Elevated brain temperature under severe heat exposure impairs cortical motor activity and executive function

Background:Excessive heat exposure can lead to hyperthermia in humans,which impairs physical performance and disrupts cognitive function.While heat is a known physiological stressor,it is unclear how severe heat stress affects brain physiology and function.Methods:Eleven healthy participants were subjected to heat stress from prolonged exercise or warm water immersion until their rectal temperatures(T_(re))attained 39.5℃,inducing exertional or passive hyperthermia,respectively.In a separate trial,blended ice was ingested before and during exercise as a cooling strategy.Data were compared to a control condition with seated rest(normothermic).Brain temperature(T_(br)),cerebral perfusion,and task-based brain activity were assessed using magnetic resonance imaging techniques.Results:T_(br)in motor cortex was found to be tightly regulated at rest(37.3℃±0.4℃(mean±SD))despite fluctuations in T_(re).With the development of hyperthermia,T_(br)increases and dovetails with the rising T_(re).Bilateral motor cortical activity was suppressed during high-intensity plantarflexion tasks,implying a reduced central motor drive in hyperthermic participants(T_(re)=38.5℃±0.1℃).Global gray matter perfusion and regional perfusion in sensorimotor cortex were reduced with passive hyperthermia.Executive function was poorer under a passive hyperthermic state,and this could relate to compromised visual processing as indicated by the reduced activation of left lateral-occipital cortex.Conversely,ingestion of blended ice before and during exercise alleviated the rise in both T_(re)and T_(bc)and mitigated heat-related neural perturbations.Conclusion:Severe heat exposure elevates T_(br),disrupts motor cortical activity and executive function,and this can lead to impairment of physical and cognitive performance.

Characterization and Functionalisation of Activated Carbon for the Enhancement of Enzyme Catalyst Activity

Background and objective:Activated carbon is commonly used as an immobilisation matrix due to its large surface area,making it a highly desirable matrix for use in immobilising enzymes as preparation for use on the industrial scale.The objective of this research is to determine the effectiveness of different acids for functionalisation on immobilisation capacity and also to characterize the functionalized activated carbon for the functional groups present.Materials and methods:Activated carbon was functionalised with three acids(hydrochloric acid,nitric acid and sulphuric acid)along with a control sample washed with distilled water.Immobilisation capacity was calculated with hydrochloric acid functionalized activated carbon(HCl-FAC)giving the highest immobilization capacity(6.022 U/g).Characterisation of the functionalised activated carbon was conducted using FT-IR(Fourier Transform Infra-Red)spectroscopy analysis of the samples with the aim of analyzing the various functional groups present to determine the sample with distinct characteristics thus telling the degree of adsorption of lipase onto the activated carbon powder.Results:HNO3-FAC(functionalized activated carbon)showed a very distinct pattern as a larger number of surface functional groups emerged.The immobilisation on a matrix ensures thermal stability and increased reusability of the enzyme.Therefore,in this research,lipase sourced from Candida antarctica was immobilised on acid functionalised activated carbon.The best acid for functionalisation was found to be hydrochloric acid.Conclusion:Due to the very distinct patterns shown by the FT-IR spectrum of the HNO3-FAC after a fair comparison with others,it allows for a larger number of surface functional groups which will definitely enhance the stability of the enzyme lipase.

Correlation of cognitive function with acetylcholinesterase activity and P300 event-related potential of patients with type 2 diabetes mellitus

