Search results for: neural activity

Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Kaouther Selmi, Alaeddine Sridi, Mohamed Bouallegue, Kais Bouallegue

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Neurons are the fundamental units of the brain and the nervous system. The variable structure model of neurons consists of a system of differential equations with various parameters. By optimizing these parameters, we can create a unique model that describes the dynamic behavior of a single neuron. We introduce a neural network based on neurons with multiple dendrites employing an activation function with a variable structure. In this paper, we present a model for heart behavior. Finally, we showcase our successful simulation of the heart's ECG diagram using our Variable Structure Neuron Model (VSMN). This result could provide valuable insights into cardiology.

Keywords: neural networks, neuron, dendrites, heart behavior, ECG

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Authors: Tripti Mishra, Shipra Shukla, Sanjeev Meena, , Ruchi Singh, Mahesh Pal, D. K. Upreti, Dipak Datta

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Roccella montagnei belongs to lichen family Roccelleceae growing luxuriantly along the coastal regions of India. As Roccella has been shown to be bioactive, we prepared methanolic extract and assessed its anticancer potential. The methanolic extract showed significant in vitro cytotoxic activity against four human cancer cell lines such as Colon (DLD-1, SW-620), Breast (MCF-7), Head and Neck (FaDu). This prompted us to isolate bioactive compounds through column chromatography. Two compounds Roccellic acid and Everninic acid have been isolated, out of which Everninic acid is reported for the first time. Both the compounds have been tested for in vitro cytotoxic activity in which Roccellic acid showed strong anticancer activity as compared to the Everninic acid. CDK-10 (Cyclin-dependent kinase) contributes to proliferation of cancer cells, and aberrant activity of these kinases has been reported in a wide variety of human cancers. These kinases, therefore, constitute biomarkers of proliferation and attractive pharmacological targets for the development of anticancer therapeutics. Therefore both the isolated compounds were tested for in silico molecular docking study against CDK-10 isomer enzyme to support the cytotoxic activity.

Keywords: cytotoxic activity, everninic acid, roccellic acid, R. montagnei

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Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer

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New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.

Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics

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Authors: Kais Rtibi, Slimen Selmi, Jamel El-Benna, Lamjed Marzouki, Hichem Sebai

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Background: Myeloperoxidase (MPO) is a hemic enzyme found in high concentrations in the primary neutrophils granules. In addition to its peroxidase activity, it has a chlorination activity, using hydrogen peroxide and chloride ions to form hypochlorous acid, a strong oxidant, capable of chlorinating molecules. Bioactive compounds contained in medicinal plants could limit the action of this enzyme to reduce the reactive oxygen species production and its chlorination activity. The purpose of this study is to evaluate the effect of the carob aqueous extract (CAE) on the release of MPO by human neutrophils in vitro and its activity following stimulation of these cells by PMA. Methods: Neutrophils were isolated by simple sedimentation using the Dextran/Ficoll method. After stimulation with phorbol 12-myristate 13-acetate (PMA), neutrophils release the MPO by degranulation. The effect of CAE on the release of MPO was analyzed by the Western blot technique, while, its activity was determined by biochemical method using the method of 3,3', 5,5'- Tetramethylbenzidine (TMB) and hydrogen peroxide. The data were expressed as mean ± SEM. Results: The carob aqueous extract causes a decrease in MPO quantity and activity in a concentration-dependent manner which leads to a reduction of the production of the ROS (reactive oxygen species) and the protection of the molecules against oxidation and chlorination mechanisms. Conclusion: Thanks to its richness in bioactive compounds, the aqueous extract of carob could limit the development of damages related to the uncontrolled activity of MPO.

