Old Photos Restoration by Using VAE

VAE is a generative model that “provides a probabilistic description of observations in potential Spaces”. Put simply, this means that VAE stores potential attributes as probability distributions. The idea of variational auto-encoders or VAE is deeply rooted in the methods of variational DB Bayesian and graphical models. This piece of work will discuss VAE Structure, VAE Loss Function, VAE Translation, and our final effects

Bulk Density Adjustment of Resin-Based Equivalent Material for Geomechanical Model Test

An equivalent material is of significance to the simulation of prototype rock in geomechanical model test. Researchers attempt to ensure that the bulk density of equivalent material is equal to that of prototype rock. In this work, barite sand was used to increase the bulk density of a resin-based equivalent material. The variation law of the bulk density was revealed in the simulation of a prototype rock of a different bulk density. Over 300 specimens were made for uniaxial compression test. Test results indicated that the substitution of quartz sand by barite sand had no apparent influence on the uniaxial compressive strength and elastic modulus of the specimens but can increase the bulk density, according to the proportional coarse aggregate content. An ideal linearity was found in the relationship between the barite sand substitution ratio and the bulk density. The relationship between the bulk density and the usage of coarse aggregate and barite sand was also presented. The test results provided an insight into the bulk density adjustment of resin-based equivalent materials

Biodynamic features Syuantszy Chzhuanti 720°.

Presents the internal parameters and image Syuantszy Chzhuanti 720 ° is shown that in the implementation of the element Syuantszy Chzhuanti 720 °, the center of gravity shifts to 2.94 pm, 1.71 m. and 1.22 m. on the X, Y and Z; rate varies according to X - with 4,22 m/s to 0, Y - to 2,42 m/s to 0, and Z - from 3.68 m/s to 3.86 m/s. Run-time item 1.4 seconds: the first turnover - 0.41 sec., The second turnover-0, 33 sec. At the end of the takeoff run strike force left and right foot of 1147.2 N and 1005 N. Pressing the second, third, fourth, fifth finger and part of the metatarsal of right foot maximum intensity of pressure - 146.1 N; when pressing the first finger and part of the metatarsal maximum intensity of pressure - 280.8 N. The dependence of convergence or remove body parts with a vertical axis of the torque to increase or decrease its speed

Gait parameter fitting and adaptive enhancement based on cerebral blood oxygen information

Accurate recognition of patients’ movement intentions and real-time adjustments are crucial in rehabilitation exoskeleton robots. However, some patients are unable to utilize electromyography (EMG) signals for this purpose due to poor or missing signals in their lower limbs. In order to address this issue, we propose a novel method that fits gait parameters using cerebral blood oxygen signals. Two types of walking experiments were conducted to collect brain blood oxygen signals and gait parameters from volunteers. Time domain, frequency domain, and spatial domain features were extracted from brain hemoglobin. The AutoEncoder-Decoder method is used for feature dimension reduction. A regression model based on the long short-term memory (LSTM) model was established to fit the gait parameters and perform incremental learning for new individual data. Cross-validation was performed on the model to enhance individual adaptivity and reduce the need for individual pre-training. The coefficient of determination (R2) for the gait parameter fit was 71.544%, with a mean square error (RMSE) of less than 3.321%. Following adaptive enhancement, the coefficient of R2 increased by 6.985%, while the RMSE decreased by 0.303%. These preliminary results indicate the feasibility of fitting gait parameters using cerebral blood oxygen information. Our research offers a new perspective on assisted locomotion control for patients who lack effective myoelectricity, thereby expanding the clinical application of rehabilitation exoskeleton robots. This work establishes a foundation for promoting the application of Brain-Computer Interface (BCI) technology in the field of sports rehabilitation

