Perceived HRM and turnover intentions of elderly care workers: perspective from person-job fit and institutional ownership

BACKGROUND

Although the phenomenon of high turnover rate in the elderly care industry has existed for a long time, there are few studies that have constructed frameworks to comprehensively analyze the strength of the effects of various factors on the turnover intention of elderly care workers.. This study analyzed the impact of different types of perceived human resource management practices on elderly care workers' turnover intentions and whether this relationship is moderated by person-job fit and ownership of elderly care institutions.

METHODS

This is a cross-sectional and regional survey study. The study included questionnaire survey data from a total of 305 elderly care workers from 42 elderly care institutions in 21 provinces in China during June to September 2021. Descriptive statistics, Pearson correlation coefficient, multiple regression, and heterogeneity analyses were performed.

RESULTS

Perceived work environment ( β =-0.5164, p< 0.01), perceived occupational protection ( β =-0.3390, p< 0.01), perceived welfare benefits ( β = -0.2620, p< 0.01) and perceived competency training ( β = -0.1421, p< 0.1) were all significantly and negatively related to turnover intentions, the quality of perceived work environment has the greatest impact on elderly care workers' turnover intentions. Under the moderating effects of person-job fit and ownership of elderly care institutions, there existed heterogeneity between perceived human resource management and turnover intentions among elderly care workers. High level of person-job fit and elderly care institutions' public feature can effectively weaken the negative impact of each type of perceived human resource management on turnover intention among elderly care workers.

CONCLUSIONS

The managers of elderly care institutions should optimize the management mechanism, typically pay attention to elderly care workers' working environment, formulate and improve the professional standards and job requirements for elderly care workers, promote the public welfare value of nursing care services, and strengthen the sense of honor and responsibility of elderly care workers to reduce the turnover rate.

Association of visceral fat area or BMI with arterial stiffness in ideal cardiovascular health metrics among T2DM patients

BACKGROUND

"Obesity paradox" occurs in type 2 diabetes mellitus (T2DM) patients when body mass index (BMI) is applied to define obesity. We examined the association of visceral fat area (VFA) as an obesity measurement with arterial stiffness in seven ideal cardiovascular health metrics (ICVHMs).

METHODS

A total of 29 048 patients were included in the analysis from June 2017 to April 2021 in 10 sites of National Metabolic Management Centers. ICVHMs were modified from the recommendations of the American Heart Association. Brachial-ankle pulse wave velocity (BaPWV) ≥ 1400 cm/s was employed to evaluate increased arterial stiffness. Multivariate regression models were used to compare the different effects of BMI and VFA on arterial stiffness.

RESULTS

Lower VFA was more strongly associated with low BaPWV than lower BMI when other ICVHMs were included (adjusted odds ratio [OR], 0.85 [95% confidence interval [CI], 0.80-0.90] vs OR 1.08 [95% CI, 1.00-1.17]). Multivariable-adjusted ORs for arterial stiffness were highest in patients with the VAT area VFA in the range of 150-200 cm2 (adjusted OR, 1.26 [95% CI 1.12-1.41]). Compared with participants with VAT VFA < 100 cm2 , among participants with higher VAT VFA, the OR for arterial stiffness decreased gradually from 1.89 (95% CI, 1.73-2.07) in patients who had ≤1 ICVHM to 0.39 (95% CI, 0.25-0.62) in patients who had ≥5 ICVHMs.

CONCLUSION

In patients with T2DM, using VAT for anthropometric measures of obesity, VFA was more relevant to cardiovascular risk than BMI in the seven ICVHMs. For anthropometric measures of obesity in the ICVHMs to describe cardiovascular risk VFA would be more optimal than BMI.

Research on Pneumatic Control of a Pressurized Self-Elevating Mat for an Offshore Wind Power Installation Platform

Efficient deep-water offshore wind power installation platforms with a pressurized self-elevating mat are a new type of equipment used for installing offshore wind turbines. However, the unstable internal pressure of the pressurized self-elevating mat can cause serious harm to the platform. This paper studies the pneumatic control system of the self-elevating mat to improve the precision of its pressure control. According to the pneumatic control system structure of the self-elevating mat, the pneumatic model of the self-elevating mat is established, and a conventional PID controller and fuzzy PID controller are designed and established. It can be seen via Simulink simulation that the fuzzy PID controller has a smaller adjustment time and overshoot, but its anti-interference ability is relatively weak. The membership degree and fuzzy rules of the fuzzy PID controller are optimized using a neural network algorithm, and a fuzzy neural network PID controller based on BP neural network optimization is proposed. The simulation results show that the overshoot of the optimized controller is reduced by 9.71% and the stability time is reduced by 68.9% compared with the fuzzy PID. Finally, the experiment verifies that the fuzzy neural network PID controller has a faster response speed and smaller overshoot, which improves the pressure control accuracy and robustness of the self-elevating mat and provides a scientific basis for the engineering applications of the self-elevating mat.