Keywords: carob, MPO, myeloperoxidase, neutrophils, PMA, phorbol 12-myristate 13-acetate

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Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

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Authors: Muhammad Atif, Cang Yan

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The enhancement of low-light images is a significant area of study aimed at enhancing the quality of captured images in challenging lighting environments. Recently, methods based on convolutional neural networks (CNN) have gained prominence as they offer state-of-the-art performance. However, many approaches based on CNN rely on increasing the size and complexity of the neural network. In this study, we propose an alternative method for improving low-light images using an autoencoder-based multiscale knowledge transfer model. Our method leverages the power of three autoencoders, where the encoders of the first two autoencoders are directly connected to the decoder of the third autoencoder. Additionally, the decoder of the first two autoencoders is connected to the encoder of the third autoencoder. This architecture enables effective knowledge transfer, allowing the third autoencoder to learn and benefit from the enhanced knowledge extracted by the first two autoencoders. We further integrate the proposed model into the PIX to PIX GAN framework. By integrating our proposed model as the generator in the GAN framework, we aim to produce enhanced images that not only exhibit improved visual quality but also possess a more authentic and realistic appearance. These experimental results, both qualitative and quantitative, show that our method is better than the state-of-the-art methodologies.

Keywords: low light image enhancement, deep learning, convolutional neural network, image processing

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Authors: Nikita Pasricha, Karen Smith, Simone Marshall, Vincent DePaul, Jessica Trier

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Understanding that physical activity in rehabilitation programs shapes outcomes in acquired brain injury (ABI) populations is not a new concept. However, there is a void in understanding the physical activity patterns of inpatients in ABI rehabilitation, the trajectory of physical activity recovery, and factors that contribute to the recovery of physical activity over the initial months post-ABI. The purpose of this study was to determine if physical activity patterns vary in people with recent ABI in inpatient rehabilitation. The study also investigated differences in physical activity patterns in ABI patients compared to age-related healthy participants. Results revealed that ABI patients spent approximately 6.7 times longer per day in sedentary postures than in active positions. In comparison, the control group spent only 2.8 times longer in sedentary postures compared to active positions. Patients with ABI took significantly fewer steps than age-matched health control participants. Within the ABI population, patients took 0.78 times fewer steps on weekends compared to weekdays. Participants with greater mobility limitations had a greater difference in WD to WE steps taken. Potential reasons could be from no structured weekend rehabilitation programs, lower availability of staff, or varying schedules. Given that the rehabilitation program is only structured on weekdays, further research to investigate the benefits of structured physical activities like group walking programs on weekends for ABI patients in inpatient rehabilitation programs is warranted.

Keywords: brain, ABI, TBI, rehabilitation

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Authors: Mohammad Kazem Mohammadi

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The use of anticancer agents forms an important part of the treatment of cancer of various types. Uranyl Complexes with DPHMP ligand have been used for the prevention and treatment of cancers. U(IV) metal complexes prepared by reaction of uranyl salt UO2 (NO3)2.6H2O with DPHMP in dry acetonitrile. Characterization of the ligand and its complexes was made by microanalyses, FT-IR, 1H NMR, 13C NMR and UV–Visible spectroscopy. These new complex showed excellent antitumor activity against two kinds of cancer cells that that are HT29:Haman colon adenocarcinoma cell line and T47D:human breast adenocarcinoma cell line.

Keywords: uranyl complexes, DPHMP ligand, antitumor activity, HT29, T47D

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Authors: Mokhtar Kouki Inès Rekik

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This paper focuses on the assessment of the air pollution and morbidity relationship in Tunisia. Air pollution is measured by ozone air concentration and the morbidity is measured by the number of respiratory-related restricted activity days during the 2-week period prior to the interview. Socioeconomic data are also collected in order to adjust for any confounding covariates. Our sample is composed by 407 Tunisian respondents; 44.7% are women, the average age is 35.2, near 69% are living in a house built after the 1980, and 27.8% have reported at least one day of respiratory-related restricted activity. The model consists on the regression of the number of respiratory-related restricted activity days on the air quality measure and the socioeconomic covariates. In order to correct for zero-inflation and heterogeneity, we estimate several models (Poisson, Negative binomial, Zero inflated Poisson, Poisson hurdle, Negative binomial hurdle and finite mixture Poisson models). Bootstrapping and post-stratification techniques are used in order to correct for any sample bias. According to the Akaike information criteria, the hurdle negative binomial model has the greatest goodness of fit. The main result indicates that, after adjusting for socioeconomic data, the ozone concentration increases the probability of positive number of restricted activity days.

