Bank Credit Strategy Model Based on AHP-Fuzzy Comprehensive Evaluation

Credit risk control and credit strategy formulation of medium and micro enterprises have always been important strategic issues faced by commercial banks. Banks usually make corporate loan policies based on the credit degree, the information of trading bills and the relationship of supply-demand chain of the enterprise. In this paper, we established the AHP-Fuzzy comprehensive evaluation model for quantifying enterprise credit risk. Based on the relevant data of 123 enterprises with credit records, the credit strategy is formulated according to the three indicators of enterprise strength, enterprise reputation and stability of supply-demand relationship. This paper also combines the credit reputation, credit risk and supply and demand stability rating in order to establish the bank credit strategic planning model to decide whether to lend or not and the lending order. The conclusion shows that, under the condition of constant total loan amount, the enterprises with the highest credit rating should be given priority. Then, combined with the change of customer turnover rate with interest rate, we take the bank's maximize expected income as objective to calculate the optimal loan interest rate of different customer groups

Strong-Magnetic-Field Magnon Transport in Monolayer Graphene

At high magnetic fields, monolayer graphene hosts competing phases distinguished by their breaking of the approximate SU(4) isospin symmetry. Recent experiments have observed an even denominator fractional quantum Hall state thought to be associated with a transition in the underlying isospin order from a spin-singlet charge density wave at low magnetic fields to an antiferromagnet at high magnetic fields, implying that a similar transition must occur at charge neutrality. However, this transition does not generate contrast in typical electrical transport or thermodynamic measurements and no direct evidence for it has been reported, despite theoretical interest arising from its potentially unconventional nature. Here, we measure the transmission of ferromagnetic magnons through the two dimensional bulk of clean monolayer graphene. Using spin polarized fractional quantum Hall states as a benchmark, we find that magnon transmission is controlled by the detailed properties of the low-momentum spin waves in the intervening Hall fluid, which is highly density dependent. Remarkably, as the system is driven into the antiferromagnetic regime, robust magnon transmission is restored across a wide range of filling factors consistent with Pauli blocking of fractional quantum hall spin-wave excitations and their replacement by conventional ferromagnetic magnons confined to the minority graphene sublattice. Finally, using devices in which spin waves are launched directly into the insulating charge-neutral bulk, we directly detect the hidden phase transition between bulk insulating charge density wave and a canted antiferromagnetic phases at charge neutrality, completing the experimental map of broken-symmetry phases in monolayer graphene

Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation

Diffusion models have proven to be highly effective in image and video generation; however, they still face composition challenges when generating images of varying sizes due to single-scale training data. Adapting large pre-trained diffusion models for higher resolution demands substantial computational and optimization resources, yet achieving a generation capability comparable to low-resolution models remains elusive. This paper proposes a novel self-cascade diffusion model that leverages the rich knowledge gained from a well-trained low-resolution model for rapid adaptation to higher-resolution image and video generation, employing either tuning-free or cheap upsampler tuning paradigms. Integrating a sequence of multi-scale upsampler modules, the self-cascade diffusion model can efficiently adapt to a higher resolution, preserving the original composition and generation capabilities. We further propose a pivot-guided noise re-schedule strategy to speed up the inference process and improve local structural details. Compared to full fine-tuning, our approach achieves a 5X training speed-up and requires only an additional 0.002M tuning parameters. Extensive experiments demonstrate that our approach can quickly adapt to higher resolution image and video synthesis by fine-tuning for just 10k steps, with virtually no additional inference time.Comment: Project Page: https://guolanqing.github.io/Self-Cascade

Isolation, characterization, and genomic analysis of a lytic bacteriophage, PQ43W, with the potential of controlling bacterial wilt

Bacterial wilt (BW) is a devastating plant disease caused by the soil-borne bacterium Ralstonia solanacearum species complex (Rssc). Numerous efforts have been exerted to control BW, but effective, economical, and environmentally friendly approaches are still not available. Bacteriophages are a promising resource for the control of bacterial diseases, including BW. So, in this study, a crop BW pathogen of lytic bacteriophage was isolated and named PQ43W. Biological characterization revealed PQ43W had a short latent period of 15 min, 74 PFU/cell of brust sizes, and good stability at a wide range temperatures and pH but a weak resistance against UV radiation. Sequencing revealed phage PQ43W contained a circular double-stranded DNA genome of 47,156 bp with 65 predicted open reading frames (ORFs) and genome annotation showed good environmental security for the PQ43W that no tRNA, antibiotic resistance, or virulence genes contained. Taxonomic classification showed PQ43W belongs to a novel genus of subfamily Kantovirinae under Caudoviricetes. Subsequently, a dose of PQ43W for phage therapy in controlling crop BW was determined: 108 PFU*20 mL per plant with non-invasive irrigation root application twice by pot experiment. Finally, a field experiment of PQ43W showed a significantly better control effect in crop BW than the conventional bactericide Zhongshengmycin. Therefore, bacteriophage PQ43W is an effective bio-control resource for controlling BW diseases, especially for crop cultivation

Involvement of NMDA-AKT-mTOR Signaling in Rapid Antidepressant-Like Activity of Chaihu-jia-Longgu-Muli-tang on Olfactory Bulbectomized Mice

