Near-IR sensitization of wide band gap oxide semiconductor by axially anchored Si-naphthalocyanines

Near-IR dye sensitized solar cells are very interesting due to their potential applications in panchromatic cells, semi-transparent windows and in tandem cells. In this work we show the utilization of axially anchored Si-naphthalocyanine dye in the spectral sensitization of TiO2 nanostructured photoelectrodes. We report the first successful evaluation of a naphthalocyanine in the production of sensitized photocurrent with maximum incident photon to current efficiency (IPCE) at λ 790 n

A novel conceptual design method for aviation PMSG based on thermal modeling

A multi-disciplinary optimization design method for permanent magnet synchronous generators based on thermal modeling is proposed in this paper. The complex coupling among the thermal, electromagnetic, and mechanical systems and the difficulties in optimization with conflicting objectives of multiple disciplines has been studied. Firstly, a multi-disciplinary design optimization model is established based on the coupling relationships between the thermal, electromagnetic, and mechanical performance of permanent magnet synchronous generators. Then, optimization objectives are set as low temperature-rise, low volume and weight, and sizeable electromagnetic size. The critical parameters in the thermal, electromagnetic, and mechanical systems of the generators are considered decision variables. The particle swarm optimization algorithm is selected as a multi-objective problem-solving algorithm to support the multi-disciplinary optimization of thermal motor design. Based on thermal modeling, a high disciplinary coupling and high physical fidelity concept design method for aerospace permanent magnet synchronous generators is presented. This conceptual design method can effectively reduce the design cost of aerospace generators, shorten the development cycle, and promote the design and development of aerospace permanent magnet generators

MLLM-DataEngine: An Iterative Refinement Approach for MLLM

Despite the great advance of Multimodal Large Language Models (MLLMs) in both instruction dataset building and benchmarking, the independence of training and evaluation makes current MLLMs hard to further improve their capability under the guidance of evaluation results with a relatively low human cost. In this paper, we propose MLLM-DataEngine, a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop iteration, the MLLM-DataEngine first analyze the weakness of the model based on the evaluation results, then generate a proper incremental dataset for the next training iteration and enhance the model capability iteratively. Compared with previous data collection methods which are separate from the benchmarking, the data generated by MLLM-DataEngine shows better targeting, quality, and correctness. For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts the ratio of different types of data within each incremental dataset based on the benchmarking results. For quality, we resort to GPT-4 to generate high-quality data with each given data type. For correctness, prompt design is critical for the data generation results. Rather than previous hand-crafted prompt, we propose an Interactive Prompt Optimization strategy, which optimizes the prompt with the multi-round interaction between human and GPT, and improve the correctness of generated data greatly. Through extensive experiments, we find our MLLM-DataEngine could boost the MLLM capability in a targeted and automatic manner, with only a few human participation. We hope it could be a general solution for the following MLLMs building. The MLLM-DataEngine has been open-sourced and is now available at https://github.com/opendatalab/MLLM-DataEngine.Comment: Code and models are available at https://github.com/opendatalab/MLLM-DataEngin

DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data

In the era of artificial intelligence, the diversity of data modalities and annotation formats often renders data unusable directly, requiring understanding and format conversion before it can be used by researchers or developers with different needs. To tackle this problem, this article introduces a framework called Dataset Description Language (DSDL) that aims to simplify dataset processing by providing a unified standard for AI datasets. DSDL adheres to the three basic practical principles of generic, portable, and extensible, using a unified standard to express data of different modalities and structures, facilitating the dissemination of AI data, and easily extending to new modalities and tasks. The standardized specifications of DSDL reduce the workload for users in data dissemination, processing, and usage. To further improve user convenience, we provide predefined DSDL templates for various tasks, convert mainstream datasets to comply with DSDL specifications, and provide comprehensive documentation and DSDL tools. These efforts aim to simplify the use of AI data, thereby improving the efficiency of AI development

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.Postprint (published version

Deliquescence of NaCl–NaNO(3), KNO(3)–NaNO(3), and NaCl–KNO(3 )salt mixtures from 90 to 120°C