Assess and Summarize: Improve Outage Understanding with Large Language Models

Cloud systems have become increasingly popular in recent years due to their flexibility and scalability. Each time cloud computing applications and services hosted on the cloud are affected by a cloud outage, users can experience slow response times, connection issues or total service disruption, resulting in a significant negative business impact. Outages are usually comprised of several concurring events/source causes, and therefore understanding the context of outages is a very challenging yet crucial first step toward mitigating and resolving outages. In current practice, on-call engineers with in-depth domain knowledge, have to manually assess and summarize outages when they happen, which is time-consuming and labor-intensive. In this paper, we first present a large-scale empirical study investigating the way on-call engineers currently deal with cloud outages at Microsoft, and then present and empirically validate a novel approach (dubbed Oasis) to help the engineers in this task. Oasis is able to automatically assess the impact scope of outages as well as to produce human-readable summarization. Specifically, Oasis first assesses the impact scope of an outage by aggregating relevant incidents via multiple techniques. Then, it generates a human-readable summary by leveraging fine-tuned large language models like GPT-3.x. The impact assessment component of Oasis was introduced in Microsoft over three years ago, and it is now widely adopted, while the outage summarization component has been recently introduced, and in this article we present the results of an empirical evaluation we carried out on 18 real-world cloud systems as well as a human-based evaluation with outage owners. The results show that Oasis can effectively and efficiently summarize outages, and lead Microsoft to deploy its first prototype which is currently under experimental adoption by some of the incident teams

Development and validation of a visualized prediction model for early miscarriage risk in patients undergoing IVF/ICSI procedures: a real-world multi-center study

BackgroundThis study focuses on the risk of early miscarriage in patients undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). These patients commonly experience heightened stress levels and may discontinue treatment due to emotional burdens associated with repeated failures. Despite the identification of numerous potential factors contributing to early miscarriage, there exists a research gap in integrating these factors into predictive models specifically for IVF/ICSI patients. The objective of this study is to develop a user-friendly nomogram that incorporates relevant risk factors to predict early miscarriage in IVF/ICSI patients. Through internal and external validation, the nomogram facilitates early identification of high-risk patients, supporting clinicians in making informed decisions.MethodsA retrospective analysis was conducted on 20,322 first cycles out of 31,307 for IVF/ICSI treatment at Sun Yat-sen Memorial Hospital between January 2011 and December 2020. After excluding ineligible cycles, 6,724 first fresh cycles were included and randomly divided into a training dataset (n = 4,516) and an internal validation dataset (n = 2,208). An external dataset (n = 1,179) from another hospital was used for validation. Logistic and LASSO regression models identified risk factors, and a multivariable logistic regression constructed the nomogram. Model performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA).ResultsSignificant risk factors for early miscarriage were identified, including female age, BMI, number of spontaneous abortions, number of induced abortions and medical abortions, basal FSH levels, endometrial thickness on hCG day, and number of good quality embryos. The predictive nomogram demonstrated good fit and discriminatory power, with AUC values of 0.660, 0.640, and 0.615 for the training, internal validation, and external validation datasets, respectively. Calibration curves showed good consistency with actual outcomes, and DCA confirmed the clinical usefulness. Subgroup analysis revealed variations; for the elder subgroup (age ≥35 years), female age, basal FSH levels, and number of available embryos were significant risk factors, while for the younger subgroup (age <35 years), female age, BMI, number of spontaneous abortions, and number of good quality embryos were significant.ConclusionsOur study provides valuable insights into the impact factors of early miscarriage in both the general study population and specific age subgroups, offering practical recommendations for clinical practitioners. We have taken into account the significance of population differences and regional variations, ensuring the adaptability and relevance of our model across diverse populations. The user-friendly visualization of results and subgroup analysis further enhance the applicability and value of our research. These findings have significant implications for informed decision-making, allowing for individualized treatment strategies and the optimization of outcomes in IVF/ICSI patients

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries

Improving nursing care for elderly patients with diabetes

As one of the common chronic diseases, diabetes not only occurs in middle age, but also the incidence of diabetes is increasing gradually. At the same time, elderly patients with diabetes are more likely to suffer from a series of complications because of the lack of relevant nursing knowledge of health care workers. So exploring what nurses can do to improve nursing care for elderly people with diabetes is extremely important. The aim of this study is to put together information on nursing intervention for elderly people with diabetes base on already existing literature. The purpose is to provide useful and clear information for nurses who take care of elderly people with diabetes. The study was conducted as a literature review and the data was collected using two databases: CINAHL and Academic Search Elite. The results was analyzed by using inductive content analysis method from eight articles. The results were divided into five categories: 1. the need for knowledge and information about diabetes for nurses, 2. teamwork among nurses, 3. the need to promote patient’s self-care, 4. regular examinations and care of elderly patients with diabetes. In conclusion, there are may things that nurses and patients themselves can do to prevent diabetes from getting worse. However, it’s difficult for nurses to take good care of every patient. For further research, we recommend that conducting research about psychological problems associated with elderly people with diabete

