Effects Of Different Models Research Articles

Stock Volatility Prediction Analysis Based on Different Models

Stock volatility prediction is an important method for making the correct financial decision in modern stock market. Additionally, traditional method of stock indices prediction has been proved by modern scholars to be ineffective. This review focuses on the volatility prediction performance of various models, aiming to figure out the most effective volatility prediction model for modern stock market. The four models discussed in this review are ARIMA, XGBOOST, GARCH, and LSTM. Concluding the effectiveness of different models in volatility prediction can be helpful for practical applications of these models in modern stock markets, which can improve the profitability of stock investors under certain situations. Among all four models discussed in this review, LSTM model shows the best performance among all four models in stock volatility prediction. The unique machine learning ability of LSTM has great potential value in future science field research. Since this study is only a general review of stock volatility prediction models, additional research and analysis are essential in order to further conduct this topic for better accuracy and credibility.

Düşük Kapsamlı WGS Verileri Kullanılarak Hızlı ve Yüksek Doğruluklu Guppy Baz Çağırma Modellerinin Varyant Çağırma Üzerine Etkisinin İncelenmesi

Long-read sequencing technologies such as Oxford Nanopore Technologies (ONT) enabled researchers to sequence long reads fast and cost-effectively. ONT sequencing uses nanopores integrated into semiconductor surfaces and sequences the genomic materials using changes in current across the surface as each nucleotide passes through the nanopore. The default output of ONT sequencers is in FAST5 format. The first and one of the most important steps of ONT data analysis is the conversion of FAST5 files to FASTQ files using “base caller” tools. Generally, base caller tools pre-trained deep learning models to transform electrical signals into reads. Guppy, the most commonly used base caller, uses 2 main model types, fast and high accuracy. Since the computation duration is significantly different between these two models, the effect of models on the variant calling process has not been fully understood. This study aims to evaluate the effect of different models on performance on variant calling.
 In this study, 15 low-coverage long-read sequencing results coming from different flow cells of NA12878 (gold standard data) were used to compare the variant calling results of Guppy. 
 Obtained results indicated that the number of output FASTQ files, read counts and average read lengths between fast and high accuracy models are not statistically significant while pass/fail ratios of the base called datasets are significantly higher in high accuracy models. Results also indicated that the difference in pass/fail ratios arises in a significant difference in the number of called Single Nucleotide Polymorphisms (SNPs), insertions and deletions (InDels). Interestingly the true positive rates of SNPs are not significantly different. These results show that using fast models for SNP calling does not affect the true positive rates statistically. The primary observation in this study, using fast models does not decrease the true positive rate but decreases the called variants that arise due to altered pass/fail ratios. Also, it is not advised to use fast models for InDel calling while both the number of InDels and true positive rates are significantly lower in fast models.
 This study, to the best of our knowledge, is the first study that evaluates the effect of different base calling models of Guppy, one of the most common and ONT-supported base callers, on variant calling.

Analysis of practical applications using the linear programming model

A linear programming model is a mathematical optimization technique used to find the best possible outcome in a mathematical model with linear relationships under a set of constraints. This technique has various applications in engineering, management, economic analysis, and military operations. The linear programming model is the most popular technique for determining the best course of action, and it is essential for decision-making and resource allocation in modern society. This research paper will introduce the standard form of the linear programming model and explore the differences and solutions between three linear programming models: 0-1 integer programming, mixed integer programming, and multi-objective programming. In the case of introducing the application and optimization effect of different models in real life, list research and compare the different applications and research scenarios of the three models to derive the strengths, weaknesses, and characteristics of the different models to achieve the effect of providing more targeted models for different real-life cases.