Dual Attention and Patient Similarity Network for drug recommendation

MOTIVATION

Artificially making clinical decisions for patients with multi-morbidity has long been considered a thorny problem due to the complexity of the disease. Drug recommendations can assist doctors in automatically providing effective and safe drug combinations conducive to treatment and reducing adverse reactions. However, the existing drug recommendation works ignored two critical information. (i) Different types of medical information and their interrelationships in the patient's visit history can be used to construct a comprehensive patient representation. (ii) Patients with similar disease characteristics and their corresponding medication information can be used as a reference for predicting drug combinations.

RESULTS

To address these limitations, we propose DAPSNet, which encodes multi-type medical codes into patient representations through code- and visit-level attention mechanisms, while integrating drug information corresponding to similar patient states to improve the performance of drug recommendation. Specifically, our DAPSNet is enlightened by the decision-making process of human doctors. Given a patient, DAPSNet first learns the importance of patient history records between diagnosis, procedure and drug in different visits, then retrieves the drug information corresponding to similar patient disease states for assisting drug combination prediction. Moreover, in the training stage, we introduce a novel information constraint loss function based on the information bottleneck principle to constrain the learned representation and enhance the robustness of DAPSNet. We evaluate the proposed DAPSNet on the public MIMIC-III dataset, our model achieves relative improvements of 1.33%, 1.20% and 2.03% in Jaccard, F1 and PR-AUC scores, respectively, compared to state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

The source code is available at the github repository: https://github.com/andylun96/DAPSNet.

Artificial Intelligent Olfactory System for the Diagnosis of Parkinson's Disease

Background: Currently, Parkinson's disease (PD) diagnosis is mainly based on medical history and physical examination, and there is no objective and consistent basis. By the time of diagnosis, the disease would have progressed to the middle and late stages. Pilot studies have shown that a unique smell was present in the skin sebum of PD patients. This increases the possibility of a noninvasive diagnosis of PD using an odor profile. Methods: Fast gas chromatography (GC) combined with a surface acoustic wave sensor with embedded machine learning (ML) algorithms was proposed to establish an artificial intelligent olfactory (AIO) system for the diagnosis of Parkinson's through smell. Sebum samples of 43 PD patients and 44 healthy controls (HCs) from Fourth Affiliated Hospital of Zhejiang University School of Medicine, China, were smelled by the AIO system. Univariate and multivariate methods were used to identify the significant volatile organic compound (VOC) features in the chromatograms. ML algorithms, including support vector machine, random forest (RF), k nearest neighbor (KNN), AdaBoost (AB), and Naive Bayes (NB), were used to distinguish PD patients from HC based on the VOC peaks in the chromatograms of sebum samples. Results: VOC peaks with average retention times of 5.7, 6.0, and 10.6 s, respectively, corresponding to octanal, hexyl acetate, and perillic aldehyde, were significantly different in PD and HC. The accuracy of the classification based on the significant features was 70.8%. Based on the odor profile, the classification had the highest accuracy and F1 of the five models with 0.855 from NB and 0.846 from AB, respectively, in the process of model establishing. The highest specificity and sensitivity of the five classifiers were 91.6% from NB and 91.7% from RF and KNN, respectively, in the evaluating set. Conclusions: The proposed AIO system can be used to diagnose PD through the odor profile of sebum. Using the AIO system is helpful for the screening and diagnosis of PD and is conducive to further tracking and frequent monitoring of the PD treatment process.

Effects of basal and premixed insulin on glycemic control in type 2 diabetes patients based on multicenter prospective real-world data

BACKGROUND

To investigate the different efficacies of glycemic control between basal and premixed insulin in participants with type 2 diabetes (T2DM) when non-insulin medications fail to reach treatment targets.

METHODS

This was a prospective, large-scale, real-world study at 10 diabetes centers in China. Between June 2017 and June 2021, we enrolled 1104 T2DM participants initiated with either once-daily basal insulin or twice-daily premixed insulin when the glycosylated hemoglobin (HbA1c) control target was not met after at least two non-insulin agents were administered. A Cox proportional hazards regression model adjusting for multiple influencing factors was performed to compare the different effects of basal and premixed insulin on reaching the HbA1c control target.