We conducted reversed deliquescence experiments in saturated NaCl–NaNO(3)–H(2)O, KNO(3)–NaNO(3)–H(2)O, and NaCl–KNO(3)–H(2)O systems from 90 to 120°C as a function of relative humidity and solution composition. NaCl, NaNO(3), and KNO(3 )represent members of dust salt assemblages that are likely to deliquesce and form concentrated brines on high-level radioactive waste package surfaces in a repository environment at Yucca Mountain, NV. Discrepancy between model prediction and experiment can be as high as 8% for relative humidity and 50% for dissolved ion concentration. The discrepancy is attributed primarily to the use of 25°C models for Cl–NO(3 )and K–NO(3 )ion interactions in the current Yucca Mountain Project high-temperature Pitzer model to describe the nonideal behavior of these highly concentrated solutions

Association of adiposity and mental health functioning across the lifespan:Findings from understanding society (The UK household longitudinal study)

Background: Evidence on the adiposity-mental health associations is mixed, with studies finding positive, negative or no associations, and less is known about how these associations may vary by age. Objective: To examine the association of adiposity -body mass index (BMI), waist circumference (WC) and percentage body fat (BF%)- with mental health functioning across the adult lifespan. Methods: Data from 11,257 participants (aged 18+) of Understanding Society: the UK Household Longitudinal Study (waves 2 and 3, 5/2010-7/2013) were employed. Regressions of mental health functioning, assessed by the Mental Component Summary (MCS-12) and the General Health Questionnaire (GHQ-12), on adiposity measures (continuous or dichotomous indicators) were estimated adjusted for covariates. Polynomial age-adiposity interactions were estimated. Results: Higher adiposity was associated with poorer mental health functioning. This emerged in the 30s, increased up to mid-40s (all central adiposity and obesity-BF% measures) or early 50s (all BMI measures) and then decreased with age. Underlying physical health generally accounted for these associations except for central adiposity, where associations remained statistically significant from the mid-30s to50s. Cardiovascular, followed by arthritis and endocrine, conditions played the greatest role in attenuating the associations under investigation. Conclusions: We found strong age-specific patterns in the adiposity-mental health functioning association that varied across adiposity measures. Underlying physical health had the dominant role in attenuating these associations. Policy makers and health professionals should target increased adiposity, mainly central adiposity, as it is a risk factor for poor mental health functioning in those aged between mid-30s to 50 years

The Effect of Micrococcal Nuclease Digestion on Nucleosome Positioning Data

Eukaryotic genomes are packed into chromatin, whose basic repeating unit is the nucleosome. Nucleosome positioning is a widely researched area. A common experimental procedure to determine nucleosome positions involves the use of micrococcal nuclease (MNase). Here, we show that the cutting preference of MNase in combination with size selection generates a sequence-dependent bias in the resulting fragments. This strongly affects nucleosome positioning data and especially sequence-dependent models for nucleosome positioning. As a consequence we see a need to re-evaluate whether the DNA sequence is a major determinant of nucleosome positioning in vivo. More generally, our results show that data generated after MNase digestion of chromatin requires a matched control experiment in order to determine nucleosome positions

Structural basis for DNA damage-induced phosphoregulation of MDM2 RING domain

Phosphorylation of MDM2 by ATM upon DNA damage is an important mechanism for deregulating MDM2, thereby leading to p53 activation. ATM phosphorylates multiple residues near the RING domain of MDM2, but the underlying molecular basis for deregulation remains elusive. Here we show that Ser429 phosphorylation selectively enhances the ubiquitin ligase activity of MDM2 homodimer but not MDM2-MDMX heterodimer. A crystal structure of phospho-Ser429 (pS429)-MDM2 bound to E2–ubiquitin reveals a unique 310-helical feature present in MDM2 homodimer that allows pS429 to stabilize the closed E2–ubiquitin conformation and thereby enhancing ubiquitin transfer. In cells Ser429 phosphorylation increases MDM2 autoubiquitination and degradation upon DNA damage, whereas S429A substitution protects MDM2 from auto-degradation. Our results demonstrate that Ser429 phosphorylation serves as a switch to boost the activity of MDM2 homodimer and promote its self-destruction to enable rapid p53 stabilization and resolve a long-standing controversy surrounding MDM2 auto-degradation in response to DNA damage