Effects Of Different Models Research Articles

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.

DualSeg: Fusing transformer and CNN structure for image segmentation in complex vineyard environment

Semantic segmentation is a kind of classification at the pixel level, which has a remarkable effect in dealing with complex scenes. Most popular semantic segmentation models are based on Convolutional Neural Network (CNN) and Transformer structures. Convolution operations and attention modules are good at extracting local and global features. In this paper, we propose a parallel network structure, termed DualSeg, which leverages the advantages of CNN at local processing and Transformer at global interaction. And a feature fusion module (FFM) is designed to fuse parameters between modules in different branches to retain local and global features to the maximum extent. In the vineyards, the grape clusters and grape peduncles are often heavily obscured by leaves, branches and clusters, making it difficult to distinguish them accurately. Therefore, the scene is taken as an example to verify the segmentation effect of the model. In the experiment, the DualSeg model and mainstream segmentation models are used to test the segmentation effect of different models in this scene. And the experimental results showed that the DualSeg model had the best segmentation effect of all models. Specifically, the model had an IoU value of grape peduncle segmentation of 72.1%, which was more than 3.9% higher than that of other models in the case of 80 K iterations. In addition, the mIoU value of this model is 83.7%, which was the highest value compared with other models. The demonstrated performance of DualSeg model indicated that harvesting robots could be use it.

Recorrido por los Modelos Teóricos y Prácticos en la Toma de Decisiones en Organizaciones

This study provides a comprehensive review of theoretical and practical models of decision making within organizations, highlighting the challenges and limitations of their application. The objective was to analyze the applicability and effectiveness of different models, from rational to incremental, and their influence on organizational performance. The methodology employed included a literature search in Scopus, restricted to articles published between 2019 and 2024, with a thematic trend analysis that identified more than 500 relevant papers. The results highlighted that rational models face practical barriers, while intuitive models are useful in uncertain contexts but introduce biases. It was also noted that organizational culture and technology influence the choice and effectiveness of these models. In the discussion, the need to adapt decision-making approaches to the particular context of each organization was stressed, integrating flexible elements that consider both human and technological constraints. It concludes that effective management of these models can optimize decision processes and improve organizational performance in highly competitive environments.

Preservation of Dopamine Levels in a Mouse Model of Parkinson's Disease by Carboxymethylated Silica and Starch Nanoparticles Coupled to Silybin

AbstractSilybin has a neuroprotective effect in different models of neurodegenerative diseases as the MPTP‐induced parkinsonian model. However, silybin has poor water solubility, decreasing its efficacy when administered orally. Therefore, the search for possible vehicles or transport systems is relevant. Among the options are the drug delivery systems (DDS). In this work, we evaluated if the use of specific solvents (water or oil) or DDS [carboxymethylated silica nanoparticles (SiO2_SIL) or starch (CMS‐SIL)] would preserve the neuroprotective effect of silybin when administered orally in a Parkinson's model induced with 1‐methyl‐4‐phenyl‐1,2,3,6‐tetrahydropyridine (MPTP). C57BL/6J male mice were exposed to MPTP (30 mg/kg i.p) and, 30 minutes after, were treated with 100 mg/kg of silybin in oil or water as solvents or coupled to silica nanoparticles or carboxymethyl starch for five consecutive days. After the last administration of MPTP and silybin, striatal dopamine levels were determined on day seven. Our results showed that silybin in water as the non‐vehicle control had no protective effect in the MPTP model. Silybin in oil preserved 57 % dopamine levels in contrast to 72 % with SiO2_SIL and 50.7 % with CSM‐SIL. In conclusion, we demonstrated that silybin was effectively coupled to carboxymethylated silica nanoparticles and carboxymethylated starch‐based DDS without losing its neuroprotective effects.