Residual life prediction of bearings based on RBF approximation models

Abstract Once the failure of rotating machinery occurs, it may cause the whole system to paralyze and cause great economic losses, or it may cause casualties. Therefore, the prediction of the remaining life of bearings is of great significance. The purpose of this paper is to analyze the approximate modeling technology and develop a framework for combined approximate modeling technology. A multi-strategy radial-based approximate model optimization model is proposed based on the limitations of radial-based approximate model technology. Utilizing the weight coefficient solving technique, the variable confidence RBF model, i.e., RBF-LSTM model, is established. Propose the remaining methods for life prediction using the bearing life prediction process. The RBF-LSTM combined approximation model is used to construct the evaluation index for rolling bearing remaining life prediction. Using the empirical analysis method, the optimization effects of different models and the accuracy of bearing remaining life prediction are analyzed, respectively. Experiments show that the data range of the RBF-LSTM combined approximation model is between [23,52], the overall fluctuation range of the data is not large, and the time taken is only 31 s. After 230 calculations, the model optimization effect is better. In the remaining life validation, the starting values of 132h and 148h are less different from real life, only 1.53h and 1.3h, respectively, and the model prediction accuracy is high.

HER-096 is a CDNF-derived brain-penetrating peptidomimetic that protects dopaminergic neurons in a mouse synucleinopathy model of Parkinson’s disease

Cerebral dopamine neurotrophic factor (CDNF) is an unconventional neurotropic factor that modulates unfolded protein response (UPR) pathway signaling and alleviates endoplasmic reticulum (ER) stress providing cytoprotective effects in different models of neurodegenerative disorders. Here, we developed a brain-penetrating peptidomimetic compound based on human CDNF. This compound called HER-096 shows similar potency and mechanism of action as CDNF, and promotes dopamine neuron survival, reduces α-synuclein aggregation and modulates UPR signaling in invitro models. HER-096 is metabolically stable and able to penetrate to cerebrospinal (CSF) and brain interstitial fluids (ISF) after subcutaneous administration, with an extended CSF and brain ISF half-life compared to plasma. Subcutaneously administered HER-096 modulated UPR pathway activity, protected dopamine neurons, and reduced α-synuclein aggregates and neuroinflammation in substantia nigra of aged mice with synucleinopathy. Peptidomimetic HER-096 is a candidate for development of a disease-modifying therapy for Parkinson's disease with a patient-friendly route of administration.

Kynurenine attenuates mitochondrial depolarization and neuronal cell death induced by rotenone exposure independently of AhR-mediated parkin induction in SH-SY5Y differentiated cells

Rotenone is a pesticide commonly used in agriculture that is associated with the risk of developing Parkinson’s disease (PD) by inducing mitochondrial damage. As a protective cell response to different challenges, they activate mitophagy, which involves parkin activity. Parkin is an E3 ubiquitin ligase necessary in the initial steps of mitophagy, and its overexpression protects against parkinsonian effects in different models. Recent studies have reported that the aryl hydrocarbon receptor (AHR), a ligand-dependent transcription factor, induces parkin expression. Kynurenine, an endogenous AHR ligand, promotes neuroprotection in chronic neurodegenerative disorders, such as PD, although its neuroprotective mechanism needs to be fully understood. Therefore, we evaluated whether the overexpression of parkin by AHR activation with kynurenine promotes autophagy and reduces the neurotoxicity induced by rotenone in SH-SY5Y cells differentiated to dopaminergic neurons. SH-SY5Y neurons were treated with rotenone or pretreated with kynurenine or 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), and parkin levels, apoptosis, mitochondrial potential membrane, and autophagy were determined. The results showed that kynurenine and TCDD treatments induced parkin expression in an AHR-dependent manner. Kynurenine pretreatment inhibited rotenone-induced neuronal apoptosis in 17%, and the loss of mitochondrial membrane potential in 30% when compare to rotenone alone, together with a decrease in autophagy. By contrast, although TCDD treatment increased parkin levels, non-neuroprotective effects were observed. The kynurenine protective activity was AHR independent, suggesting that parkin induction might not be related to this effect. On the other hand, kynurenine treatment inhibited alpha amine-3-hydroxy-5-methyl-4-isoxazol propionic acid and N-methyl-D-aspartate receptors, which are well-known excitotoxicity mediators activated by rotenone exposure.

Membrane Localization and Phosphorylation of Indoleamine 2,3-Dioxygenase 2 (IDO2) in A549 Human Lung Adenocarcinoma Cells: First Steps in Exploring Its Signaling Function.