Keywords: bootstrapping, hurdle negbin model, overdispersion, ozone concentration, respiratory-related restricted activity days

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Authors: Kartini Ilias, Nabilah Md Ahir, Nor Zafirah Ab Rahman, Safiah Md Yusof, Nuri Naqieyah Radzuan, Siti Sabariah Buhari

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This study aim to develop and assess the effectiveness of an intervention in improving eating habits and physical activity level of university students of UiTM Puncak Alam. The intervention consists of weekly dietary counselling by registered dietitian and high-intensity interval training (HIIT) for three times per week for the duration of 8 weeks. A total of 25 students from the intervention group and 25 students from control group who had BMI equal to or greater than 25kg/m² participated in the study. The results showed a significant reduction in body weight (3.0 kg), body fat percentage (7.9 %), waist circumference (7.3 cm) and BMI (2.9 kg/m²) between pre and post intervention. Besides, there was a significant increase in the level of physical activity among subjects in intervention group. In conclusion, the intervention made an impact on eating habit, physical activity level and improves weight status of the students. It is expected that the intervention could be adopted and implemented by the government and private sector as well as policy-makers in formulating obesity intervention.

Keywords: obesity, diet, obesity intervention, physical activity

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Authors: Yik Ling Chew, Adlina Maisarah Mahadi, Joo Kheng Goh

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Bauhinia kockiana originates from Peninsular Malaysia, and it is grown as a garden ornamental plant. However, it is used as medicinal plant by Malaysia ‘Kelabit’ ethic group in treating various diseases and illnesses. This study focused on the assessment of the antibacterial activity of B. kockiana towards MRSA, to purify and identify the antibacterial compounds, and to determine the mechanism of antibacterial activity. Antibacterial activity of B. kockiana flower is evaluated qualitatively and quantitatively using disc diffusion assay and microbroth dilution method to determine the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of extracts. Phytochemical analysis is performed to determine the classes of phytochemicals in the extracts. Bioactivity-guided isolation is performed to purify the antibacterial agents and identified the chemical structures via various spectroscopy methods. Scanning electron microscopy (SEM) technique is adopted to evaluate the antibacterial mechanism of extract and compounds isolated. B. kockiana flower is found to exhibit fairly strong antibacterial activity towards both strains of MRSA bacteria. Gallic acid and its ester derivatives are purified from ethyl acetate extract and the antibacterial activity is evaluated. SEM has revealed the mechanism of the extracts and compounds isolated.

Keywords: alkyl gallates, Bauhinia kockiana, MRSA, scanning electron microscopy

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Authors: Alexander Vandesompele, Joni Dambre

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The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.

Keywords: dendritic computation, spiking neural networks, point neuron model

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: Vivek Singh Bhadauria, Abhishek Pandey

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Indole-2,3-diones are known for their various biological activities. By suitable control of a substituent, different novel indole-2,3-diones were synthesized. In this present study, various Schiff and Mannich bases were synthesized and characterized, and evaluated their for different pharmacological activities. The compounds were prepared by reacting indole-2,3-dione with benzyl chloride and 4-substituted thiosemicarbazides. All the synthesized compounds were characterized by the TLC, MP, Elemental analysis, FTIR, 1H-NMR and Mass spectroscopy. The compounds have been evaluated for their anticancer, antituberculosis, anticonvulsant, antiinflammatory as well as anti-SARS activity and the results are presented. Some of compounds possessed different pharmacological activity at a concentration of 200 mg/kg body weight and even at lower concentration.