Spectral reflectance fitting based on land-based hyperspectral imaging and semi-empirical kernel-driven model for typical camouflage materials

The reflectance of an object is a physical quantity that is related to a variety of factors such as wavelength, direction of light source, direction of detection, and weather conditions. If complete spectral information about the target is to be obtained, this can only be done by measuring the spectral reflectance in all angular directions. Obviously, this method of acquiring spectral data has the disadvantages of complex operation, low efficiency and poor timeliness in military applications. The Semi-Empirical kernel-driven model captures the main factors affecting the bidirectional reflective properties of an object and uses physically meaningful kernel parameters to characterise the reflective properties of an object. By measuring these kernel parameters and combining them with a small number of measurements, it is possible to extrapolate and fit the spectral reflectance of the target in all directions, improving the efficiency of information acquisition and processing. Semi-empirical kernel-driven models were initially used to study the composition and structure of vegetation and its spectral reflectance properties with some results. However, whether the semi-empirical kernel-driven model can be effectively used to study the spectral reflectance properties of military materials has not been verified. This paper first introduces three commonly used semi-empirical kernel-driven models, namely RossThick-LiSparseR (RTLSR), RossThick-LiTransitN (RTLT) and RossThick-Roujean (RTR). Then, the spectral reflectance of four typical military materials was measured using an imaging spectrometer, and the fitting effects of different models were evaluated. Experiments show that the three semi-empirical kernel-driven models have good data fitting ability for different types of military materials. Overall, RTLSR model has the best data fitting ability and the best stability of inversion results.

Сравнительный анализ воздействия экспериментальных моделей светового десинхроноза на агрегационную активность тромбоцитов

The effect of different models of artificial light exposure on the aggregation activity of platelets of white male rats using a continuous Light-Light (LL) lighting model and models with alternating Light-Dark (12:10) and Light-Dark (18:6) light and dark modes was studied. Analysis of platelet aggregation activity was performed with a computerized 230LA "Biola" aggregation analyzer using the method of V.A. Gabbasov. It was found that under light exposure in the body of laboratory animals there is an increase of platelet aggregation activity accompanied by an increase in the parameters of the weighted average radius curve and the parameters of the light transmittance curve. Alteration of circadian rhythms in the form of impaired photoperiodism leads to an increase in platelet aggregation activity and provokes the development of microcirculatory disorders. The most pronounced changes in the microcirculatory bed by day 10 of the experiment are formed using the Light-Dark model (18:6).

Investigation of the Cross Sections and Effect of Level Density Models for Platinum Element

In cases where experimental studies cannot be carried out and there is no experimental data with it, studies carried out with theoretical models shed light on the researchers' knowledge of different data. The most important of this data is the measurable or calculatable influence cross-section value, which is defined as the probability of a reaction occurrence. Examining the possible effects of different models in the calculation of the effect section is important for the correct calculation of this value. The most important data, the cross section of influence, has taken its place in the radioisotope world as well as in many areas of nuclear physics. With developing technology and advancing science, radioisotopes have widespread and diversified uses. Most commonly, radioisotopes are used in medical diagnosis and treatment applications. Among the many radioisotopes used for this purpose, 191-199Au radioisotopes are also important in terms of both their benefits and characteristics in medical applications. In this respect, the study aimed to investigate the effects of different nuclear level density models in production impact cross-section calculations of 191-199Au radioisotopes with deuteron reference. Theoretical cross sections using the TALYS code have been simulated for all isotopes. The results of the calculations obtained were compared with each other and with the experimental data in the literature and it was aimed to determine the most compatible level density models according to the reaction situations examined.

A Machine Learning Approach for Air-Quality Forecast by Integrating GNSS Radio Occultation Observation and Weather Modeling

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach.

Empagliflozin enhances neuroplasticity in rotenone-induced parkinsonism: Role of BDNF, CREB and Npas4

