A Study of the Superplastic Deformation Behavior of Low-Cost Ti-2Fe-0.1B Alloys.

Titanium alloys have high specific strength and corrosion resistance, which have promising applications in industry. However, the machinability of titanium alloys is limited due to their crystal lattice and physical properties. Thus, in recent years, the superplastic forming of titanium alloys has been intensively developing, in particular, forming at low temperatures and/or high strain rates. In this work, a tensile test of low-cost Ti-2Fe-0.1B alloys was carried out at a temperature of 550~750 °C and a strain rate of 1 × 10-3 s-1~1 × 10-2 s-1. The results showed that the alloy exhibited good superplasticity even at a high strain rate (1 × 10-2 s-1) and a low deformation temperature of 550 °C; the elongation of the alloy in this state reached 137.5%. The high strain rate sensitivity coefficient m (0.3) and the maximum elongation (452%) were obtained at a strain rate of 1 × 10-3 s-1 and a temperature of 750 °C. Characteristics of the microstructure showed that during superplastic deformation, the recrystallization and grain boundary sliding of the alloy phases were accelerated, which could be ascribed to the effect of the element Fe. At the same time, the TiB phase located around the primary elongated α grains could also induce dynamic recrystallization and dynamic globularization during deformation.

Assessing the mediation pathways: How decent work affects turnover intention through job satisfaction and burnout in nursing.

AIM: This study aimed to assess the potential mediating roles of nurses' job satisfaction and burnout in the association between decent work and turnover intention. BACKGROUND: There is a global challenge of nursing shortages in healthcare systems worldwide. Decent work is crucial for safeguarding the rights and professional development outcomes of nurses. However, there is currently limited research on decent work among nurses, and there is a lack of studies exploring the relationships between nurses' decent work, job satisfaction, burnout, and turnover intention. METHODS: A cross-sectional survey design was employed with a sample of 460 nurses from three hospitals: The Affiliated Hospital of Hangzhou Normal University, Hangzhou Third People's Hospital, and Hangzhou Red Cross Hospital. The STROBE checklist was used. Mediation analysis using the PROCESS Macro was used to examine the relationships between decent work, job satisfaction, burnout, and turnover intention. RESULTS: The results showed that nurses' perception of decent work directly influences their turnover intention. Additionally, these findings strongly support the role of job satisfaction and burnout as mediating factors in the relationship between decent work and turnover intention. CONCLUSIONS: Decent work reduces nurse burnout and turnover intention of enhancing their job satisfaction. IMPLICATIONS FOR NURSING AND HEALTH POLICY: This study's findings have important implications for healthcare organizations and policymakers. Recognizing the pivotal role of decent work in nurses' job satisfaction and well-being can guide the development of strategies to improve working conditions and reduce turnover rates. It is imperative for healthcare institutions to prioritize creating safe, supportive, and equitable work conditions for nurses, as this can contribute to higher job satisfaction and, subsequently, lower turnover rates.

Decent work, work engagement, and turnover intention among registered nurses: a cross-sectional study.

BACKGROUND: Nurses face substantial career challenges arising from global pandemics, economic crises, and their roles in conflict-ridden areas. In this context, the rights of nurses pertaining to decent work, such as freedom, fairness, safety, and dignity, are not adequately safeguarded. This study examines decent work status among Chinese nurses and its links to demographics, work engagement, and turnover intention. METHODS: A cross-sectional study design was used following STROBE guidelines. Through a convenient sampling method, a total of 476 nurses were surveyed. These participants were drawn from three esteemed tertiary Grade A hospitals in Hangzhou, with data collection spanning from June to August in 2023. We used a comprehensive set of assessment instruments, encompassing an evaluation of demographic characteristics, the Decent Work Perceptions Scale (DWPS), the Utrecht Work Engagement Scale (UEWS), and turnover intention questionnaire. Bootstrapping procedures were used to ensure the robustness and reliability of the model. RESULTS: The study revealed that nurses' perceptions of decent work significantly impacted work engagement (ß = 0.603, p < 0.001) and turnover intention (ß = -0.275, p < 0.001). Work engagement operated as a mediator between decent work and turnover intention, decreasing the likelihood of nurses leaving their positions (ß = -0.062, p < 0.001). Factors such as age, years of working experience, professional title, job category, and attendance at professional conferences significantly influenced nurses' perceptions of decent work (all p < 0.05). CONCLUSIONS: This study examines factors affecting decent work among nurses and explores its connection with work engagement and the intention to leave. Despite limitations (sample, social desirability bias), the study offers valuable insights for nursing practice. This suggests managers improve decent work for young nurses through rational shift schedules and continuous education. Policymakers should consider adjusting nursing policies for better employment conditions.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics.

