Search results for: neural activity

Authors: Lavinia Flueckiger, Roselind Lieb, Andrea H. Meyer, Cornelia Witthauer, Jutta Mata

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Objective: Health behaviors tend to show a high variability over time within the same person. However, most existing research can only assess a snapshot of a person’s behavior and not capture this natural daily variability. Two intensive longitudinal studies examine the variability in health behavior over one academic year and their implications for other aspects of daily life such as affect and academic performance. Can already a single day of increased physical activity, snacking, or improved sleep have beneficial effects? Methods: In two intensive longitudinal studies with up to 65 assessment days over an entire academic year, university students (Study 1: N = 292; Study 2: N = 304) reported sleep quality, physical activity, snacking, positive and negative affect, and learning goal achievement. Results: Multilevel structural equation models showed that on days on which participants reported better sleep quality or more physical activity than usual, they also reported increased positive affect, decreased negative affect, and better learning goal achievement. Higher day-to-day snacking was only associated with increased positive affect. Both, increased day-to-day sleep quality and physical activity were indirectly associated with better learning goal achievement through changes in positive and negative affect; results for snacking were mixed. Importantly, day-to-day sleep quality was a stronger predictor for affect and learning goal achievement than physical activity or snacking. Conclusion: One day of better sleep or more physical activity than usual is associated with improved affect and academic performance. These findings have important implications for low-threshold interventions targeting the improvement of daily functioning.

Keywords: sleep quality, physical activity, snacking, affect, academic performance, multilevel structural equation model

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Authors: Hanjun Zhao, Ke Zhang, Bojian Zheng

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Background: So far, there is no universal antiviral agent which can inhibit multiple viral infections. More and more drug-resistant viral strains emerge after the antiviral drug application for treatment. Defensins are the front line of host innate immunity and have broad spectrum antibacterial and antiviral effects. However, there is limited data to show if these defensins have good antiviral activity in vivo and what the antiviral mechanism is. Subjects: To investigate a peptide with widespread antivirus activity in vitro and in vivo and illustrate the antiviral mechanism. Methods: Antiviral peptide library designed from mouse beta defensins was synthesized by the company. Recombinant beta defensin was obtained from E. coli. Antiviral activity in vitro was assayed by plaque assay, qPCR. Antiviral activity in vivo was detected by animal challenge with 2009 pandemic H1N1 influenza A virus. The antiviral mechanism was assayed by western blot, ELISA, and qPCR. Conclusions: We identify a new peptide which has widespread effects against multiple viruses (H1N1, H5N1, H7N9, MERS-CoV) in vitro and has efficient antivirus activity in vivo. This peptide inhibits viral entry into target cells and subsequently blocks viral replication. The in vivo study of the antiviral peptide against other viral infections and the investigation of its more detail antiviral mechanism are ongoing.

Keywords: antiviral peptide, defensin, Influenza A virus, mechanism

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Authors: J. E. Mendes, L. Abrunhosa, J. A. Teixeira, E. R. de Camargo, C. P. de Souza, J. D. C. Pessoa

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Among the many promising nanomaterials with antifungal properties, metal nanoparticles (silver nanoparticles) stand out due to their high chemical activity. Therefore, the aim of this study was to evaluate the effect of silver nanoparticles (AgNPs) against Phomopsis sp. AgNPs were synthesized by silver nitrate reduction with sodium citrate and stabilized with ammonia. The synthesized AgNPs have further been characterized by UV/Visible spectroscopy, Biophysical techniques like Dynamic light scattering (DLS) and Scanning Electron Microscopy (SEM). The average diameter of the prepared silver colloidal nanoparticles was about 52 nm. Absolute inhibitions (100%) were observed on treated with a 270 and 540 µg ml-1 concentration of AgNPs. The results from the study of the AgNPs antifungal effect are significant and suggest that the synthesized silver nanoparticles may have an advantage compared with conventional fungicides.

Keywords: antifungal activity, Phomopsis sp., seeds, silver nanoparticles, soybean

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Authors: N. Gopal, K. Jaasminerjiit, L. Z. Xiang