RESULTS

At baseline, basal insulin (57.3%) was prescribed more frequently than premixed insulin (42.7%). Patients with a higher body mass index (BMI) or higher HbA1c levels were more likely to receive premixed insulin than basal insulin (both p < 0.001). After a median follow-up of 12.0 months, compared to those with premixed insulin, the hazard ratio for reaching the HbA1c target to those with basal insulin was 1.10 (95% CI, 0.92-1.31; p = 0.29) after adjustment, and less weight gain was observed in those with basal insulin than with premixed insulin (percentage change of BMI from baseline -0.37[5.50]% vs 3.40[6.73]%, p < 0.0001).

CONCLUSIONS

In this real-world study, once-daily basal insulin was more frequently prescribed and had similar glycemic control effects but less weight gain compared with twice-daily premixed insulin when used as initiation therapy for those in whom glycemic control with non-insulin medications failed.

Probiotic Potential of a Folate-Producing Strain Latilactobacillus sakei LZ217 and Its Modulation Effects on Human Gut Microbiota

Folate is a B-vitamin required for DNA synthesis, methylation, and cellular division, whose deficiencies are associated with various disorders and diseases. Currently, most folic acid used for fortification is synthesized chemically, causing undesirable side effects. However, using folate-producing probiotics is a viable option, which fortify folate in situ and regulate intestinal microbiota. In this study, the folate production potential of newly isolated strains from raw milk was analyzed by microbiological assay. Latilactobacillus sakei LZ217 showed the highest folate production in Folic Acid Assay Broth, 239.70 ± 0.03 ng/μL. The folate produced by LZ217 was identified as 5-methyltetrahydrofolate. LZ217 was tolerant to environmental stresses (temperature, pH, NaCl, and ethanol), and was resistant to gastrointestinal juices. Additionally, the in vitro effects of LZ217 on human gut microbiota were investigated by fecal slurry cultures. 16S rDNA gene sequencing indicated that fermented samples containing LZ217 significantly increased the abundance of phylum Firmicutes and genus Lactobacillus, Faecalibacterium, Ruminococcus 2, Butyricicoccus compared to not containing. Short-chain fatty acids (SCFAs) analysis revealed that LZ217 also increased the production of butyric acid by fermentation. Together, L. sakei LZ217 could be considered as a probiotic candidate to fortify folate and regulate intestinal microecology.

Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic

Coronavirus Disease 2019 (COVID-19) is a highly infectious virus that has created a health crisis for people all over the world. Social distancing has proved to be an effective non-pharmaceutical measure to slow down the spread of COVID-19. As unmanned aerial vehicle (UAV) is a flexible mobile platform, it is a promising option to use UAV for social distance monitoring. Therefore, we propose a lightweight pedestrian detection network to accurately detect pedestrians by human head detection in real-time and then calculate the social distancing between pedestrians on UAV images. In particular, our network follows the PeleeNet as backbone and further incorporates the multi-scale features and spatial attention to enhance the features of small objects, like human heads. The experimental results on Merge-Head dataset show that our method achieves 92.22% AP (average precision) and 76 FPS (frames per second), outperforming YOLOv3 models and SSD models and enabling real-time detection in actual applications. The ablation experiments also indicate that multi-scale feature and spatial attention significantly contribute the performance of pedestrian detection. The test results on UAV-Head dataset show that our method can also achieve high precision pedestrian detection on UAV images with 88.5% AP and 75 FPS. In addition, we have conducted a precision calibration test to obtain the transformation matrix from images (vertical images and tilted images) to real-world coordinate. Based on the accurate pedestrian detection and the transformation matrix, the social distancing monitoring between individuals is reliably achieved.

The synthesis of a nanodrug using metal-based nanozymes conjugated with ginsenoside Rg3 for pancreatic cancer therapy

Nanozymes have limited applications in clinical practice due to issues relating to their safety, stability, biocompatibility, and relatively low catalytic activity in the tumor microenvironment (TME) in vivo. Herein, we report a synergistic enhancement strategy involving the conjugation of metal-based nanozymes (Fe@Fe₃O₄) with natural bioactive organic molecules (ginsenoside Rg3) to establish a new nanodrug. Importantly, this metal-organic nanocomposite drug ensured the stability and biosafety of the nanozyme cores and the cellular uptake efficiency of the whole nanodrug entity. This nanodrug is based on integrating the biological characteristics and intrinsic physicochemical properties of bionics. The glycoside chain of Rg3 forms a hydrophilic layer on the outermost layer of the nanodrug to improve the biocompatibility and pharmacokinetics. Additionally, Rg3 can activate apoptosis and optimize the activity and status of normal cells. Internal nanozymes enter the TME and release Fe³⁺ and Fe²⁺, and the central metal Fe(0) continuously generates highly active Fe²⁺ under the conditions of the TME and in the presence of Fe³⁺, maintaining the catalytic activity. Therefore, these nanozymes can effectively produce reactive oxygen species and oxygen in the TME, thereby promoting the apoptosis of cancer cells. Thus, we propose the use of a new type of metal-organic nanocomposite material as a synergistic strategy against cancer.