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

Traditional Chinese medicine for treating aplastic anemia.

Aplastic anemia (AA) is a bone marrow failure disease caused by T cell hyperfunction. Although the overall response rate has been improved by immunosuppressive therapy (IST) plus Eltrombopag, 30% of patients have either no response or relapse. We therefore attempted to find other ways to improve the outcomes of AA patients. Traditional Chinese medicine has the advantages of low cost, reasonable effects, and few side effects. More and more clinical studies have confirmed that traditional Chinese medicine has a beneficial role in treating AA patients. This article reviews the potential mechanism of traditional Chinese medicine or its active ingredients in the treatment of AA. These include improving the bone marrow microenvironment, regulating immunity, and affecting the fate of hematopoietic stem cells. This provides useful information for further treatment of AA with integration of traditional Chinese and Western medicine and the development of new treatment strategies.

Phenomenological characteristics of autobiographical future thinking in nurses with burnout: a case-control study.

Objective: Nurses constitute the largest group of healthcare workers worldwide, and job burnout is very common among them. This study aims to explore abnormal future thinking in nurses with burnout. Additionally, the study investigates whether these manifestations worsen as burnout progresses. Methods: The study was conducted in inpatient ward nurses at a tertiary hospital in Hangzhou, China. In the first phase, two group of nurses were recruited: nurses with burnout (N = 70) and nurses without burnout (N = 70). In the second phase, three groups were recruited according to the burnout levels: mild burnout (N = 43), moderate burnout (N = 42) and severe burnout (N = 43). Data on job burnout were obtained using the Chinese Maslach Burnout Inventory. The Sentence Completion for Events in the Future Test (SCEFT) was employed to measure the content of future thinking, which was evaluated by two raters in terms of the specificity, emotional valence, and concrete content of the imagined future events. The proportions of specific types of events among all the produced events were calculated. Results: The results revealed that nurses with burnout, compared to nurses without burnout, imagined fewer specific future events, positive events, and events related to relationships and achievement. They also had more omissions. As the level of burnout increased, their impairment in future thinking worsened. Furthermore, the results also revealed that the scores of emotional exhaustion, depersonalization, and personal accomplishment had significant correlations with the proportions of positive events and events related to relationships and achievement/mastery in nurses' future thinking content. Conclusion: The future thinking ability of nurses with burnout was impaired, and this impairment worsened as the symptoms of burnout progressed. The findings of the present study have important implications for nurse caring and advocate effective interventions targeting positive future thinking to mitigate nurses' burnout.

The bionic neural network for external simulation of human locomotor system.

Muscle forces and joint kinematics estimated with musculoskeletal (MSK) modeling techniques offer useful metrics describing movement quality. Model-based computational MSK models can interpret the dynamic interaction between the neural drive to muscles, muscle dynamics, body and joint kinematics, and kinetics. Still, such a set of solutions suffers from high computational time and muscle recruitment problems, especially in complex modeling. In recent years, data-driven methods have emerged as a promising alternative due to the benefits of flexibility and adaptability. However, a large amount of labeled training data is not easy to be acquired. This paper proposes a physics-informed deep learning method based on MSK modeling to predict joint motion and muscle forces. The MSK model is embedded into the neural network as an ordinary differential equation (ODE) loss function with physiological parameters of muscle activation dynamics and muscle contraction dynamics to be identified. These parameters are automatically estimated during the training process which guides the prediction of muscle forces combined with the MSK forward dynamics model. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The results demonstrate that the proposed deep learning method can effectively identify subject-specific MSK physiological parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion and muscle forces predictions.

Niobium Nitride Preparation for Superconducting Single-Photon Detectors.