Indoleamine 2,3-dioxygenase 2 (IDO2) is a paralog of Indoleamine 2,3-dioxygenase 1 (IDO1), a tryptophan-degrading enzyme producing immunomodulatory molecules. However, the two proteins are unlikely to carry out the same functions. IDO2 shows little or no tryptophan catabolic activity and exerts contrasting immunomodulatory roles in a context-dependent manner in cancer and autoimmune diseases. The recently described potential non-enzymatic activity of IDO2 has suggested its possible involvement in alternative pathways, resulting in either pro- or anti-inflammatory effects in different models. In a previous study on non-small cell lung cancer (NSCLC) tissues, we found that IDO2 expression revealed at the plasma membrane level of tumor cells was significantly associated with poor prognosis. In this study, the A549 human cell line, basally expressing IDO2, was used as an in vitro model of human lung adenocarcinoma to gain more insights into a possible alternative function of IDO2 different from the catalytic one. In these cells, immunocytochemistry and isopycnic sucrose gradient analyses confirmed the IDO2 protein localization in the cell membrane compartment, and the immunoprecipitation of tyrosine-phosphorylated proteins revealed that kinase activities can target IDO2. The different localization from the cytosolic one and the phosphorylation state are the first indications for the signaling function of IDO2, suggesting that the IDO2 non-enzymatic role in cancer cells is worthy of deeper understanding.

Age-dependent down-regulation of orexin receptors in trigeminal nucleus caudalis correlated with attenuation of orexinergic analgesia in rats

Aging is related to a variety of physiological organ changes, including central and peripheral nervous systems. It has been reported that the orexin signaling has a potential analgesic effect in different models of pain, especially inflammatory pulpal pain. However, the age-induced alteration in dental pain perception and orexin analgesia has not yet been fully elucidated.Here, we tested that how aging may change the effect of orexin-A on nociceptive behaviors in a rat dental pulp pain model. The expression levels of orexin receptors and the nociceptive neuropeptides substance P (SP) and calcitonin-related gene peptide (CGRP) were also assessed in the trigeminal nucleus caudalis (TNC) of young and aged rats. Dental pulp pain was induced by intradental application of capsaicin (100 μg). The immunofluorescence technique was used to evaluate the expression levels.The results show less efficiency of orexin-A to ameliorate pain perception in aged rats as compared to young rats. In addition, a significant decrease in the number of orexin 1 and 2 receptors was observed in the TNC of aged as compared to young rats. Dental pain-induced SP and CGRP overexpression was also significantly inhibited by orexin-A injection into the TNC of young animals. In contrast, orexin-A could not produce such effects in the aged animals.In conclusion, the older age-related reduction of the antinociceptive effect of orexin may be due to the downregulation of its receptors and inability of orexin signaling to inhibit the expression of nociceptive neuropeptides such as SP and CGRP in aged rats.

Power Station Engine Failure Early Warning System Using Thermal Camera

Power station engines are critical infrastructure components that require constant monitoring to prevent failures and ensure an uninterrupted power supply. This paper proposes a failure early warning system based on a thermal camera using a computer vision approach. The system uses a thermal camera to generate thermal images in a video format, which is then processed by an automated fire detection engine and temperature detection engine. The results of these two subsystems are then used as input for an anomaly detection engine, which predicts the likelihood of engine failure. Based on the results of the experiments, it can be concluded that the YOLOv7 model outperforms Faster R-CNN in detecting fires, achieving a higher mAP score on the one-class dataset. The proposed temperature and anomaly detection system also accurately detected temperature levels and anomalies in thermal images. Furthermore, in the failure time prediction experiment, the Holt-Winters additive method with additive errors, additive trend, and additive seasonality model was identified as the best fit among the models evaluated. In contrast, the Decision Tree model showed good performance and a short training time, making it a good choice for applications where training time is critical. These results highlight the importance of selecting the most suitable method for a given application. Moreover, it demonstrates the effectiveness of different models and approaches for engine failure early warning systems in a power station using a thermal camera.