Keywords: indoles, isatin, NMR, biological activities

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Authors: Mei-Fang Wu, Shu-Li Wang, Feng-Tsung Cheng

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Highly developed technology and highly competitive global market highlight the important role of competitive advantages and operation performances in sustainable company operation. Activity-Based Costing (ABC) provides accurate operation cost and operation performance information. Rich literature provide relevant research with cases study on Activity-Based Costing application, and yet, there is no research studying on cause relationship between key success factors of applying Activity-Based Costing and its specific outcomes, such as profitability or share market. These relationships provide the ways to handle the key success factors to achieve the specific outcomes for ensuring to promote the competitive advantages and operation performances. The main purposes of this research are exploring the key success paths by Key Success Paths approach which will lead the ways to apply Activity-Base Costing. The Key Success Paths is the innovative method which is exploring the cause relationships and explaining what are the effects of key success factors to specific outcomes of Activity-Based Costing implementation. The cause relationships between key success factors and successful specific outcomes are Key Success Paths (KSPs). KSPs are the guidelines to lead the cost management strategies to achieve the goals of competitive advantages and operation performances. The research findings indicate that good management system design may impact the good outcomes of Activity-Based Costing application and achieve to outstanding competitive advantage, operating performance and profitability as well by KSPs exploration.

Keywords: activity-based costing, key success factors, key success paths approach, key success paths, key failure paths

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Authors: Chia-Hsun Chang

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Background: Combination of drug treatment, dietary management, and regular exercise can effectively control type II diabetes mellitus (T2DM). Performing daily physical activities other than structured exercise is much easier and whether daily physical activities including work, walking, housework, gardening, leisure exercise, or transportation have a similar effect on diabetes control is not well studied.Aims and Objectives: This study aims to determine whether daily physical activity undertaken by patients with T2DM is associated with their diabetes control. Design: A correlation study with prospective design. Methods: Purposive sampling of 206 patients with T2DM was recruited from a medical center in Central Taiwan. The International Physical Activity Questionnaire was used to assess daily levels of physical activities, and the Diabetes Compliance Questionnaire was used to assess medication and dietary compliance. Data of diabetes control (hemoglobin A1c, HbA1c)were followed up every three months for one year after recruitment. Results: In this study, the average age of the participants was 62.5 years (±10.4 years), and the average duration of diabetes since diagnosis was 13.2 years (±7.8), 112 of the participants were women (54.4%) and 94 of the participants were men (45.6%). The mean HbA1c level was 7.8% (±1.4), and 78.2% of the participants presented with unsatisfactory diabetes control. Because the participants were distributed across a wide age range, and their physical health, activity levels, and comorbidities might have varied with age, the participants were divided into two groups: 121 participants who were younger than 65 years (58.7%) and 85 participants who were older than 65 years (41.3%). Both younger (< 65 years) and older (> 65 years) patients with diabetes engaged in more moderate and low levels of physical activity (89.3% and 87%, respectively). Results showed that the levels of daily physical activity were not significantly associated with diabetes control after adjustment for medication and dietary compliance in both groups. Conclusion: Performing daily physical activity is not significantly correlated with diabetes control. Daily physical activity cannot completely replace exercise. Relevance to Clinical Practice: Health personnel must encourage patients to engage in exercise that is planned, structured, and repetitive for improving diabetes control.

