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Plantwide control system design for economically optimum operation of a C4 isomerization process is studied. The steady state degrees of freedom of a base case design are optimized for a given C4 fresh feed processing rate (Mode I) and maximum production (Mode II). At maximum production, the number of active constraints equal the steady state degrees of freedom (dof) exhausting all the available dof. From the set of active constraints, regulatory plantwide control structures, CS1 and CS2, that minimize the back-off from the economically dominant active constraints are synthesized along with a simple supervisory optimizing scheme to drive the process operation as close as possible to the active constraints. Quantitative results for the back- off necessary to avoid constraint limit violation during transients due to a ±10% feed composition change are reported. Comparison with a conventional plantwide control structure, CS3, where the fresh feed is flow controlled, shows that the maximum achievable throughput (profit) for CS2 is higher by ∼2% (> $1×106 per yr).

We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage the multimodal capabilities of Gemini to produce embeddings for arbitrary combinations of interleaved inputs across all these modalities that generalize well across a wide variety of tasks. Applying large-scale contrastive learning in a multi-task multi-stage training setup, we achieve state-of-the-art performance on key embedding benchmarks including unimodal, cross-modal, and multimodal retrieval spanning a diverse set of tasks. We show that our embedding model demonstrates strong performance (with a score of 62.9 R@1 on MSCOCO, 68.8 NDCG@10 on Vatex, 69.9 on MTEB multilingual and 84.0 on MTEB Code) across a variety of tasks surpassing the performance of specialized models. These unified capabilities make Gemini Embedding 2 a promising candidate for downstream use cases such as RAG, recommendation and search. Furthermore, its robust zero-shot performance across distinct fields - from astronomy and bioscience to fine arts and the culinary arts - establishes it as a highly reliable, out-of-the-box representation even for specialized domains.

We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledge from larger models via encoder-decoder initialization and geometric embedding distillation. We improve model robustness and expressiveness with a spread-out regularizer, and ensure generalizability by merging checkpoints from varied, optimized mixtures. Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual, English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art results. Notably, it outperforms prior top models, both proprietary and open, with fewer than 500M parameters, and provides performance comparable to models double its size, offering an exceptional performance-to-cost ratio. Remarkably, this lead persists when quantizing model weights or truncating embedding outputs. This makes EmbeddingGemma particularly well-suited for low-latency and high-throughput use cases such as on-device applications. We provide ablation studies exploring our key design choices. We release EmbeddingGemma to the community to promote further research.

In this report, we introduce Gemini Embedding, a state-of-the-art embedding model leveraging the power of Gemini, Google's most capable large language model. Capitalizing on Gemini's inherent multilingual and code understanding capabilities, Gemini Embedding produces highly generalizable embeddings for text spanning numerous languages and textual modalities. The representations generated by Gemini Embedding can be precomputed and applied to a variety of downstream tasks including classification, similarity, clustering, ranking, and retrieval. Evaluated on the Massive Multilingual Text Embedding Benchmark (MMTEB), which includes over one hundred tasks across 250+ languages, Gemini Embedding substantially outperforms prior state-of-the-art models, demonstrating considerable improvements in embedding quality. Achieving state-of-the-art performance across MMTEB's multilingual, English, and code benchmarks, our unified model demonstrates strong capabilities across a broad selection of tasks and surpasses specialized domain-specific models.

We tackle the problem of protein secondary structure prediction using a common task framework. This lead to the introduction of multiple ideas for neural architectures based on state of the art building blocks, used in this task for the first time. We take a principled machine learning approach, which provides genuine, unbiased performance measures, correcting longstanding errors in the application domain. We focus on the Q8 resolution of secondary structure, an active area for continuously improving methods. We use an ensemble of strong predictors to achieve accuracy of 70.7% (on the CB513 test set using the CB6133filtered training set). These results are statistically indistinguishable from those of the top existing predictors. In the spirit of reproducible research we make our data, models and code available, aiming to set a gold standard for purity of training and testing sets. Such good practices lower entry barriers to this domain and facilitate reproducible, extendable research.

BackgroundThe physical characteristics which are known to affect the ovarian reserve are age, body mass index (BMI), occupational exposures, age at menarche and menstrual cycle length. A correlation between different physical characteristics and the ovarian reserve will help to identify areas which need to be tackled to increase the chances of fertility of women in India.MethodsIn this retrospective, observational study, namely the MOTHER Study, data of women between 18 and 45 years of age, attending the selected fertility centers across different states in India were taken for evaluation. Demographic information along with information on factors potentially related to fertility like age of menarche, menstrual cycle length and occupational factors were collected by review of medical records at screening visit. Most recent AMH assay and antral follicle count (AFC) where the subject has not taken any contraceptives 12 months prior to the test were collected.ResultsAge of woman, years of marriage, years of infertility and smoking have shown effect on ovarian reserve testing like AMH and AFC. The other physical characteristics which were evaluated and considered to affect the ovarian reserve like body mass index BMI, occupational exposures, age at menarche and menstrual cycle length have not shown statistically significant correlation.ConclusionAge of woman and years of infertility are inversely proportional to ovarian reserve markers, namely AMH and AFC. Addictions like smoking and alcohol affect ovarian reserve.