Effects of Associated Minerals on the Co-Current Oxidizing Pyrolysis of Oil Shale in a Low-Temperature Stage

Low-temperature co-current oxidizing pyrolysis, which can achieve high recovery of hydrocarbons without significant oil loss, has great potential to reduce the huge external energy required for oil shale conversion. However, this promising method is far from being fully understood, especially the unknown competing mechanism of different types of inorganic minerals in promoting or inhibiting hydrocarbon generation. In this study, the raw Huadian oil shale (HD-R), its carbonate-free (HD-C-F), and carbonate-silicate-free (HD-CS-F) samples obtained through acid treatment are used to investigate the effects of associated minerals on the oil shale co-current oxidizing pyrolysis. The results of shale oil yields of HD-R, HD-C-F, and HD-CS-F were 41.53, 22.38, and 33.97%, respectively, indicating that silicates inhibited, while carbonates catalyzed the formation of shale oil during the co-current oxidizing pyrolysis. Meanwhile, silicates increase the alkane content and decrease the alkene content in shale oil via promoting the combination of hydrogen radicals and alkyl radicals. On the contrary, alkali metals and alkaline earth metals in carbonates inhibit the binding activity of hydrogen radicals and alkyl radicals, concurrently enhancing the release of hydrogen-free radicals of alkyl radicals to form more alkenes. The removal of carbonates could enhance the conversion of organic carbon into hydrocarbons, and the silicates will strengthen the conversion process. It is hoped that this experiment can further enrich and perfect the basic theory of oil shale pyrolysis and provide a reliable reference for the pretreatment of oil shale conversion.

Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study

INTRODUCTION

Early screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study was to validate an artificial intelligence-enabled DR screening and to investigate the prevalence of DR in adult patients with diabetes in China.

RESEARCH DESIGN AND METHODS

The study was prospectively conducted at 155 diabetes centers in China. A non-mydriatic, macula-centered fundus photograph per eye was collected and graded through a deep learning (DL)-based, five-stage DR classification. Images from a randomly selected one-third of participants were used for the DL algorithm validation.

RESULTS

In total, 47 269 patients (mean (SD) age, 54.29 (11.60) years) were enrolled. 15 805 randomly selected participants were reviewed by a panel of specialists for DL algorithm validation. The DR grading algorithms had a 83.3% (95% CI: 81.9% to 84.6%) sensitivity and a 92.5% (95% CI: 92.1% to 92.9%) specificity to detect referable DR. The five-stage DR classification performance (concordance: 83.0%) is comparable to the interobserver variability of specialists (concordance: 84.3%). The estimated prevalence in patients with diabetes detected by DL algorithm for any DR, referable DR and vision-threatening DR were 28.8% (95% CI: 28.4% to 29.3%), 24.4% (95% CI: 24.0% to 24.8%) and 10.8% (95% CI: 10.5% to 11.1%), respectively. The prevalence was higher in female, elderly, longer diabetes duration and higher glycated hemoglobin groups.

CONCLUSION

This study performed, a nationwide, multicenter, DL-based DR screening and the results indicated the importance and feasibility of DR screening in clinical practice with this system deployed at diabetes centers.

TRIAL REGISTRATION NUMBER

NCT04240652.

Promoting information technology for the sustainable development of the phosphate fertilizer industry: a case study of Guizhou Province, China

The information technology revolution has brought unprecedented opportunities to the sustainable development of the traditional phosphate fertilizer industry. In this paper, the changes in characteristic indexes during this technological progress and business innovation are investigated at the industrial level and for different stakeholders using scenario simulation analysis based on system dynamics. The results show that information technology will have a significant impact on the traditional fertilizer industry. The popularity of information technology represents a win-win situation for industries, farmers, enterprises and governments. The sustainable development of the phosphate fertilizer industry promoted by information technology means that agrochemical services are a new growth point for the industry, and farmers will be the largest beneficiaries. Enterprises will adjust their product structures to achieve the relevant phosphate reduction goals before 2020. At the government level, the indirect benefits from energy savings, water conservation and reductions in non-point source pollution control treatment also increase significantly. In the new production and sales model, the development of the phosphate fertilizer industry is completely decoupled from resource consumption. In the future, this technological progress will eventually form a sustainable network of industrial innovation patterns. Our finding suggests that the application of information technology in the phosphate fertilizer industry can stimulate the vitality of each entity in the industry and achieve a win-win situation.