BACKGROUND: At present, central cholinergic neuron system is regarded the most major structural basis of cognitive function. Changes in structure of cholinergic neuron system of brain and receptor expression after brain injury can cause cognitive impairment. OBJECTIVE: To comparatively observe the intelligence quotient (IQ), latent period and wave amplitude of P300 event-related potential and the difference of activity of acetylcholinesterase (AChE) in blood and cerebrospinal fluid between patients with type 2 diabetes mellitus and with non-diabetes mellitus, and analyze the correlation of IQ of cognitive impairment patients with diabetes mellitus with AChE activity, latent period and wave amplitude of P300 event-related potential in cerebrospinal fluid. DESIGN: Correlation analysis of contrast observation. SETTING: Department of Endocrinology, Affiliated Hospital of Binzhou Medical College. PARTICIPANTS: Totally 32 patients with type 2 diabetes mellitus who received the treatment in the Department of Endocrinology, Affiliated Hospital of Binzhou Medical College between April 2004 and April 2005 were recruited, serving as diabetes mellitus group. They, including 19 male and 13 female, aged 49 to 73 years, with disease course of 4 to 11 years, all met the diagnostic criteria of diabetes mellitus revised by World Health Organization in 1999. Another 30 patients with non-diabetes mellitus who homeochronously underwent lumbar anesthesia in the Department of Surgery and Department of Gynecology were recruited, serving as non-diabetes mellitus group. The 30 patients included 18 male and 12 female, and their age ranged from 46 to 71 years. Informed consents of detected items were obtained from the involved patients. METHODS: ① Evaluation on IQ: The IQ of involved subjects was evaluated with Chinese Version of the Wechsler Adult Intelligence Scale revised by Gong Yao-xian (WAIS-RC). WAIS-RC included 6 verbal subscales and 5 performance subscales. The test scores of the 11 subscales integrated into the scores of the whole scale, and the scores on the WAIS-RC included verbal IQ (VIQ), performance IQ (PIQ) and full scale IQ (FIQ). FIQ ≤79 scores indicated low IQ and FIQ≤69 indicated intelligence impairment. ② Detection of P300 wave: P300 wave was detected with evoked potential instrument (MYTOPRO, Italian), and data of latent period and amplitude of P300 event-related potential were automatically shown by computer. ③ Detection of AChE activity in blood and cerebrospinal fluid: Activity of AChE of blood and cerebrospinal fluid was measured with biochemical methods by using CORNING-560 autoanalyzer. ④Correlation analysis: Correlation of FIQ with AChE of cerebrospinal fluid and P300 wave of patients with type 2 diabetes mellitus was analyzed. t test was used in intergroup comparison and linear correlation analysis for relevant treatment. MAIN OUTCOME MEASURES: ① Comparison of IQ, latent period and wave amplitude of P300 wave as well as the activity of AChE between two groups. ② Analysis on the correlation of FIQ of patients with type 2 diabetes mellitus with AChE of cerebrospinal fluid and P300 wave. RESULTS: Thirty-two patients with diabetes mellitus and 30 non-diabetes mellitus participated in the result analysis. ① Comparison of IQ, latent period and wave amplitude of P300 wave as well as the activity of AChE between two groups: The scores of VIP, PIQ and FIQ of patients with type 2 diabetes mellitus were (97.4±10.4),(92.6±8.4) and (95.2±9.7) scores, respectively; and those of patients with non-diabetes mellitus were (104.7±9.6),(102.5±8.5)and(102.7±8.9) scores, respectively, and P < 0.05-0.01 was set in intergroup comparison. The latent period of P300 wave at points Fz , Cz and Pz of patients with type 2 diabetes mellitus was (370.8±41.8),(371.5±39.1)and (375.1±43.1) ms, respectively, and that of patients with non-diabetes mellitus was (332.1±28.3),(335.7±29.4)and(339.7±27.3) ms, respectively, and P < 0.01 was set in intergroup comparison; Wave amplitude of P300 of patients with type 2 diabetes mellitus was (8.6±4.1),(8.6±4.0) and(7.7±4.0) μV, respectively and that of patients with non-diabetes mellitus was (11.9±4.1),(11.5±4.4) and (10.9±5.0) μV, respectively , and P < 0.05-0.01 was set in intergroup comparison; The level of AChE in blood and cerebrospinal fluid of patients with type 2 diabetes mellitus was (235.61±50.34)and (17.89±4.46) μkat/L, respectively, which was significantly higher than that of patients with non-diabetes mellitus [(205.03±44.15)and (14.63±0.48) μkat /L, respectively], and P < 0.05-0.01 was set in the intergroup comparison. ② Correlation of FIQ value of patients with type 2 diabetes mellitus with AChE of cerebrospinal fluid and P300 wave: The value of FIQ was significantly negatively correlated with the AChE activity of cerebrospinal fluid (r=-0.588 1, P < 0.01), significantly negatively correlated with the latent period at points Fz, C and Pz of P300 wave (r= -0.700 5, -0.689 4, -0.688 5, P < 0.01), and significantly positively correlated with the amplitude at points Fz , Cz and Pz of P300 wave(r= 0.607 4,0.616 1,0.592 0,P < 0.01). CONCLUSION: ① Cognitive impairment of patients with type 2 diabetes mellitus might be related to the increase of activity of AChE in cerebrospinal fluid. ② Combined application of examination of P300 wave and evaluation of IQ is more useful in deciding the state of cognitive function of patients with type 2 diabetes mellitus.