Niobium nitride (NbN) is widely used in the production of superconducting nanowire single-photon detectors (SNSPDs) due to its high superconducting transition temperature and suitable energy gap. The processing parameters used for the preparation of NbN films and the subsequent processing of nanowires have a significant effect on the performance of the SNSPD. In this review, we will present various thin film growth methods, including magnetron sputtering, atomic layer deposition (ALD), and chemical vapor deposition (CVD). The relationships between the superconducting performance of each thin film and the corresponding deposition process will be discussed. Subsequently, NbN nanowire fabrication methods and microstructures based on thin film etching will be summarized, and their impact on the qualities of the finished SNSPDs will be systematically analyzed. Finally, we will provide an outlook for the future development of preparation for SNSPD.

Preparation of an Antimicrobial and Antioxidant Bio-Polymer Film and Its Application as Glazing Shell for Postharvest Quality of Fresh-Cut Apple.

The aim of this work is to glazing a modified bio-polymer shell as substitute of peel to keep the postharvest quality of fresh-cut fruits. In this study, chitosan as backbone of the shell was modified by addition of the functional extracts obtained from Zanthoxylum, in which 12 kinds of main identified bio-active components consisted of over 55% of the total extracts. The introduction of the extracts improved physic and mechanical properties of the shell, and endowed it with significant antimicrobial and antioxidant activity. Accordingly, the modified chitosan was used as the substitute of peel to preserve fresh-cut apples. Results exhibited that such treatments obviously delayed the decline process of overall postharvest quality of the preserved apple samples throughout all the storage period, represented by the variations in physical, chemical, and microbial properties of the apple samples were significantly inhibited. The overall observations revealed promising potential of the bio-polymer shell in food application.

Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG.

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods have emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. Simultaneously, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.

TRAF2 gene silencing induces proliferation and represses apoptosis of nucleus pulposus cells in rats with intervertebral disc degeneration.

OBJECTIVE: We aim to research the role of TRAF2 silencing in regulating proliferation and apoptosis of nucleus pulposus cells (NIPCs) in rats with intervertebral disc degeneration (IDD) through mediating NF-κB signaling pathway. METHODS: Degenerative disc nucleus pulposus tissues and normal nucleus pulposus tissues were collected to compare the positive expression of TRAF2 protein by immunohistochemistry, and to compare TRAF2, NF-κB (P65) and NF-κB (P50) expression by RT-qPCR and Western blot. A rat model with IDD was replicated using fibrous ring needle method and then treated with TRAF2-siRNA to silence TRAF2. Then, pathological changes and apoptosis in nucleus pulposus tissues were observed. In vitro NIPCs were transfected with TRAF2-siRNA or NF-κB pathway activator (recombinant TNF-α), and then colony formation, proliferation, senescence and apoptosis of NIPCs were determined. RESULTS: Increased TRAF2 and NF-κB were found in nucleus pulposus tissues from IDD patients and rats. Silencing TRAF2 inactivated NF-κB signaling pathway and attenuated pathological damage and apoptosis in nucleus pulposus tissues of rats with IDD. In in-vitro NIPCs, knockdown of TRAF2 resulted in promoted proliferation and colony formation ability while suppressed senescence and apoptosi. The NF-κB pathway activator (recombinant TNF-α) reversed the phenotypic changes of NIPCs resulted from TRAF2 silence. CONCLUSION: Our study demonstrates that TRAF2 is highly expressed in IDD, and silencing of TRAF2 promotes NIPC proliferation and restrains the apoptosis in IDD.

Metapristone (RU486-derivative) inhibits endometrial cancer cell progress through regulating miR-492/Klf5/Nrf1 axis.

