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This paper aims to systematically analyze the reasons for the differences in the relationship between transformational leadership (TL) and project success and apply meta-analysis to summarize which dimensions of TL are the main driving forces for project success. Adopting the meta-analysis approach, we investigated 31 independent studies (N = 6475) and studied the theoretical moderators of this relationship from the perspectives of mediating variables, cultural background, and document type to test whether the moderating effects can explain the inconsistent research results. The results reveal that TL positively affects project success and leadership charm is the primary driver of TL. Also, the existence of a mediating mechanism has a more significant impact on the success of the leading project. Meanwhile, compared with project construction under the Western cultural background, countries with Eastern culture are more inclined to use a people-oriented philosophy for project management to promote project success. This research provides an empirical perspective to help project leaders select management talents, regulate leaders' words and deeds, and cultivate technical and soft leadership skills. Besides, this paper proposes a unique and nuanced view of the relationship between TL and project success, enhancing people's understanding of the TL's role in influencing project success.With the progress of sci-tech, the interdisciplinary and comprehensive development, and various advanced sci-tech gradually integrated into the field of sports, it has become possible to study how to reasonably prevent sports injuries, minimize the risk of sports injuries, and maintain the best physical condition of retired athletes. Due to the long-term high-load exercise of retired athletes during their sports career, athletes' physical functions have been damaged to varying degrees, resulting in more injuries. According to the characteristics that many factors need to be considered in the prediction of retired athletes' injuries, this paper puts forward an improved self-organizing neural network (SOM) method to predict retired athletes' injuries. In this paper, an early warning analysis model of retired athletes' susceptibility to injury based on SOM is proposed, which screens the state of retired athletes' physical function variables in each stage, considers athletes' physical function data whose standard deviation is higher than the limit specification of susceptibility to injury as susceptible injury data, quickly judges all vulnerable injury data, and completes the high-speed early warning analysis of retired athletes' susceptibility to injury.Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. buy Nesuparib Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages.Soil liquefaction is a dangerous phenomenon for structures that lose their shear strength and soil resistance, occurring during seismic shocks such as earthquakes or sudden stress conditions. Determining the liquefaction and nonliquefaction capacity of soil is a difficult but necessary job when constructing structures in earthquake zones. Usually, the possibility of soil liquefaction is determined by laboratory tests on soil samples subjected to dynamic loads, and this is time-consuming and costly. Therefore, this study focuses on the development of a machine learning model called a Forward Neural Network (FNN) to estimate the activation of soil liquefaction under seismic condition. The database is collected from the published literature, including 270 liquefaction cases and 216 nonliquefaction case histories under different geological conditions and earthquakes used for construction and confirming the model. The model is built and optimized for hyperparameters based on a technique known as random search (RS)apid and accurate prediction of liquefaction based on several basic soil properties.COVID-19 vaccination began in São Paulo, Brazil in January 2021, first targeting healthcare workers (HCWs) and the elderly, using the CoronaVac vaccine (Sinovac/Butantan) and subsequently the Oxford/AstraZeneca (ChAdOx1) vaccine (AstraZeneca/FIOCRUZ-RJ). Studies on such vaccines have shown efficacy in preventing severe cases and deaths, but there is a lack of information regarding their effectiveness. This manuscript presents data from the Instituto Adolfo Lutz (IAL), a public health laboratory located in São Paulo City that receives samples from 17 Regional Health Departments under the Secretary of Health of São Paulo, for SARS-CoV-2 genomic surveillance. Through May 15, 2021 IAL received 20 samples for analysis from COVID-19 vaccinated individuals who needed hospitalization and/or died from COVID-19. Next-generation sequencing was performed on an Ion Torrent S5 platform using the AmpliSeq™ SARS-CoV-2 kit. Almost all cases were vaccinated with CoronaVac and presented the gamma variant of concern (VOC). Cases of death were observed mostly in the elderly in nursing homes, and severe cases in younger frontline HCWs. This data confirmed that the SARS-CoV-2 gamma variant is highly transmissible, severe, and lethal for COVID-19 in these groups of individuals, thereby highlighting the importance of continuous vaccination and non-pharmacological prevention measures to avoid virus dissemination and the emergence of new VOCs.Psychiatric disorder, including bipolar disorder (BD), major depression (MDD), and schizophrenia (SCZ), affects millions of persons around the world. Understanding the disease causal mechanism underlying the three diseases and identifying the modifiable risk factors for them hold the key for the development of effective preventative and treatment strategies. We used a two-sample Mendelian randomization method to assess the causal effect of insomnia on the risk of BD, MDD, and SCZ in a European population. We collected one dataset of insomnia, three of BD, one of MDD, and three of SCZ and performed a meta-analysis for each trait, further verifying the analysis through extensive complementarity and sensitivity analysis. Among the three psychiatric disorders, we found that only insomnia is causally associated with MDD and that higher insomnia increases the risk of MDD. Specifically, the odds ratio of MDD increase of insomnia is estimated to be 1.408 [95% confidence interval (CI) 1.210-1.640, p = 1.03E-05] in the European population. The identified causal relationship between insomnia and MDD is robust with respect to the choice of statistical methods and is validated through extensive sensitivity analyses that guard against various model assumption violations. Our results provide new evidence to support the causal effect of insomnia on MDD and pave ways for reducing the psychiatric disorder burden.Diabetic mellitus erectile dysfunction (DMED) is one of the most common complications of diabetes mellitus (DM), which seriously affects the self-esteem and quality of life of diabetics. MicroRNAs (miRNAs) are endogenous non-coding RNAs whose expression levels can affect multiple cellular processes. Many pieces of studies have demonstrated that miRNA plays a role in the occurrence and development of DMED. However, the exact mechanism of this process is unclear. Hence, we apply miRNA sequencing from blood samples of 10 DMED patients and 10 DM controls to study the mechanisms of miRNA interactions in DMED patients. Firstly, we found four characteristic miRNAs as signature by the SVM-RFE method (hsa-let-7E-5p, hsa-miR-30 days-5p, hsa-miR-199b-5p, and hsa-miR-342-3p), called DMEDSig-4. Subsequently, we correlated DMEDSig-4 with clinical factors and further verified the ability of these miRNAs to classify samples. Finally, we functionally verified the relationship between DMEDSig-4 and DMED by pathway enrichment analysis of miRNA and its target genes. In brief, our study found four key miRNAs, which may be the key influencing factors of DMED. Meanwhile, the DMEDSig-4 could help in the development of new therapies for DMED.[This corrects the article DOI 10.3389/fgene.2021.665920.].Spermatocyte meiosis is the cornerstone of mammalian production. Thousands of long noncoding RNAs (lncRNAs) have been reported to be functional in various cellular processes, but the function of lncRNAs in meiosis remains largely unknown. Here, we profiled lncRNAs in spermatocytes at stage I of meiosis and identified a testis-specific lncRNA, Rbakdn, as a vital regulator of meiosis. Rbakdn is dynamically expressed during meiosis I, and Rbakdn knockdown inhibits meiosis in vitro. Furthermore, Rbakdn knockdown in testes in mice by intratesticular injection disturbs meiosis, reduces testicular volume, and increases apoptosis of spermatocytes, resulting in vacuolation of the seminiferous tubules. Rbakdn can bind to Ptbp2, an RNA-binding protein that is important in the regulation of the alternative splicing of many genes in spermatogenesis. Rbakdn knockdown leads to a decrease in Ptbp2 through the ubiquitination degradation pathway, indicating that Rbakdn maintains the stability of Ptbp2. In conclusion, our study identified an lncRNA, Rbakdn, with a crucial role in meiosis.
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