A Universal Activation Function for Deep Learning

Recently,deep learning has achieved remarkable results in fields that require human cognitive ability,learning ability,and reasoning ability.Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity.Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process.However,it takes a lot of time and effort for researchers to use the existing activation function in their research.Therefore,in this paper,we propose a universal activation function(UA)so that researchers can easily create and apply various activation functions and improve the performance of neural networks.UA can generate new types of activation functions as well as functions like traditional activation functions by properly adjusting three hyperparameters.The famous Convolutional Neural Network(CNN)and benchmark datasetwere used to evaluate the experimental performance of the UA proposed in this study.We compared the performance of the artificial neural network to which the traditional activation function is applied and the artificial neural network to which theUA is applied.In addition,we evaluated the performance of the new activation function generated by adjusting the hyperparameters of theUA.The experimental performance evaluation results showed that the classification performance of CNNs improved by up to 5%through the UA,although most of them showed similar performance to the traditional activation function.

Activation Functions Effect on Fractal Coding Using Neural Networks

Activation functions play an essential role in converting the output of the artificial neural network into nonlinear results,since without this nonlinearity,the results of the network will be less accurate.Nonlinearity is the mission of all nonlinear functions,except for polynomials.The activation function must be dif-ferentiable for backpropagation learning.This study’s objective is to determine the best activation functions for the approximation of each fractal image.Different results have been attained using Matlab and Visual Basic programs,which indi-cate that the bounded function is more helpful than other functions.The non-lin-earity of the activation function is important when using neural networks for coding fractal images because the coefficients of the Iterated Function System are different according to the different types of fractals.The most commonly cho-sen activation function is the sigmoidal function,which produces a positive value.Other functions,such as tansh or arctan,whose values can be positive or negative depending on the network input,tend to train neural networks faster.The coding speed of the fractal image is different depending on the appropriate activation function chosen for each fractal shape.In this paper,we have provided the appro-priate activation functions for each type of system of iterated functions that help the network to identify the transactions of the system.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Neural Networks on an FPGA and Hardware-Friendly Activation Functions

This paper describes our implementation of several neural networks built on a field programmable gate array (FPGA) and used to recognize a handwritten digit dataset—the Modified National Institute of Standards and Technology (MNIST) database. We also propose a novel hardware-friendly activation function called the dynamic Rectifid Linear Unit (ReLU)—D-ReLU function that achieves higher performance than traditional activation functions at no cost to accuracy. We built a 2-layer online training multilayer perceptron (MLP) neural network on an FPGA with varying data width. Reducing the data width from 8 to 4 bits only reduces prediction accuracy by 11%, but the FPGA area decreases by 41%. Compared to networks that use the sigmoid functions, our proposed D-ReLU function uses 24% - 41% less area with no loss to prediction accuracy. Further reducing the data width of the 3-layer networks from 8 to 4 bits, the prediction accuracies only decrease by 3% - 5%, with area being reduced by 9% - 28%. Moreover, FPGA solutions have 29 times faster execution time, even despite running at a 60× lower clock rate. Thus, FPGA implementations of neural networks offer a high-performance, low power alternative to traditional software methods, and our novel D-ReLU activation function offers additional improvements to performance and power saving.

Botulinum toxin type A plus rehabilitative training for improving the motor function of the upper limbs and activities of daily life in patients with stroke and brain injury