A propensão ao consumo de luxo: comparando consumidores chineses e Portugueses

Dissertação de Mestrado em Marketing apresentada à Faculdade de EconomiaThe main purpose of this study is exploring the factors which will influence luxury consumption tendency among both Chinese and Portuguese consumers. The objective is comparing the differences between Chinese and Portuguese consumers among the variables which are social influence, conspicuous consumption, brand image, luxury consumption tendency, life satisfaction, luxury spending and urge to buy.The main research objective of this research is to clarify the impact of the independent variables on luxury consumption tendency. The independent variables include social influence, conspicuous consumption and brand image. The core-variable of this research is luxury consumption tendency. Meanwhile, this research will explore whether conspicuous consumption has an impact on life satisfaction, luxury spending and urge to buy. Then it will be explored whether luxury consumption tendency has an impact on life satisfaction, luxury spending and urge to buy and whether brand image has an impact on luxury spending and urge to buy. Furthermore, this research will verify the relationship between life satisfactions and urge to buy, clarify the relationship between life satisfaction and luxury spending and the relationship between luxury spending and urge to buy.The author selects 3 independent variables which are based on literature review to examine hypothesize. Furthermore, the research model is also developed by literature review. In this study, 380 respondents from China and 403 respondents from Portugal were tested by both online questionnaires and paper questionnaires. The data were tested by Amos and SPSS software.The results indicate that factors that have a significant impact on luxury product tendency include social influence, conspicuous consumption and brand image. Meanwhile, luxury consumption tendency is related with life satisfaction, luxury spending and urge to buy. Furthermore, life satisfaction will influence on urge to buy. Furthermore, the country difference will also lead different in conspicuous consumption and luxury spending.O objetivo principal deste estudo é explorar os fatores que irão influenciar as tendências do consumo de luxo entre os consumidores chineses e portugueses. Pretendemos comparar as diferenças entre os consumidores chineses e portugueses usando as variáveis influência social, consumo conspícuo, imagem de marca, tendência para o consumo de luxo, satisfação com a vida, gastos de luxo e vontade de comprar.O objetivo principal desta pesquisa é esclarecer o impacto das variáveis independentes na tendência de consumo de luxo. As variáveis independentes incluem influência social, consumo conspícuo e imagem da marca. A variável central desta pesquisa é a tendência ao consumo de luxo. Enquanto isso, esta pesquisa irá explorar se o consumo conspícuo tem um impacto na satisfação com a vida, nos gastos com luxo e no desejo de comprar. Em seguida, será explorado se a tendência do consumo de luxo tem impacto sobre a satisfação com a vida, os gastos com luxo e o desejo de comprar e se a imagem da marca tem impacto sobre os gastos com luxo e o desejo de comprar. Além disso, esta pesquisa irá verificar a relação entre a satisfação com a vida e a vontade de comprar, esclarecer a relação entre a satisfação com a vida e os gastos com luxo e a relação entre os gastos com luxo e a vontade de comprar.O autor seleciona 3 variáveis independentes que são baseadas na revisão da literatura para examinar a hipótese. Além disso, o modelo de pesquisa também é desenvolvido por meio de revisão da literatura. Neste estudo, 380 respondentes da China e 403 respondentes de Portugal foram testados por questionários online e questionários em papel. Os dados foram testados pelos softwares Amos e SPSS.Os resultados indicam que os fatores que têm impacto significativo na tendência dos produtos de luxo incluem influência social, consumo conspícuo e imagem da marca. Enquanto isso, a tendência do consumo de luxo está relacionada à satisfação com a vida, gastos com luxo e desejo de comprar. Além disso, a satisfação com a vida influencia o desejo de comprar. Além disso, a diferença de país também levará diferentes em consumo conspícuo e gastos de luxo

Human-AI Collaborative Sub-Goal Optimization in Hierarchical Reinforcement Learning

Hierarchical reinforcement learning often involves human expertise in defining multiple sub-goals to decompose complex objectives into relevant sub-tasks. However, manually specifying these sub-goals is labor-intensive, costly, and prone to introducing biases or misleading the agent. To overcome these challenges, we propose a collaborative human-AI algorithm that seamlessly integrates with hierarchical models to automatically update prior knowledge and optimize candidate sub-goals. Our algorithm can be easily incorporated into a wide range of goal-conditioned frameworks. We evaluate our approach in comparison with relevant baselines, we demonstrate the effectiveness of our algorithm in addressing and preventing negative inferences arising from confusing or conflicting sub-goals. Additionally, our algorithm shows robustness across different levels of human knowledge, accelerating convergence towards optimal sub-goal spaces and hierarchical policies