Financial Comprehensive Appraisal Empirical Research on Grey Relational Model ——Testing in Listed Companies in Real Estate Industry in China

The financial comprehensive appraisal method plays an essential role in decision-making for investors and reflecting real company’s financial comprehensive situation. Because of the grey characteristics of financial information, this paper constructs a grey relational model with a index system of financial comprehensive appraisal by grey relation theory. It is applied to listed companies in real estate industry in China and tested in the both dynamic and static state, and then the result is that the ranking of financial comprehensive evaluation of the listed companies in the real estate industry is almost as same as the real ranking. It shows that the financial comprehensive appraisal method on the grey relational analysis is a kind of effective and good method of evaluating company’s financial comprehensive situation

Lithium-Excess Research of Cathode Material Li2MnTiO4 for Lithium-Ion Batteries

Lithium-excess and nano-sized Li2+xMn1−x/2TiO4 (x = 0, 0.2, 0.4) cathode materials were synthesized via a sol-gel method. The X-ray diffraction (XRD) experiments indicate that the obtained main phases of Li2.0MnTiO4 and the lithium-excess materials are monoclinic and cubic, respectively. The scanning electron microscope (SEM) images show that the as-prepared particles are well distributed and the primary particles have an average size of about 20–30 nm. The further electrochemical tests reveal that the charge-discharge performance of the material improves remarkably with the lithium content increasing. Particularly, the first discharging capacity at the current of 30 mA g−1 increases from 112.2 mAh g−1 of Li2.0MnTiO4 to 187.5 mAh g−1 of Li2.4Mn0.8TiO4. In addition, the ex situ XRD experiments indicate that the monoclinic Li2MnTiO4 tends to transform to an amorphous state with the extraction of lithium ions, while the cubic Li2MnTiO4 phase shows better structural reversibility and stability

Developing urban residential reference buildings using clustering analysis of satellite images

Built-up areas tend to comprise a variety of buildings with diverse and complex shapes, functions and construction characteristics. This variety is the source of significant challenges when calculating building energy use at the building stock level. Moreover, the process of developing stock models usually requires large amounts of data that are frequently scarce, nonexistent or at least not publicly available. Under these circumstances, defining a limited set of reference buildings representing the stock is useful to study the actual energy consumption and the potential effects of different energy conservation measures. This paper presents a new method for developing typical residential reference buildings at district level for bottom-up energy modeling purposes. By means of widely and freely available satellite images, an information database of building shapes is created and a clustering analysis of the geometrical features is performed to define a number of archetypes representative of the heating and cooling energy demand of the district. The method is tested and demonstrated through the case study of the Yuzhong District in Chongqing (China) by comparing the Energy Use Intensity (EUI) of the archetypes derived in this way against detailed dynamic simulations. Results show very small differences in the estimated stock energy consumption (+0.03% in heating energy consumption and +2.97% in cooling energy consumption)

Critical Intersections through Poetry in a TESOL & World Language Graduate Education Program

In this studio submission, Language Education students who took one or more poetry writing courses along with their instructor share one poem draft and critical reflection, noting the political climate of the work co-produced and inquiry regarding the impact of producing creative work as reflexive, critical teacher education scholarship. Together they draw a context and implications for creative and critical teacher education through shared poetry writing

SiRA: Sparse Mixture of Low Rank Adaptation

Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks. Most previous works considers adding the dense trainable parameters, where all parameters are used to adapt certain task. We found this less effective empirically using the example of LoRA that introducing more trainable parameters does not help. Motivated by this we investigate the importance of leveraging "sparse" computation and propose SiRA: sparse mixture of low rank adaption. SiRA leverages the Sparse Mixture of Expert(SMoE) to boost the performance of LoRA. Specifically it enforces the top $k$ experts routing with a capacity limit restricting the maximum number of tokens each expert can process. We propose a novel and simple expert dropout on top of gating network to reduce the over-fitting issue. Through extensive experiments, we verify SiRA performs better than LoRA and other mixture of expert approaches across different single tasks and multitask settings

Transcriptomics-based analysis of genes related to lead stress and their expression in the roots of Pogonatherum crinitum