Keywords: daily physical activity, diabetes control, international physical activity questionnaire (IPAQ), type II diabetes mellitus (T2DM)

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Authors: Young-Kwang Lee, Eun-Gu Ji, Min-Joo Kim, Seung-Jae Oh

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The purpose of this study is to develop a social participation activity scale for the elderly. As a result of exploratory factor analysis, confirmatory factor analysis was conducted using maximum likelihood method using bundled items. In conclusion, thirteen items of social participation activity scale seemed appropriate. Finally, convergent validity and discriminant validity were verified on the scale with the fit. The convergent validity was based on the variance extracted value. In other words, the hypothesis that the variables are the same is rejected and the validity is confirmed. This study extensively considered the measurement items of the social participation activity scale used to measure social participation activities of the elderly. In the future, it will be meaningful that it can be used as a tool to verify the effectiveness of services in organizations that provide social welfare services to elderly people such as comprehensive social welfare centers and the elderly comprehensive social welfare centers.

Keywords: elderly, social participation, scale development, validity

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Authors: Sara Mosteller, Larissa K. Samuelson, Sobanawartiny Wijeakumar, John P. Spencer

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This study was designed to examine how humans are able to teach and learn semantic information as well as cooperate in order to jointly achieve sophisticated goals. Specifically, we are measuring individual differences in how these abilities develop from foundational building blocks in early childhood. The current study adopts a paradigm for novel noun learning developed by Samuelson, Smith, Perry, and Spencer (2011) to a hyperscanning paradigm [Cui, Bryant and Reiss, 2012]. This project measures coordinated brain activity between a parent and child using simultaneous functional near infrared spectroscopy (fNIRS) in pairs of 2.5, 3.5 and 4.5-year-old children and their parents. We are also separately testing pairs of adult friends. Children and parents, or adult friends, are seated across from one another at a table. The parent (in the developmental study) then teaches their child the names of novel toys. An experimenter then tests the child by presenting the objects in pairs and asking the child to retrieve one object by name. Children are asked to choose from both pairs of familiar objects and pairs of novel objects. In order to explore individual differences in cooperation with the same participants, each dyad plays a cooperative game of Jenga, in which their joint score is based on how many blocks they can remove from the tower as a team. A preliminary analysis of the noun-learning task showed that, when presented with 6 word-object mappings, children learned an average of 3 new words (50%) and that the number of objects learned by each child ranged from 2-4. Adults initially learned all of the new words but were variable in their later retention of the mappings, which ranged from 50-100%. We are currently examining differences in cooperative behavior during the Jenga playing game, including time spent discussing each move before it is made. Ongoing analyses are examining the social dynamics that might underlie the differences between words that were successfully learned and unlearned words for each dyad, as well as the developmental differences observed in the study. Additionally, the Jenga game is being used to better understand individual and developmental differences in social coordination during a cooperative task. At a behavioral level, the analysis maps periods of joint visual attention between participants during the word learning and the Jenga game, using head-mounted eye trackers to assess each participant’s first-person viewpoint during the session. We are also analyzing the coherence in brain activity between participants during novel word-learning and Jenga playing. The first hypothesis is that visual joint attention during the session will be positively correlated with both the number of words learned and with the number of blocks moved during Jenga before the tower falls. The next hypothesis is that successful communication of new words and success in the game will each be positively correlated with synchronized brain activity between the parent and child/the adult friends in cortical regions underlying social cognition, semantic processing, and visual processing. This study probes both the neural and behavioral mechanisms of learning and cooperation in a naturalistic, interactive and developmental context.

Keywords: communication, cooperation, development, interaction, neuroscience

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Authors: Harish Rajak, Swati Singh

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A series of novel pyrimidine based semicarbazone were designed and synthesized on the basis of semicarbazone based pharmacophoric model to satisfy the structural prerequisite crucial for antiepileptic activity. The semicarbazones based pharmacophoric model consists of following four essential binding sites: (i) An aryl hydrophobic binding site with halo substituent; (ii) A hydrogen bonding domain; (iii) An electron donor group and (iv) Another hydrophobic-hydrophilic site controlling the pharmacokinetic features of the anticonvulsant. The aryl semicarbazones has been recognized as a structurally novel class of compounds with remarkable anticonvulsant activity. In the present study, all the test semicarbazones were subjected to molecular docking using Glide v5.8. Some of the compounds were found to interact with ARG192, GLU270 and THR353 residues of 1OHV protein, present in GABA-AT receptor. The chemical structures of the synthesized molecules were characterized by elemental and spectral (IR, 1H NMR, 13C NMR and MS) analysis. The anticonvulsant activities of the compounds were investigated using maximal electroshock seizure (MES) and subcutaneous pentylenetrtrazole (scPTZ) models. The neurotoxicity was evaluated in mice by the rotorod test. The attempts were also made to establish structure-activity relationships among synthesized compounds. The results of the present study confirmed that the pharmacophore model with four binding sites is essential for antiepileptic activity.