High Discounts and Low Fundamental Surplus: An Equivalence Result for Unemployment Fluctuations

We establish an observational equivalence between unemployment fluctuations of the Diamond-Mortensen-Pissarides search economy augmented with time varying risk premia and an otherwise identical economy without risk premia but with a time varying value of leisure. This equivalence holds for general risk premia processes and allows us to view the effects of different models of risk premia as operating through a single channel—one that alters the value of leisure. We derive simple expressions for semielasticities of labor market tightness with respect to productivity and risk premium shocks. We show wages can be used to detect misspecification in the discount rate process used in hiring decisions. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.03712 .

STF-083010 an inhibitor of IRE1α endonuclease activity affects mitochondrial respiration and generation of mitochondrial membrane potential

STF-083010 is an inhibitor of endonuclease activity of inositol requiring-enzyme 1α (IRE1α) that is involved in activation of IRE1α-XBP1 axis of the unfolded protein response after ER stress. STF-083010 was tested as a possible antitumor agent in some previous studies exhibiting the ability either to induce death of tumour cells or to increase sensitivity of tumours cells to other neoplastic agents. STF-083010 exhibits also hepatoprotective effects in different models of liver injury and hepatic steatohepatitis. We have shown that STF-083010 has significant impact on mitochondrial functions that is not dependent on the way of STF-083010 application. We have observed that STF-083010 decrease of both maximal respiration (representing maximal electron transfer capacity of mitochondrial respiratory chain) and spare respiratory capacity after either incubation of the SH-SY5Y cells with STF-083010 or direct addition of STF-083010 to the respiration medium. In addition, we have documented impact of STF-083010 on generation of mitochondrial membrane potential (ΔΨm) that could be a result of decreased mitochondrial substrate level phosphorylation. Finally, increased sensitivity of ΔΨm to uncoupler in the presence of STF-083010 was documented. Our results indicate that STF-083010 has important impact on mitochondrial functions independently of its ability to inhibit endonuclease activity of IRE1α that is involved in activation of IRE1α-XBP1 axis of the unfolded protein response after ER stress. The impact of STF-083010 on mitochondrial functions could be associated with its possible off-target effect.

Type, Timing, Frequency, and Durability of Outcome of Physical Therapy for Parkinson Disease

Parkinson disease (PD) is a neurodegenerative syndrome affecting approximately 1% of the population older than 60 years, and a major goal of treatment is preservation of physical function through physical therapy (PT). Although PT outcomes for PD are well documented, aggregate information on the parameters of PT are needed to guide implementation. To evaluate current evidence on the types, timing, frequency, duration, and outcomes of PT regimens applied for PD. PubMed, Embase, Medline, and the Web of Science Core Collection were searched for articles published from January 1, 2000, to August 10, 2022. Search terms included terms related to Parkinson disease, PT interventions, and PT-related outcomes. Included studies were peer-reviewed randomized clinical trials available in English of any PT intervention for patients with PD that included PT-related outcomes. The Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline was followed. Two reviewers extracted data and assessed quality using the Cochrane Risk of Bias Tool. Data were analyzed using a random-effects model. A meta-analysis compared outcomes of nonstandard PT vs standard PT and standard PT vs no intervention for Unified Parkinson's Disease Rating Scale (UPDRS) score and measures of gait and balance. A total of 46 trials with 3905 patients were included (range of mean ages, 61-77 years). Ten trials (22%) compared 2 types of nonstandard PT interventions; 26 (57%), nonstandard PT vs standard PT; and 10 (22%), PT vs no intervention. The most common nonconventional PT intervention was aquatic physiotherapy (5 trials [11%]). Durations of PT regimen ranged from 2 to 12 weeks in 39 trials (85%), and PT was most commonly performed with frequencies of either twice or 3 times weekly (27 [59%]). In most trials (39 [85%]), PT session length ranged from 30 to 60 minutes. Across trials, PT outcomes were reported for gait (14 trials [30%]), balance (10 [22%]), quality of life (3 [9%]), and cognition (1 [2%]). Approximately half of the trials (22 [48%]) documented durability of some level of benefit after completion of the prescribed regimen. Meta-analysis showed no significant difference for PT vs no intervention in UPDRS scores (standardized mean difference [SMD], -1.09; 95% CI, -2.50 to 0.33) or for nonstandard PT vs standard PT in measures of gait (SMD, 0.03; 95% CI, -0.53 to 0.59), balance (SMD, 0.54; 95% CI, -0.03 to 1.12), and UPDRS score (SMD, -0.49; 95% CI, -1.04 to 0.06). Meta-analytic regression of moderators revealed no significant differences in outcomes by frequency of PT per week (SMD, 0.17; 95% CI, -0.03 to 0.36). The findings suggest that although a wide range of types and regimens of PT for PD have been tested, comparative effectiveness of different models of care and implementation strategies as well as long-term durability of their outcomes remain undetermined.