AimsParkinsonism is characterized by degeneration of dopaminergic neurons and impairment in neuroplasticity. Empagliflozin (EMPA) is an anti-diabetic drug that has been shown to improve cognitive dysfunctions and exerted antioxidant and anti-inflammatory effects in different models. This study aimed to determine the neuroprotective effects of EMPA against rotenone (ROT)-induced parkinsonism. Main methodsROT (1.5 mg/kg) was injected subcutaneously three times per week for two successive weeks. Mice were treated with EMPA (3 and 10 mg/kg, orally) for one week prior ROT administration and for another two weeks along with ROT. After that, motor functions and histopathological changes were assessed, and brains were isolated for biochemical analyses and immunohistochemical investigation. Key findingsResults indicated that, in a dose dependent manner, EMPA improved motor functions and histopathological changes induced by ROT, increased brain content of reduced glutathione (GSH), dopamine (DA), peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), nuclear factor erythroid 2–related factor 2 (Nrf2), inositol trisphosphate (IP3), calcium (Ca2+), calcium/calmodulin-dependent protein kinase type IV (CaMKIV) and phospho-Protein kinase B (p-Akt) levels compared to ROT group. Additionally, EMPA decreased the levels of malondialdehyde (MDA), and tumor necrosis factor-α (TNF-α), and inactivated glycogen synthase kinase-3 beta (GSK-3β). Improvement in neuroplasticity was also observed indicated by elevation in brain derived neurotrophic factor (BDNF), cAMP response element-binding protein (CREB), and neuronal PAS domain Protein 4 (Npas4). SignificanceEMPA improved motor functions possibly through improving neuroplasticity markers and antioxidant, anti-inflammatory, and neuroprotective effects in a dose dependent manner.

Detection of maize seed germination rate based on improved locally linear embedding

Locally linear embedding (LLE) is a nonlinear dimensionality reduction method, which has great advantages over linear dimensionality reduction methods. However, the traditional LLE takes the Euclidean distance as the distance measure, which is difficult to accurately reflect the spatial position relationship between the high-dimensional data of the near-infrared spectrum, resulting in a poor modeling effect. This paper tries to improve the LLE with different distance metric methods and proposes a rapid detection method for maize seed germination rate based on improved local linear embedding and near-infrared spectroscopy. In this paper, a total of 315 samples from 7 different types of maize seeds, purchased from the seed market, were adopted as the research object. We performed artificial aging tests with 8 different gradients (from 0d to 7d with interval of 1d) on them and completed the germination rate test after collecting the near-infrared spectral data for each sample. The Monte Carlo cross-validation (MCCV) algorithm, combined with PLSR and SVM, was used to remove abnormal samples from the spectral and germination rate data. And then, for comparison, we used several different improvement strategies for LLE (traditional Euclidean distance, Manhattan distance, Chebyshev distance, Correlation coefficient, and Cosine similarity) to reduce the spectral data dimension and established PLS and SVM germination rate prediction models. We compared the prediction effects of different models to explore the optimal improvement strategy of LLE dimension reduction distance measurement. The results showed that the cosine similarity was the best improvement strategy under the same modeling method. The R2 of the LLE_ cos-PLS model's test set can reach 0.8384, and the R2 of the LLE_cos-SVM model's test set can reach 0.8765. The results showed that the cosine similarity could better reflect the spatial distribution in the spectral data of aged maize seeds, and the precision of the model was higher after LLE_cos dimensionality reduction. Compared with the linear modeling method PLS, the nonlinear modeling method SVM is more suitable for predicting the germination rate of maize seeds. This study can provide a reference method for the quality inspection of other agricultural products.

Parkinson’s disease and palliative care: a quality of care Delphi study

ObjectivesExtending palliative care services to those with long-term neurological conditions is a current aim of UK health policy. Lack of holistic guidelines for palliative and end-of-life care, and differing models...

Andrographolide contributes to spinal cord injury repair via inhibition of apoptosis, oxidative stress and inflammation.