BACKGROUND: Botulinum toxin type A (BTX-A) is mostly to be used to treat various diseases of motor disorders, whereas its effect on muscle spasm after stroke and brain injury needs further observation. OBJECTIVE: To observe the effect of BTX-A plus rehabilitative training on treating muscle spasm after stroke and brain injury. DESIGN: A randomized controlled observation. SETTINGS: Department of Rehabilitation, Department of Neurology and Department of Neurosurgery, the Second Hospital of Hebei Medical University. PARTICIPANTS: Sixty inpatients with brain injury and stroke were selected from the Department of Rehabilitation, Department of Neurology and Department of Neurosurgery, the Second Hospital of Hebei Medical University from January 2001 to August 2006. They were all confirmed by CT and MRI, and had obvious increase of spastic muscle strength in upper limbs, their Ashworth grades were grade 2 or above. The patients were randomly divided into treatment group (n =30) and control group (n =30). METHODS: ① Patients in the treatment group undertook comprehensive rehabilitative trainings, and they were administrated with domestic BTX-A, which was provided by Lanzhou Institute of Biological Products, Ministry of Health (S10970037), and the muscles of flexion spasm were selected for upper limbs, 20-25 IU for each site. ② Patients in the treatment group were assessed before injection and at 1 and 2 weeks, 1 and 3 months after injection respectively, and those in the control group were assessed at corresponding time points. The recovery of muscle spasm was assessed by modified Ashworth scale (MAS, grade 0-Ⅳ; Grade 0 for without increase of muscle strength; Grade Ⅳ for rigidity at passive flexion and extension); The recovery of motor function of the upper limbs was evaluated with Fugl-Meyer Assessment (FMA, total score was 226 points, including 100 for exercise, 14 for balance, 24 for sense, 44 for joint motion, 44 for pain and 66 for upper limb); The ADL were evaluated with Barthel index, the total score was 100 points, 60 for mild dysfunction, 60-41 for moderate dysfunction, < 40 for severe dysfunction). MAIN OUTCOME MEASURES: Changes of MAS grade, FMA scores and Barthel index before and after BTX-A injection. RESULTS: All the 60 patients with brain injury and stroke were involved in the analysis of results. ① FMA scores of upper limbs: The FMA score in the treatment group at 2 weeks after treatment was higher than that before treatment [(14.98±10.14), (13.10±9.28) points, P < 0.05], whereas there was no significant difference at corresponding time point in the control group. The FMA scores at 1 and 3 months in the treatment group [(23.36±10.69), (35.36±11.36) points] were higher than those in the control group [(20.55±10.22), (30.33±10.96) points, P < 0.01]. ② MAS grades of upper limbs: There were obviously fewer cases of grade Ⅲ in MAS at 2 weeks after treatment than before treatment in the treatment group (0, 9 cases, P < 0.05), whereas there was no obvious difference in the control group. There were obviously fewer cases of grade Ⅲ in MAS at 2 weeks and 1 month after treatment in the treatment group (0, 0 case) than the control group (5, 2 cases, P < 0.01). ③ Barthel index of upper limbs: The Barthel index at 2 weeks after treatment was higher than that before treatment in the treatment group [(30.36±22.25), (28.22±26.21) points, P < 0.05], whereas there was no significant difference in the control group. The Barthel indexes at 1 and 3 months after treatment in the treatment group were obviously higher than those in the control group [(20.55±10.22), (30.33±10.96) points, P < 0.01]. CONCLUSION: BTX-A has obvious efficacy on decreasing muscle tension after stroke and brain injury, and relieving muscle spasm; Meanwhile, the combination with rehabilitative training can effectively ameliorate the motor function of upper limbs and ADL of the patients.

To Actively Perform the Judicial Administration Function and Promote the Development of Cause of Human Rights

Respect for human rights and protection of human rights are significant rules in the Constitution of the People’s Republic of China.In 2015,judicial administration departments at all levels legally exercised their duties,implemented the principles and rules in constitution,and kept strengthening propagation of human rights through creation of contents and methods which had acquired great effects.What they have done contributes significantly toward the development of human rights in China.

Impact point prediction guidance of ballistic missile in high maneuver penetration condition

An impact point prediction(IPP) guidance based on supervised learning is proposed to address the problem of precise guidance for the ballistic missile in high maneuver penetration condition.An accurate ballistic trajectory model is applied to generate training samples,and ablation experiments are conducted to determine the mapping relationship between the flight state and the impact point.At the same time,the impact point coordinates are decoupled to improve the prediction accuracy,and the sigmoid activation function is improved to ameliorate the prediction efficiency.Therefore,an IPP neural network model,which solves the contradiction between the accuracy and the speed of the IPP,is established.In view of the performance deviation of the divert control system,the mapping relationship between the guidance parameters and the impact deviation is analysed based on the variational principle.In addition,a fast iterative model of guidance parameters is designed for reference to the Newton iteration method,which solves the nonlinear strong coupling problem of the guidance parameter solution.Monte Carlo simulation results show that the prediction accuracy of the impact point is high,with a 3 σ prediction error of 4.5 m,and the guidance method is robust,with a 3 σ error of 7.5 m.On the STM32F407 singlechip microcomputer,a single IPP takes about 2.374 ms,and a single guidance solution takes about9.936 ms,which has a good real-time performance and a certain engineering application value.