BACKGROUND: Endometrial cancer is an invasive gynecological cancer prevalent in the world. The pathogenesis of endometrial cancer is related to multiple levels of regulation, referring to oestrogen, tumor-suppressor gene (e.g. PTEN) or microRNAs (e.g. miR-23a and miR-29b). Metapristone is a hormone-related drug, which is widely used in clinical treatment of endometrial cancer. However, the underlying regulatory mechanism of metapristone on endometrial cancer is still unclear, especially the regulatory effect on microRNAs. The aim of this study is to investigate the specific molecular mechanism of metapristone regulating microRNAs in the treatment of endometrial cancer. METHODS: RL95-2 cells and Ishikawa cells were used as the endometrial cancer models. MiR-492 or si-miR-492 was transfected into RL95-2 cells and Ishikawa cells to explore the role of miR-492 in endometrial cancer. The cell cancer model and mice cancer model were used to confirm the function and mechanism of metapristone affected on endometrial cancer in vitro and in vivo. Mechanically, cell proliferation was monitored using MTT assay, cell colony formation assay and EdU assay. Luciferase reporter assay was used to identify the downstream target gene of miR-492. The protein expression and RNA expression were respectively measured by western blot and qRT-PCR for cell signaling pathway research, subsequently, were verified in the mice tumor model via immunohistochemistry. RESULTS: Metapristone as a kind of hormone-related drug significantly inhibited the endometrial cancer cell growth through regulating cell apoptosis-related gene expression. Mechanically, miR-492 and its target genes Klf5 and Nrf1 were highly expressed in the endometrial cancer cell lines, which promoted cell proliferation and inhibited cell apoptosis. Metapristone decreased the expression of miR-492 and its target genes Klf5 and Nrf1, leading to endometrial cancer cell growth inhibition in vitro and in vivo. CONCLUSION: Metapristone inhibited the endometrial cancer cell growth through regulating the cell apoptosis-related signaling pathway and decreasing the expression of miR-492 and its downstream target genes (Klf5 and Nrf1), which provided the theoretical basis in clinical treatment of endometrial cancer.

An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion.

EMG-based continuous wrist joint motion estimation has been identified as a promising technique with huge potential in assistive robots. Conventional data-driven model-free methods tend to establish the relationship between the EMG signal and wrist motion using machine learning or deep learning techniques, but cannot interpret the functional relationship between neuro-commands and relevant joint motion. In this paper, an EMG-driven musculoskeletal model is proposed to estimate continuous wrist joint motion. This model interprets the muscle activation levels from EMG signals. A muscle-tendon model is developed to compute the muscle force during the voluntary flexion/extension movement, and a joint kinematic model is established to estimate the continuous wrist motion. To optimize the subject-specific physiological parameters, a genetic algorithm is designed to minimize the differences of joint motion prediction from the musculoskeletal model and joint motion measurement using motion data during training. Results show that mean root-mean-square-errors are 10.08°, 10.33°, 13.22° and 17.59° for single flexion/extension, continuous cycle and random motion trials, respectively. The mean coefficient of determination is over 0.9 for all the motion trials. The proposed EMG-driven model provides an accurate tracking performance based on user's intention.

Effect of Low pH and Aluminum Toxicity on the Photosynthetic Characteristics of Different Fast-Growing Eucalyptus Vegetatively Propagated Clones.

Knowing how acid soils and aluminum in soils may limit the growth of Eucalyptus trees in plantations is important because these plantations grow in many tropical and subtropical regions. Seedlings of four vegetatively propagated Eucalyptus clones, E. grandis × E. urophylla 'GLGU9'(G9), E. grandis × E. urophylla 'GLGU12' (G12), E. urophylla × E. camaldulensis 'GLUC3' (G3) and E. urophylla 'GLU4'(G4), were subjected to liquid culture with Hoagland nutrient solution for 40 days, then treated with four different treatments of acid and aluminum for 1 day. The four treatments used either pH 3.0 or 4.0 with or without added aluminum (4.4 mM) in all possible combinations; a control used no added aluminum at pH 4.8. Subsequently, the photosynthetic parameters and morphology of leaves from eucalypt seedlings were determined and observed. The results showed that the tested chlorophyll content, net photosynthetic rate, transpiration rate and water use efficiency were apparently inhibited by aluminum. Under uniform Al concentration (4.4 mM), the Al-induced limitation to photosynthetic parameters increased with pH, indicating acid stimulation to Al toxicity. Among all treatments, the most significant reduction was found in the combination of pH 3.0 and 4.4 mM Al. The photosynthetic and transpiration rates showed similar trends with G9 > G12 > G3 > G4, suggesting that G9 and G12 had higher Al-tolerance than other two clones. Microscopic observation revealed changes in leaf morphology when exposed to Al stress; for example, a reduced thickness of leaf epidermis and palisade tissue, the descendant palisade tissue/spongy tissue ratio and leaf tissue looseness. Overall, the acid and aluminum stress exerted negative effects on the photosynthetic activity of eucalypt seedlings, but the differences in tolerance to Al toxicity between the clones were favorable, offering potential to improve Eucalyptus plantation productivity by selecting Al tolerant clones.