Revealing plants’ tolerance and transport genes to heavy metal stress play an important role in exploring the potential of phytoremediation. Taking the heavy metal lead (Pb) hyperaccumulator plant Pogonatherum crinitum (Thunb.) Kunth as the research object, a hydroponic simulation stress experiment was set up to determine the physiological indicators such as antioxidant enzymes and non-enzymatic antioxidants in the roots of P. crinitum under different Pb concentrations (0, 300, 500, 1000, 2000 mg·L-1). RNA-Seq was performed, the Unigenes obtained by transcriptome sequencing were enriched and annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, and the differential expression genes (DEGs) of root were screened and verified by quantitative real-time polymerase chain reaction (qRT-PCR). The results are as follows: with the increase of Pb concentration, superoxide dismutase (SOD), catalase (CAT), and ascorbic acid (AsA) content increased. Peroxidase (POD), malondialdehyde (MDA), and ascorbic acid–glutathione (AsA-GSH) cycles showed low promotion with high inhibition. A total of 38.21 Gb of bases were obtained by transcriptome sequencing, and the base quality of each sample reached Q20 and Q30, accounting for 90%, making the sequencing results reliable. Combined with transcriptome sequencing, functional annotation, and qRT-PCR validation results, 17 root Pb-tolerant genes of P. crinitum were screened out, which were related to antioxidation, transportation, and transcription functions. Moreover, qRT-PCR verification results under different Pb stress concentrations were consistent with the transcriptome sequencing results and changes in physiological indicators. In brief, the root of P. crinitum can adapt to the Pb stress environment by up-regulating the expression of related genes to regulate the physiological characteristics

Towards Large-Scale RFID Positioning:A Low-cost, High-precision Solution Based on Compressive Sensing

RFID-based positioning is emerging as a promising solution for inventory management in places like warehouses and libraries. However, existing solutions either are too sensitive to the environmental noise, or require deploying a large number of reference tags which incur expensive deployment cost and increase the chance of data collisions. This paper presents CSRP, a novel RFID based positioning system, which is highly accurate and robust to environmental noise, but relies on much less reference tags compared with the state-of-the-art. CSRP achieves this by employing an noise-resilient RFID fingerprint scheme and a compressive sensing based algorithm that can recover the target tag's position using a small number of signal measurements. This work provides a set of new analysis, algorithms and heuristics to guide the deployment of reference tags and to optimize the computational overhead. We evaluate CSRP in a deployment site with 270 commercial RFID tags. Experimental results show that CSRP can correctly identify 84.7% of the test items, achieving an accuracy that is comparable to the state-of-the-art, using an order of magnitude less reference tags

Tumor‐derived exosomal PD-L1: a new perspective in PD-1/PD-L1 therapy for lung cancer

Exosomes play a crucial role in facilitating intercellular communication within organisms. Emerging evidence indicates that a distinct variant of programmed cell death ligand-1 (PD-L1), found on the surface of exosomes, may be responsible for orchestrating systemic immunosuppression that counteracts the efficacy of anti-programmed death-1 (PD-1) checkpoint therapy. Specifically, the presence of PD-L1 on exosomes enables them to selectively target PD-1 on the surface of CD8+ T cells, leading to T cell apoptosis and impeding T cell activation or proliferation. This mechanism allows tumor cells to evade immune pressure during the effector stage. Furthermore, the quantification of exosomal PD-L1 has the potential to serve as an indicator of the dynamic interplay between tumors and immune cells, thereby suggesting the promising utility of exosomes as biomarkers for both cancer diagnosis and PD-1/PD-L1 inhibitor therapy. The emergence of exosomal PD-L1 inhibitors as a viable approach for anti-tumor treatment has garnered significant attention. Depleting exosomal PD-L1 may serve as an effective adjunct therapy to mitigate systemic immunosuppression. This review aims to elucidate recent insights into the role of exosomal PD-L1 in the field of immune oncology, emphasizing its potential as a diagnostic, prognostic, and therapeutic tool in lung cancer

Identification of Target Genes at Juvenile Idiopathic Arthritis GWAS Loci in Human Neutrophils

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease among children which could cause severe disability. Genomic studies have discovered substantial number of risk loci for JIA, however, the mechanism of how these loci affect JIA development is not fully understood. Neutrophil is an important cell type involved in autoimmune diseases. To better understand the biological function of genetic loci in neutrophils during JIA development, we took an integrated multi-omics approach to identify target genes at JIA risk loci in neutrophils and constructed a protein-protein interaction network via a machine learning approach. We identified genes likely to be JIA risk loci targeted genes in neutrophils which could contribute to JIA development