The Evaluation of Snow Depth Simulated by Different Land Surface Models in China Based on Station Observations

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Studying sex differences in responses to fibroblast growth factor 21 administration in obese mice consuming a sweet-fat diet.

In animals, obesity caused by consumption of a sweet-fat diet (SFD) is the most adequate mouse model of human diet-induced obesity. Fibroblast growth factor 21 (FGF21) reduces body weight, beneficially affects taste preferences, and corrects glucose metabolism in obese mice. Sex is known to influence FGF21 effects in different models of diet-induced and hereditary obesity. In mice with SFD-induced obesity, the effects of FGF21 have been studied only in males. The aim of this study was to compare the effects of FGF21 on body weight, food preferences and glucose and lipid metabolism in C57Bl/6J male and female mice with SFD-induced obesity. Mice were fed with a diet consisting of standard chow, lard and cookies for 10 weeks, then they were injected with FGF21 (1 mg per 1 kg) or vehicle for 7 days. Body weight, weights of different types of food, blood parameters, glucose tolerance, gene and protein expression in the liver, gene expression in the white, brown adipose tissues, and the hypothalamus were assessed. FGF21 administration reduced body weight, did not alter total energy consumption, and activated orexigenic pathways of hypothalamus in mice of both sexes. However, sex dimorphism was found in the realization of the orexigenic FGF21 action at the transcriptional level in the hypothalamus. Metabolic effects of FGF21 were also sex-specific. Only in males, FGF21 exerted beneficial antidiabetic action: it reduced fatty acid and leptin plasma levels, improved glucose-tolerance, and upregulated hepatic expression of Ppargc1, Fasn, Accα, involved in lipid turnover, gene Insr and protein glucokinase, involved in insulin action. Only in obese females, FGF21 induced preference of standard diet to sweet food. Thus, in mouse model of obesity induced by consumption of a sweet-fat diet, the catabolic effect of FGF21 was not sex-specific and hormonal, transcriptional and behavioral effects of FGF21 were sex-specific. These data suggest elaboration of different approaches to use FGF21 analogs for correction of metabolic consequences of obesity in different sexes.

The niacin receptor HCAR2 induces a protective response of microglia to amyloid pathology.

AbstractBackgroundDietary intake of niacin correlates with reduced risk of Alzheimer´s disease (AD). Niacin is a ligand for the niacin receptor HCAR2, which, in the brain, is expressed by microglia. HCAR2 ligands have been shown to modulate microglia functions and exert neuroprotective effects in different models of neuronal injury. Therefore, we hypothesized that HCAR2 modulates a protective microglial phenotype in AD, which can be stimulated by HCAR2 agonists.MethodTo assess the role of HCAR2 in amyloid pathology, the brains of the AD animal model 5xFAD and 5xFAD mice lacking Hcar2 gene (5xFAD‐KO) were subjected to transcriptomic, immunohistochemistry and biochemical analysis. Furthermore, to activate HCAR2, 5 month‐old 5xFAD mice were treated with a FDA‐approved formulation of niacin (Niaspan®) daily, by oral gavage for 30 days with 100 mg niacin/kg. Treated animals were sacrificed and the brains were collected for immunohistochemistry and biochemical analysis. To validate that Niaspan® effects were dependent on HCAR2, we performed the same treatment paradigm and analysis using 5xFAD‐KO mice.ResultOur results indicate that microglia significantly induce HCAR2 expression in the AD brain. Lack of Hcar2 in the 5xFAD mice leads to a deficient response of microglia to amyloid pathology, which is accompanied by an increase in plaque load and exacerbation of the amyloid‐associated neuropathology. Niaspan® treatment increased the response of microglia to amyloid pathology, decreased plaque burden in 5xFAD, and exerted a neuroprotective effect evidenced by reduced neuronal loss in the subiculum, accompanied by a rescue of working memory deficits. Niaspan® did not induce these beneficial effects in 5xFAD‐KO mice.ConclusionThese data suggest that HCAR2 induces a protective microglial phenotype in AD. Furthermore, activation of HCAR2 by Niaspan® treatment has neuroprotective effects in AD, even after the onset of severe amyloid pathology, possibly through the stimulation of a protective microglia response. Niaspan® is a FDA‐approved drug, thus there is a translational potential of this strategy into clinical practice, supporting further study of this therapeutic approach.