Keywords: pyrimidine, semicarbazones, anticonvulsant activity, neurotoxicity

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Authors: A. S. Al-Fatesh, A. A. Ibrahim, A. M. AlSharekh, F. S. Alqahtani, S. O. Kasim, A. H. Fakeeha

Abstract:

Hydrogen is the expected future fuel since it produces energy without any pollution. It can be used as a fuel directly or through the fuel cell. It is also used in chemical and petrochemical industry as reducing agent or in hydrogenation processes. It is produced by different methods such as reforming of hydrocarbon, electrolytic method and methane decomposition. The objective of the present paper is to study the decomposition of methane reaction at 700°C and 800°C. The catalysts were prepared via impregnation method using 20%Fe and different proportions of combined alumina and silica support using the following ratios [100%, 90%, 80%, and 0% Al₂O₃/SiO₂]. The prepared catalysts were calcined and activated at 600 OC and 500 OC respectively. The reaction was carried out in fixed bed reactor at atmospheric pressure using 0.3g of catalyst and feed gas ratio of 1.5/1 CH₄/N₂ with a total flow rate 25 mL/min. Catalyst characterizations (TPR, TGA, BET, XRD, etc.) have been employed to study the behavior of catalysts before and after the reaction. Moreover, a brief description of the weight loss and the CH₄ conversions versus time on stream relating the different support ratios over 20%Fe/Al₂O₃/SiO₂ catalysts has been added as well. The results of TGA analysis provided higher weights losses for catalysts operated at 700°C than 800°C. For the 90% Al₂O₃/SiO₂, the activity decreases with the time on stream using 800°C reaction temperature from 73.9% initial CH₄ conversion to 46.3% for a period of 300min, whereas the activity for the same catalyst increases from 47.1% to 64.8% when 700°C reaction temperature is employed. Likewise, for 80% Al₂O₃/SiO₂ the trend of activity is similar to that of 90% Al₂O₃/SiO₂ but with a different rate of activity variation. It can be inferred from the activity results that the ratio of Al₂O₃ to SiO₂ is crucial and it is directly proportional with the activity. Whenever the Al/Si ratio decreases the activity declines. Indeed, the CH₄ conversion of 100% SiO₂ support was less than 5%.

Keywords: Al₂O₃, SiO₂, CH₄ decomposition, hydrogen, iron

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Authors: Hanafizan Hussain

Abstract:

This study shows that several projects will be conducted at the workshop in which using the conceptual model for knowledge sharing approach to create an idea for mobile application. The sharing idea has been done through the collaborative activity in which a group of different field sought to define the mobile application which will lead to new media approach of using social media platform. The collaborative activity will be provided and implemented in the form of one day workshop to determine the approach towards the theme given. The activity later will be continued for four weeks for the participant to prepare for the pitch day workshop. This paper shows the pitch of idea including the interface and prototype for the said products. The collaboration between the members with different field of study shows that social media influenced the knowledge sharing model and its creation or innovations. One of the projects supported a collaborative activity in which a group of young designers sought to define the knowledge sharing model of their ability in creating idea for mobile applications.

Keywords: mobile application, collaborative activity, conceptual knowledge sharing model, social media platform

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Authors: Vidhu Mitha Goutham, Ruma Mukherjee

Abstract:

The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

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Authors: D. J. Madhu Kumar, D. Jagadeesh Prasad, Sana Sheik, E. P. Rejeesh

Abstract:

A series of Mannich bases derived from 1,2,4-triazole(3a-k and 4a-k) are synthesized by treating a Schiff base with various substituted primary/secondary amines and formaldehyde. The Schiff base is prepared by treating 3-methyl-4-amino-5-mercapto-1,2,4-triazole with 3,4-dimethoxybenzaldehyde in the presence of acid catalyst. The triazole is prepared by treating acetic acid with thiocarbohydrazide at reflux temperature. All the synthesized samples are characterised by FT-IR, 1H-NMR, and LC-MASS spectral studies and screened for their anti-oxidant activity.

Keywords: mannich bases, anti-oxidant activity, schiff base, triazole

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Authors: I. Savitskaya, A. Kistaubayeva, A. Zhubanova, I. Blavachinskaiya, D. Ibrayeva, M. Abdulzhanova, A. Otarbay, A.Isabekova

Abstract:

This study was conducted for the investigation of number of cellulolytic bacteria and their ability in decomposition. Seven samples surface soil were collected on cellulose Zailiskii Alatau slopes. Cellulolitic activity of new strains of Bacillus, isolated from soil is determined. Isolated cellulose degrading bacteria were screened for determination of the highest cellulose activity by quantitative assay using Congo red, gravimetric assay and colorimetric DNS method trough of the determination of the parameters of sugar reduction. Strains are assigned to: B.subtilis, B.licheniformis, B. cereus and, В. megaterium. Bacillus strains consisting of several different types of cellulases have broad substrate specificity of cellulase complexes formed by them. Cellulolitic bacteria were recorded to have highest cellulase activity and selected for optimization of cellulase enzyme production.

Keywords: cellulose-degrading bacteria, cellulase complex, foothills soil, screening

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Authors: Nathan Labiosa, Aryan Kohli

Abstract:

This study introduces a hybrid machine learning model for classifying proteins, developed to address the complexities of protein sequence and structural analysis. Utilizing an architecture that combines a lightweight transformer with a concurrent neural network, the hybrid model leverages both sequential and intrinsic physical properties of proteins. Trained on a comprehensive dataset from the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the model demonstrates a classification accuracy of 95%, outperforming existing methods by at least 15%. The high accuracy achieved demonstrates the potential of this approach to innovate protein classification, facilitating advancements in drug discovery and the development of personalized medicine. By enabling precise protein function prediction, the hybrid model allows for specialized strategies in therapeutic targeting and the exploration of protein dynamics in biological systems. Future work will focus on enhancing the model’s generalizability across diverse datasets and exploring the integration of more machine learning techniques to refine predictive capabilities further. The implications of this research offer potential breakthroughs in biomedical research and the broader field of protein engineering.

Keywords: amino acids, deep learning, enzymes, neural networks, protein classification, proteins, transformers

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Authors: Youssef. K. A. Abdalhafid, Ezaldin A. M. Mohammed, Masoud M. M. Zatout

Abstract:

This study described the changes in the liver of Uromastyx acanthinura (Bell, 1825) males and females during hibernation and activity seasons. The results revealed that, hibernation causes increase fatty liver and pigment cells with abundant damage, comparing with nearly normal structure and less fatty liver after the hibernation with almost normal pattern. Genomic DNA showed apparent separation during hibernation. Also, caspase 3 and caspase 7 activity reached a high level in the liver tissue during hibernation comparing with activity season.

Keywords: histological liver, DNA fragmentation, hibernation, caspase 3 and caspase 7

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Authors: Furkan Ozkan, M. Selcuk Arslan, Hatice Mercan

Abstract:

Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem.

Keywords: electric vehicles, energy efficiency, green wave control, model predictive control, neural networks

Procedia PDF Downloads 52