Background: Spinal cord injury (SCI) is a common disorder of the central nervous system with considerable socio-economic burden. Andrographolide (Andro), the main active component of Andrographis paniculata, has exhibited neuroprotective effects in different models of neurological diseases. The aim of this study was to evaluate the neuroprotective effects of Andro against SCI and explore the related mechanisms. Methods: SCI was induced in rats by the Allen method, and the modeled animals were randomly divided into sham-operated, SCI, SCI + normal saline (NS) and SCI + Andro groups. The rats were injected intraperitoneally with Andro (1 mg/kg) or the same volume of NS starting day one after the establishment of the SCI model for 28 consecutive days. Post-SCI tissue repair and functional recovery were evaluated by measuring the spinal cord water content, footprint tests, Basso-Beattie-Bresnahan (BBB) scores, hematoxylin-eosin (HE) staining and Nissl staining. Apoptosis, oxidative stress and inflammation, as well as axonal regeneration and remyelination were analyzed using suitable markers. The in vitro model of SCI was established by treating cortical neurons with H2O2. The effects of Andro on apoptosis, oxidative stress and inflammation were evaluated as indicated. Results: Andro treatment significantly improved tissue repair and functional recovery after SCI by reducing apoptosis, oxidative stress and inflammation through the nuclear factor E2-related factor 2/heme oxygenase-1 (Nrf-2/HO-1) and nuclear factor-kappa B (NF-κB) signaling pathways. Furthermore, Andro treatment promoted M2 polarization of the microglial cells and contributed to axonal regeneration and remyelination to improve functional recovery after SCI. In addition, Andro also attenuated apoptosis, oxidative stress and inflammation in H2O2-stimulated cortical neurons in vitro. Conclusion: Andro treatment alleviated SCI by reducing apoptosis, oxidative stress and inflammation in the injured tissues and cortical neurons, and promoted axonal regeneration and remyelination for functional recovery.

Optimization of MLP neural network for modeling flow boiling performance of Al2O3/water nanofluids in a horizontal tube

In this paper, a multilayer perceptron (MLP) artificial neural network (ANN) with a back-propagation (BP) training algorithm is applied for modeling thermophysical properties and subcooled flow boiling performance of Al2O3/water nanofluid in a horizontal tube. The influence of nanofluid concentration, heat flux, and flow rate on different thermophysical parameters, including thermal conductivity, thermal conductivity enhancement, viscosity, viscosity enhancement, and heat transfer coefficient, are investigated. Specifically, flow boiling of Al2O3/water nanofluid in a horizontal tube is modeled with the MLP neural network optimized by three novel swarm-based optimization algorithms: namely, Equilibrium Optimizer (EO), Marine Predators Algorithm (MPA), and Slime Mould Algorithm (SMA). To evaluate the effectiveness of different models, the MSE (Mean-Square Error) of the ANN model with varying optimization algorithms is calculated and compared. Additionally, the optimal network and regression values for each parameter are determined. The results show that the applied neural network and optimization algorithms could model the thermal conductivity, thermal conductivity enhancement, and viscosity better than the viscosity enhancement and heat transfer coefficient. The MSE of the best network for the thermal conductivity is 2.693 × 10−7, while the MSE of the best network for the viscosity enhancement is 0.0598. Also, the EO algorithm achieves the best optimization for the first three outputs, thermal conductivity, thermal conductivity enhancement, and viscosity. In comparison, the MPA algorithm extracts the optimal network for the other two outputs, viscosity enhancement, and heat transfer coefficient.

Comparative Analysis of Deep Learning Algorithm for Cancer Classification using Multi-omics Feature Selection

Advancement of high-throughput technologies in omics studies had produced large amount of information that enables integrated analysis of complex diseases. Complex diseases such as cancer are often caused by a series of interactions that involve multiple biological mechanisms. Integration of multi-omics data allows more advanced analysis using features from various aspects of biology. However, analysing cancer multi-omics data on a large scale could be challenging due to the high dimensionality of the data. The recent development of advanced computational algorithms, especially deep learning, had sparkednumerous efforts in applying these algorithms in multi-omics studies. This study aims to investigate how deep learning algorithms, namely stacked denoising autoencoder (SDAE) and variational autoencoder (VAE) can be used in cancer classification using multi-omics data. Moreover, this study also investigates the impact of feature selection in multi-omics analysis through the implementation of an embedded feature selection. The multi-omics data used in this study includes genomics, methylomics, transcriptomics and clinical data for a case study of lung squamous cell carcinoma. The classification performance has beencompared and discussed in terms of the effectiveness of different models and the impact of feature selection. Results showed that VAE outperforms SDAE with 91.86% accuracy, 22.73% specificity and 0.21% Matthews Correlation Coefficient (MCC).