Enhancing palliative care at home: a generalist community nursing case study

Objective: This quality improvement project aimed to explore the experience of home care nurse managers implementing a general palliative approach to care. Implementation comprised several interlinking activities encompassing stakeholder engagement and an education program in the outer eastern region of a home care and nursing organisation in Melbourne, Australia, between September 2019 and December 2020. Background: High quality provision of community based palliative and end-of-life care means people are much more likely to die at home, supported by family, friends and services. To achieve this, care staff must be adequately trained and supported, alongside a collaborative approach with other generalist and specialist providers. Study design and methods: This Case Study reports on the participant perspectives of the stakeholder engagement and education program. Focus groups and an interview were used to elucidate the experience of nursing and allied health professionals providing a general palliative approach in the community. The 2006 National Institute of Clinical Studies framework to identify barriers to best practice healthcare guided the development of focus group questions and the deductive analysis of data. Results: Seven Nursing Care Managers and one Social Worker participated in two online focus groups and one interview. Three overarching themes highlighted the value of tailored education in increasing staff knowledge and confidence, the factors for a successful generalist-specialist partnership, and the unique nature of the home care setting which requires accessible systems and processes. Discussion: This project supports assertions that generalist services can play a valuable role when a palliative approach to care is incorporated. It also underscores the importance of collaborative working partnerships between generalist and specialist services in providing quality community-based palliative care. Future research should examine the needs and perspective of clients, carers and families receiving a general palliative care approach in the community. Implications for research, policy and practice: Investment in developing the capacity and capability of generalist service staff to deliver palliative care is vital, particularly when caring for people in their homes. Although small, this study demonstrates that consideration should be given to embedding staff with specialist palliative care training within generalist organisations to drive and champion evidence-based palliative care provision, training, policies and procedures that meet the unique needs of mobile workforces. Future research should examine the effectiveness of different models and methods for education and generalist-specialist partnerships that meet the needs of mobile home nurses, particularly regarding long-term impact and sustainability. What is already known about the topic? Most Australians prefer to be cared for and die in their own home. Home care nurses are key generalist health professionals who support community-based palliative and end-of-life care, organising and providing care, coordinating the input of other health and social care professionals. Home care nurses face challenges in meeting their clients’ palliative care needs (such as symptom management and communication) and have deficits in their knowledge and confidence of caring for palliative clients. What this paper adds: This case study demonstrated that facilitating ongoing education opportunities that draw on the expertise of local palliative care champions or internal specialists can enhance care provision. Nurse care managers believe the value of generalist services, particularly home care services, in the generalist-specialist palliative care partnership, needs to be understood and respected. Tailored investment in home care to provide a general palliative approach would be beneficial given the unique challenges of this mobile workforce.

Accreditation Approaches for Professional Education Programs: Toward Best Practice

Accreditation has a central role in the quality assurance of professional education programs, but research on the effectiveness of different models of accreditation is limited. The purpose of this study was to rapidly appraise the evidence for the effectiveness, impact, and feasibility of different accreditation approaches, in order to inform best practices for the accreditation of professional education programs. The study focused on accreditation for programs which produce practice-ready graduates, including for veterinary programs. The authors searched several databases for articles published from 2000 to 2020, using search terms identified during a scoping phase, and applied a "rapid review" methodology in line with contextual, time, and resource requirements. Relevant articles which were classed as empirical or conceptual were included in the study, while papers appraised as solely commentaries or descriptive were excluded from the evidence base. The full-text review included 32 articles. We identified a clear transition in the literature from input- and process-based models (pre- and early 2000s) to outcomes-based models (in the 2000s and early 2010s). Continuous quality improvement and targeted models (including risk-based and thematic) represent more recent approaches in accreditation practice. However, as noted by other scholars, we identified limited empirical evidence for the relative effectiveness of different accreditation approaches in professional education, although evidence for the more recent accreditation approaches is emerging. In terms of best practice in view of the current lack of definitive evidence for the adoption of any specific model of accreditation, we argue that accrediting authorities adopt a contextual approach to accreditation which includes clearly articulating the purpose and focus of their regulatory activities, and selecting and implementing accreditation methods that are consistent with their underlying principles.

Exploiting Regressive Model for Population Prediction in China

The demographics of China reveal a huge population, which amounted to around 1.4 billion people in 2022. However, evidence and authorities demonstrate that the Chinese population is about to shrink for the first time and the country’s total fertility rate keeps decreasing. It introduces great uncertainty to the future development of China. An accurate population prediction is important to estimate the economy, make reasonable policies, and stabilize society. In this paper, three machine learning models, including the polynomial regression, logistic growth model and Autoregressive Integrated Moving Average (ARIMA), are used to forecast the population in China. The effectiveness of different models on population forecasting is compared and analyzed. The results show that ARIMA performs the best, which is about a 0.34% error rate validated on previous population data. The prediction results demonstrate that the population in China will experience a brief rise and then enter negative growth. To embrace the population decline, the government should get prepared for the aging society and propose reasonable policies to increase fertility.

TCM2Vec: a detached feature extraction deep learning approach of traditional Chinese medicine for formula efficacy prediction

In current era, the intelligent development of traditional Chinese medicine (TCM) has attracted more and more attention. As the main carrier of clinical medication, formulas use synergies of active substances to enhance efficacy and reduce side effects. Related studies show that there is a nonlinear relationship between the efficacy of formulas and herbs. Deep learning is an effective technique for fitting nonlinear relationships. However, it is not good for using deep learning model directly due to ignoring the characteristics of formulas. In this paper, we propose a detached feature extraction approach (TCM2Vec) based on deep learning for better feature extraction and efficacy prediction. We build two detached encoders, one of it uses cross-feature-based unsupervised pre-training model (FMh2v) to extract the relationship features of herbal medicines for initializing, while the other one simulates multi-dimensional characteristics of medicines by normal distribution. Then we integrate relationships and medicinal characteristics for deep feature extraction. We processed 31,114 unlabeled formulas for pre-training and two classification tasks in-domain for predicting and fine-tuning. One of tasks is multi-classed with 1036 formulas, other one is multi-labelled with 1,723 formulas. For labelled formulas, different feature extraction models based on detached encoder are trained to predict efficacy. Compared with the no pre-training, CBOW and BERT baseline models, FMh2v leads to performance gains. Moreover, the detached encoder offers large positive effects in different models which for efficacy prediction, where ACC increased by 5.80% on average and F1 increased by 12.06% on average. Overall, the proposed feature extraction is an effective method for obtaining characteristic representation of TCM formulas, and provides reference for the adaptability of artificial intelligence technology in the domain of TCM.

Transient thermal shock behavior of the graphene-platelets reinforced annular-shape sector plates in the case of fully-clamped and simply-supported boundary conditions

This research is the first attempt to observe the consequences of the instantaneous thermal shock on the thermo-elastodynamic response of the initially stressed graphene-reinforced nanocomposite annular-shape sector plates resting on the Kerr-type elastic substrate in the background of elasticity theory in its general form. To enable the solvability of the system’s equations for the fully-clamped boundary conditions as well as the simply-supported ones, HDQA is employed. For the first time, the effect of different models of thermal shock in the form of Heaviside, sinusoidal and cosinusoidal functions on the fluctuation of the system’s stress and deflection terms is explored. The convergence of DQA and HDQA with respect to the number of grid nodes are compared to select the solution with the optimal convergence rate, and it is found that HDQA requires a comparatively lower number of grid nodes to present stable results.