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

Machine Learning(ML)and Deep Learning(DL)technologies are revolutionizing the medical domain,especially with Electrocardiogram(ECG),by providing new tools and techniques for diagnosing,treating,and preventing diseases.However,DL architectures are computationally more demanding.In recent years,researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches,say for example,combining the convolutional layer blocks of Convolution Neural Networks(CNNs)into ML algorithms such as Extreme Gradient Boosting(XGBoost)and K-Nearest Neighbor(KNN)resulting in CNN-XGBoost and CNN-KNN,respectively.However,these approaches are homogenous in the sense that they use a fixed Activation Function(AFs)in the sequence of convolution and pooling layers,thereby limiting the ability to capture unique features.Since various AFs are readily available and each could capture unique features,we propose a Convolutionbased Heterogeneous Activation Facility(CHAF)which uses multiple AFs in the convolution layer blocks,one for each block,with a motivation of extracting features in a better manner to improve the accuracy.The proposed CHAF approach is validated on PTB and shown to outperform the homogeneous approaches such as CNN-KNN and CNN-XGBoost.For PTB dataset,proposed CHAF-KNN has an accuracy of 99.55%and an F1 score of 99.68%in just 0.008 s,outperforming the state-of-the-art CNN-XGBoost which has an accuracy of 99.38%and an F1 score of 99.32%in 1.23 s.To validate the generality of the proposed CHAF,experiments were repeated on MIT-BIH dataset,and the proposed CHAF-KNN is shown to outperform CNN-KNN and CNN-XGBoost.

Machine Learning Controller for DFIG Based Wind Conversion System

Renewable energy production plays a major role in satisfying electricity demand.Wind power conversion is one of the most popular renewable energy sources compared to other sources.Wind energy conversion has two major types of generators such as the Permanent Magnet Synchronous Generator(PMSG)and the Doubly Fed Induction Generator(DFIG).The maximum power tracking algo-rithm is a crucial controller,a wind energy conversion system for generating maximum power in different wind speed conditions.In this article,the DFIG wind energy conversion system was developed in Matrix Laboratory(MATLAB)and designed a machine learning(ML)algorithm for the rotor and grid side converter.The ML algorithm has been developed and trained in a MATLAB environment.There are two types of learning algorithms such as supervised and unsupervised learning.In this research supervised learning is used to power the neural networks and analysis is made for various hidden layers and activation functions.Simulation results are assessed to demonstrate the efficiency of the proposed system.

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag

The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.

A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model

In this study, the author will investigate and utilize advanced machine learning models related to two different methodologies to determine the best and most effective way to predict individuals with heart failure and cardiovascular diseases. The first methodology involves a list of classification machine learning algorithms, and the second methodology involves the use of a deep learning algorithm known as MLP or Multilayer Perceptrons. Globally, hospitals are dealing with cases related to cardiovascular diseases and heart failure as they are major causes of death, not only for overweight individuals but also for those who do not adopt a healthy diet and lifestyle. Often, heart failures and cardiovascular diseases can be caused by many factors, including cardiomyopathy, high blood pressure, coronary heart disease, and heart inflammation [1]. Other factors, such as irregular shocks or stress, can also contribute to heart failure or a heart attack. While these events cannot be predicted, continuous data from patients’ health can help doctors predict heart failure. Therefore, this data-driven research utilizes advanced machine learning and deep learning techniques to better analyze and manipulate the data, providing doctors with informative decision-making tools regarding a person’s likelihood of experiencing heart failure. In this paper, the author employed advanced data preprocessing and cleaning techniques. Additionally, the dataset underwent testing using two different methodologies to determine the most effective machine-learning technique for producing optimal predictions. The first methodology involved employing a list of supervised classification machine learning algorithms, including Naïve Bayes (NB), KNN, logistic regression, and the SVM algorithm. The second methodology utilized a deep learning (DL) algorithm known as Multilayer Perceptrons (MLPs). This algorithm provided the author with the flexibility to experiment with different layer sizes and activation functions, such as ReLU, logistic (sigmoid), and Tanh. Both methodologies produced optimal models with high-level accuracy rates. The first methodology involves a list of supervised machine learning algorithms, including KNN, SVM, Adaboost, Logistic Regression, Naive Bayes, and Decision Tree algorithms. They achieved accuracy rates of 86%, 89%, 89%, 81%, 79%, and 99%, respectively. The author clearly explained that Decision Tree algorithm is not suitable for the dataset at hand due to overfitting issues. Therefore, it was discarded as an optimal model to be used. However, the latter methodology (Neural Network) demonstrated the most stable and optimal accuracy, achieving over 87% accuracy while adapting well to real-life situations and requiring low computing power overall. A performance assessment and evaluation were carried out based on a confusion matrix report to demonstrate feasibility and performance. The author concluded that the performance of the model in real-life situations can advance not only the medical field of science but also mathematical concepts. Additionally, the advanced preprocessing approach behind the model can provide value to the Data Science community. The model can be further developed by employing various optimization techniques to handle even larger datasets related to heart failures. Furthermore, different neural network algorithms can be tested to explore alternative approaches and yield different results.