Evolving knowledge in surgical oncology of pancreatic cancer: from theory to clinical practice—a fifteen-year journey at a tertiary referral centre

Pancreatic ductal adenocarcinoma (PDAC) is an increasing disease having a poor prognosis. The aim of the present study was to evaluate the effect of different models of care for pancreatic cancer in a tertiary referral centre in the period 2006–2020. Retrospective study of patients with PDAC observed from January 2006 to December 2020. The demographic and clinical data, and data regarding the imaging techniques used, preoperative staging, management, survival and multidisciplinary tumour board (MDTB) evaluation were collected and compared in three different periods characterised by different organisation of pancreatic cancer services: period A (2006–2010); period B (2011–2015) and period C (2016–2020). One thousand four hundred seven patients were analysed: 441(31.3%) in period A; 413 (29.4%) in B and 553 (39.3%) in C. The proportion of patients increased significantly, from 31.3% to 39.3% (P = 0.032). Body mass index (P = 0.033), comorbidity rate (P = 0.002) and Karnofsky performance status (P < 0.001) showed significant differences. Computed tomography scans (P < 0.001), endoscopic ultrasound (P < 0.001), fine needle aspiration, fine needle biopsy (P < 0.001), and fluorodeoxyglucose-positron emission tomography/computed tomography (P < 0.001) increased; contrast-enhanced ultrasound (P = 0.028) decreased. The cTNM was significantly different (P < 0.001). The MDTB evaluation increased significantly (P < 0.001). Up-front surgery and exploratory laparotomy decreased (P < 0.001), neoadjuvant treatment increased (P < 0.001). The present study showed the evolving knowledge in surgical oncology of pancreatic cancer at a tertiary referral centre over the time. The different models of care of pancreatic cancer, in particular the introduction of the MDTB and the institution of a pancreas unit to the decision-making process seemed to be influential.

How is Social Care Provided in Adult Prisons in England and Wales?

AbstractThere is variation in provision of social care in prisons. Our research aimed to understand variation across adult prisons in England and Wales, including: (1) what social care is provided? (2) who delivers social care? (3) what peer support initiatives are used? (4) what social care indicators are relevant? and (5) are there differences between prison type and social care provision? We analysed Her Majesty’s Inspectorate of Prisons (HMIP) reports (published 2017–2020) from 102 prisons. From these reports we extracted and analysed data on social care provision. Elements of social care are not consistently delivered; need assessments (81.4 per cent) and referrals (75.5 per cent) were most frequently reported. Different providers (health care/social care/prison) deliver social care. Forty-one prisons (40.2 per cent) included peer support (formal to informal). We found no notable differences between prison categories and social care delivery, although, within category D prisons, a significantly larger proportion of those with a disability reported receiving support they needed. Inspection reports highlighted that prison social care should mirror community social care, but we could not fully evaluate this due to reporting issues. Social care provision varies; effectiveness of different models is not yet known. We provide recommendations to improve social care reporting within HMIP reports.

Oleuropein attenuates testicular ischemia-reperfusion by inhibiting apoptosis and inflammation

BackgroundIschemia-reperfusion injury (IRI) is the key reason of injury after testicular torsion and may eventually lead to male infertility. Oleuropein, a natural antioxidant isolated from Olea europaea, has shown beneficial effects in different models of ischemia. We evaluated the effects of oleuropein on testicular IRI and explored the underlying protective mechanisms. MethodsA mouse testicular torsion/detorsion (T/D) model and an oxygen-glucose deprivation/reperfusion (OGD/R) germ cell model were established and treated with oleuropein. H&E staining was used to evaluate testicular pathological changes. Apoptosis and apoptosis-associated protein levels in testis tissues were assessed by TUNEL staining, immunohistochemical staining and western blot. Apoptosis levels and apoptosis-associated protein levels in GC-1 were evaluated by flow cytometry, immunofluorescence and western blot. Oxidative stress levels were assessed by malondialdehyde (MDA) and superoxide dismutase (SOD) kits. Cell viability and inflammatory protein levels were evaluated by CCK-8 assay coupled with qRT-PCR. ResultsRelative to the control group, SOD activity was markedly suppressed, while MDA, Bax, Caspase-3, TNF-α as well as IL-1β levels were significantly increased in the T/D model and OGD/R model. However, all of the aforementioned alterations were relieved by oleuropein treatment. ConclusionOur findings indicate that oleuropein may be a promising treatment option to attenuate testicular IRI via its anti-oxidant, anti-inflammatory as well as anti-apoptotic properties.

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER