector: the scarcity of high-quality labeled data for specific buildings and the need for models that can generalize well across different building types and climatic zones. The authors adapt a transformer-based architecture (likely inspired by recent advances in foundation models like TimesNet or PatchTST) to leverage large-scale pre-training on diverse building datasets, followed by fine-tuning on downstream tasks.

The topic is timely and highly relevant to the readership of the journal. The transition from specialized, single-building models to generalist foundation models represents a significant shift in the paradigm of energy forecasting. However, while the conceptual framework is sound, the manuscript currently suffers from insufficient detail regarding the pre-training protocol and a lack of rigorous benchmarking against state-of-the-art (SOTA) statistical and machine learning baselines.

Strengths:

• Novelty: The application of a "foundation model" approach (pre-train and fine-tune) specifically for building energy is a relatively underexplored and high-potential area.
• Scalability: The proposed methodology demonstrates promising scalability, addressing the "cold-start" problem common in new building energy management projects.
• Dataset Aggregation: The compilation of a large-scale, multi-source dataset for pre-training is a valuable contribution in itself.

Weaknesses:

• Lack of Architecture Detail: The specific modifications made to standard transformer architectures to handle building energy features (e.g., exogenous variables like weather and occupancy) are not clearly articulated.
• Benchmark Selection: The comparison baselines appear slightly outdated. The authors must compare against modern deep learning timeseries models (e.g., N-BEATS, TiDE, PatchTST) rather than just traditional LSTM/GRU or simple statistical models.
• Data Leakage Concerns: There is ambiguity regarding how the temporal splits were managed during the fine-tuning phase, raising potential concerns about data leakage.