Research Progress in the Extraction and Application of Flavonoids from Ginkgo biloba Leaves

Ginkgo biloba resources in China are enormous. With the demand of the market, the preparation and application of flavonoids have become a current research hotspot. The main active substances in G. biloba leaves, flavonoids, have various functional activities and are widely used in fields such as food, medicine, cosmetics, feed, etc. In this paper, the introduction, functional activity, extraction methods, and application research of flavonoids from G. biloba leaves are reviewed, and the development prospects of flavonoids from G. biloba leaves are expected.

Study on Marine actinomycetes and analysis of their secondary metabolites

Actinomycetes are relatively prevalent bacteria in the ocean,constituting 9% of the total number of marine bacteria.The advancement of science and technology has led to a more profound exploration of marine actinomycetes.These studies hold immense significance in comprehending the distribution and adaptation of marine actinomycetes within the oceanic environment,as well as uncovering new secondary metabolites.Based on differing lifestyles,marine actinomycetes can be categorized as free-living or co-epiphytic.The activity and metabolism of actinomycetes vary across diverse marine settings,including the deep sea,benthic regions,and marine organisms.Due to their distinctive biological traits and genetic background,these marine actinomycetes inevitably generate metabolites possessing unique structures.Research methodologies concerning marine actinomycetes predominantly encompass traditional pure culture techniques,molecular biology approaches,and the integration of metagenomics and bioinformatics.The exploration of varied methodologies proves pivotal for the analysis of metabolite processes.Through the cultivation of marine actinomycetes,numerous compounds featuring novel structures and significant activities have been isolated,furnishing a substantial foundation for new drug investigations.These encompass,but are not restricted to,peptides,antibiotics,terpenoids,ketones,quinones,macrolides,and pigments.The potential applications of marine actinomyces and their secondary metabolites extend beyond antibacterial and anti-tumor effects,exhibiting promising prospects in antifungal and antiviral domains.This paper provides a comprehensive review of the classification,resources,research methodologies,and habitats of marine actinomycetes.Furthermore,it delves into the classification of secondary metabolites and their functional activities,facilitating a more exhaustive analysis of the secondary metabolites produced by marine actinomycetes.

Chip-Based High-Dimensional Optical Neural Network

Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high parallelization,large bandwidth,and low power consumption to meet the demand of big data.Here,we demonstrate the dual-layer ONN with Mach-Zehnder interferometer(MZI)network and nonlinear layer,while the nonlinear activation function is achieved by optical-electronic signal conversion.Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN.We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution.Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN.This work provides a high-performance architecture for future parallel high-capacity optical analog computing.

Complex-Valued Neural Networks:A Comprehensive Survey

Complex-valued neural networks(CVNNs)have shown their excellent efficiency compared to their real counterparts in speech enhancement,image and signal processing.Researchers throughout the years have made many efforts to improve the learning algorithms and activation functions of CVNNs.Since CVNNs have proven to have better performance in handling the naturally complex-valued data and signals,this area of study will grow and expect the arrival of some effective improvements in the future.Therefore,there exists an obvious reason to provide a comprehensive survey paper that systematically collects and categorizes the advancement of CVNNs.In this paper,we discuss and summarize the recent advances based on their learning algorithms,activation functions,which is the most challenging part of building a CVNN,and applications.Besides,we outline the structure and applications of complex-valued convolutional,residual and recurrent neural networks.Finally,we also present some challenges and future research directions to facilitate the exploration of the ability of CVNNs.

Convolutional neural network for transient grating frequency-resolved optical gating trace retrieval and its algorithm optimization

A convolutional neural network is employed to retrieve the time-domain envelop and phase of few-cycle femtosecond pulses from transient-grating frequency-resolved optical gating(TG-FROG) traces.We use theoretically generated TGFROG traces to complete supervised trainings of the convolutional neural networks,then use similarly generated traces not included in the training dataset to test how well the networks are trained.Accurate retrieval of such traces by the neural network is realized.In our case,we find that networks with exponential linear unit(ELU) activation function perform better than those with leaky rectified linear unit(LRELU) and scaled exponential linear unit(SELU).Finally,the issues that need to be addressed for the retrieval of experimental data by this method are discussed.