Background: Fast-onset antidepressants are urgently needed. Chaihu-jia-Longgu-Muli-tang (CLM), a classic Chinese herbal medicine, has been used for antidepressant treatment with long history. Olfactory bulbectomization (OB) model is validated for identification of rapid antidepressant efficacy. Here we used OB model for investigating the rapid onset activity of CLM in mice, and also tested the involvement of prefrontal Akt-mTOR and associated AMPA/NMDA receptors as well as hippocampal BDNF in the rapid antidepressant-like effect of CLM.Methods: The OB model was first characterized with depression-like behaviors and the time course changes of the behaviors. The fast onset of antidepressant effect of CLM was evaluated using sucrose preference test, tail suspension test and forced swim test in OB mice after a single administration. The expression of synaptic proteins of AMPA and NMDA subunits as well as Akt/mTOR signaling in the prefrontal cortex, and hippocampal BDNF was evaluated with the immunoblotting method.Results: A single dose of CLM significantly improved the deficiency in the sucrose preference and decreased the immobility time in the tail suspension test in OB mice. In the prefrontal cortex (PFC) in OB mice, there was lower expression level of the AMPA receptor subunit GluR1, rescued by a single dose of CLM. Additionally, the expression of NMDA subunit NR1 was up-regulated in OB mice, whereas mTOR and its upstream Akt signalings were both down-regulated. These deficiencies were reversed by a single dose of CLM. The CLM treatment also attenuated the expressions of NMDA receptor subunits NR2A and NR2B, which did not change in OB mice. In the hippocampus, expressions of GluR1 and brain derived neurotrophic factor (BDNF) were both up-regulated in OB mice, although CLM increased GluR1, but not BDNF.Conclusion: CLM elicited rapid antidepressant-like effects in the OB model mice, and CLM reversal of the abnormality in PFC expression of AMPA and NMDA receptors and associated Akt-mTOR signaling may underlie the effects

Research on Computer-Aided Diagnosis Method Based on Symptom Filtering and Weighted Network

In the process of disease identification, as the number of diseases increases, the collection of both diseases and symptoms becomes larger. However, existing computer-aided diagnosis systems do not completely solve the dimensional disaster caused by the increasing data set. To address the above problems, we propose methods of using symptom filtering and a weighted network with the goal of deeper processing of the collected symptom information. Symptom filtering is similar to a filter in signal transmission, which can filter the collected symptom information, further reduce the dimensional space of the system, and make the important symptoms more prominent. The weighted network, on the other hand, mines deeper disease information by modeling the channels of symptom information, amplifying important information, and suppressing unimportant information. Compared with existing hierarchical reinforcement learning models, the feature extraction methods proposed in this paper can help existing models improve their accuracy by more than 10%

Carbohydrate and Amino Acid Metabolism as Hallmarks for Innate Immune Cell Activation and Function

Immune activation is now understood to be fundamentally linked to intrinsic and/or extrinsic metabolic processes which are essential for immune cells to survive, proliferate, and perform their effector functions. Moreover, disruption or dysregulation of these pathways can result in detrimental outcomes and underly a number of pathologies in both communicable and non-communicable diseases. In this review, we discuss how the metabolism of carbohydrates and amino acids in particular can modulate innate immunity and how perturbations in these pathways can result in failure of these immune cells to properly function or induce unfavorable phenotypes

Automated analysis of pectoralis major thickness in pec-fly exercises: evolving from manual measurement to deep learning techniques

Abstract This study addresses a limitation of prior research on pectoralis major (PMaj) thickness changes during the pectoralis fly exercise using a wearable ultrasound imaging setup. Although previous studies used manual measurement and subjective evaluation, it is important to acknowledge the subsequent limitations of automating widespread applications. We then employed a deep learning model for image segmentation and automated measurement to solve the problem and study the additional quantitative supplementary information that could be provided. Our results revealed increased PMaj thickness changes in the coronal plane within the probe detection region when real-time ultrasound imaging (RUSI) visual biofeedback was incorporated, regardless of load intensity (50% or 80% of one-repetition maximum). Additionally, participants showed uniform thickness changes in the PMaj in response to enhanced RUSI biofeedback. Notably, the differences in PMaj thickness changes between load intensities were reduced by RUSI biofeedback, suggesting altered muscle activation strategies. We identified the optimal measurement location for the maximal PMaj thickness close to the rib end and emphasized the lightweight applicability of our model for fitness training and muscle assessment. Further studies can refine load intensities, investigate diverse parameters, and employ different network models to enhance accuracy. This study contributes to our understanding of the effects of muscle physiology and exercise training

Research Progress and Prospects of Agricultural Aero-Bionic Technology in China

Accelerating the development of agricultural aviation technology is the need of China’s modern agricultural construction. With the rise of emerging industries such as artificial intelligence, biotechnology, autonomous navigation, and the Internet of Things, agricultural aviation is further developing toward the direction of intelligence to meet the requirements of efficient and sophisticated agricultural aviation operations. Bionics is a multi-discipline and comprehensive border subject. It is produced by the mutual penetration and integration of life science and engineering science. Bionic technology has received more and more attention in recent years, and breakthroughs have been made in the fields of biomedicine and health, military, brain science and brain-like navigation, and advanced manufacturing. This study summarized the research progress of biomimetic technology in the field of agricultural aviation from three aspects of biological perception, biological behavior, and biological intelligence. On this basis, problems of related research and application of agricultural aircraft in real-time obstacle avoidance, path planning, and intelligent navigation were analyzed. Combined with the practice of the rapid development of agricultural aircraft, research and application of bionic technology suitable for agricultural aircraft were then proposed. Finally, prospects of agricultural aero-bionic technology were also discussed from multiple bionic target fusion, three-dimensional spatial information exploration, sensors, and animal brain system mechanism. This review provides a reference for the development of bionic technology in China’s agricultural aviation