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Quinazoline-4(3H)-one ring system has been consistently regarded as promising privileged structural icon owing to its pharmacodynamic versatility in many of its synthetic derivatives as well as in several naturally occurring alkaloids. The literature reveals that 2nd & 3rd positions of the quinazolin-4(3H)-one pharmacophore are the target for substitution with other moieties. On the other hand, sulphanilamide derivatives and isatin moiety also displayed valuable biological activities. Hence, it was thought worthwhile to study the effects of three pharmacophoric moieties like quinazolinone, sulphanilamide and isatin in a single molecule for the better analgesic activity with lower toxicity. Series of novel 2,3-disubstituted quinazolin-4(3H)-one derivatives have been synthesised from the intermediate Schiff base of 2-(4’-substitutedphenyl)-3-[(N-2-oxoindolin-3-ylidene)-4”-sulphonamidophenyl]-quinazolin-4(3H)-one derivatives, which was prepared from reacting 2-(substituted phenyl)-4H-benzo[d][1,3]-oxazin-4-one with sulphanilamide. The required benzoxazinone derivatives were prepared by reacting anthranilic acid with benzoyl chloride. All the compounds structure was characterised by using H1 NMR, IR and Mass spectroscopy. The intermediate Schiff base and final Mannich base compounds were evaluated for their analgesic activity by acetic acid-induced writhing method at the dose of 25mg/kg, 50 mg/kg, and 100 mg/kg (bw) and Diclofenac (25mg/kg of body weight) will be used as the reference drugs. From the results of the study, it has been observed that final Mannich base showed a better analgesic activity when compared to the parent Schiff bases, it was found that compound substituted with N-methyl piperazine at 1st position of the indole nucleus of the final quinazolinone derivatives (GA4B1) i.e. 2-(4’-methoxy phenyl)-3-[4-(N-(1-N-methyl piperazine amine) methyl-2-oxo indoilin-3-ylidene] benzenesulfonyl quinazolin-4(3H)-one increases the analgesic activity and among the synthesised compounds, GA4B1 exhibited quite superior analgesic activity. The remaining Schiff bases and Mannich base derivatives exhibited moderate analgesic activity. All the compounds showed a dose dependent activity. None of the synthesised compound showed ulcer index whereas the standard drug, diclofenac [25 mg/kg (bw)] showed significantly higher gross ulcer index values.

Keywords: analgesic activity, isatin, mannich base, quinazolin-4(3H)-one

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Authors: Ali Garba Danjani, Abdulrasheed Halliru Usman

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Due to the rapidly increasing biological applications and antibacterial properties of versatile chitosan composites, the effects of chitosan/polyvinyl chloride composites film were investigated. Chitosan/polyvinyl chloride films were prepared by a casting method. Polyethylene glycol (PEG) was used as a plasticizer in the blending stage of film preparation. Characterizations of films were done by Scanning Electron microscopy (SEM), Fourier transforms infrared spectroscopy (FTIR), and thermogravimetric analyzer (TGA). Chitosan composites incorporation enhanced the antibacterial activity of chitosan films against Escherichia coli and Staphylococcus aureus. The composite film produced is proposed as packaging or coating material because of its flexibility, antibacterial efficacy, and good mechanical strength.

Keywords: chitosan, polymeric nanocomposites, antibacterial activity, polymer blend

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Authors: Hai Quang Hong Dam, Hai Ho, Minh Hoang Le Ngo

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This paper investigates the problem of blind speech separation from the speech mixture of two speakers. A voice activity detector employing the Steered Response Power - Phase Transform (SRP-PHAT) is presented for detecting the activity information of speech sources and then the desired speech signals are extracted from the speech mixture by using an optimal beamformer. For evaluation, the algorithm effectiveness, a simulation using real speech recordings had been performed in a double-talk situation where two speakers are active all the time. Evaluations show that the proposed blind speech separation algorithm offers a good interference suppression level whilst maintaining a low distortion level of the desired signal.

Keywords: blind speech separation, voice activity detector, SRP-PHAT, optimal beamformer

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Authors: Mohammad Karimi Moridani, Mohammad Abdi Zadeh, Zahra Shahiazar Mazraeh

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In recent years, the use of intelligent systems in biomedical engineering has increased dramatically, especially in the diagnosis of various diseases. Also, due to the relatively simple recording of the electrocardiogram signal (ECG), this signal is a good tool to show the function of the heart and diseases associated with it. The aim of this paper is to design an intelligent system for automatically detecting a normal electrocardiogram signal from abnormal one. Using this diagnostic system, it is possible to identify a person's heart condition in a very short time and with high accuracy. The data used in this article are from the Physionet database, available in 2016 for use by researchers to provide the best method for detecting normal signals from abnormalities. Data is of both genders and the data recording time varies between several seconds to several minutes. All data is also labeled normal or abnormal. Due to the low positional accuracy and ECG signal time limit and the similarity of the signal in some diseases with the normal signal, the heart rate variability (HRV) signal was used. Measuring and analyzing the heart rate variability with time to evaluate the activity of the heart and differentiating different types of heart failure from one another is of interest to the experts. In the preprocessing stage, after noise cancelation by the adaptive Kalman filter and extracting the R wave by the Pan and Tampkinz algorithm, R-R intervals were extracted and the HRV signal was generated. In the process of processing this paper, a new idea was presented that, in addition to using the statistical characteristics of the signal to create a return map and extraction of nonlinear characteristics of the HRV signal due to the nonlinear nature of the signal. Finally, the artificial neural networks widely used in the field of ECG signal processing as well as distinctive features were used to classify the normal signals from abnormal ones. To evaluate the efficiency of proposed classifiers in this paper, the area under curve ROC was used. The results of the simulation in the MATLAB environment showed that the AUC of the MLP and SVM neural network was 0.893 and 0.947, respectively. As well as, the results of the proposed algorithm in this paper indicated that the more use of nonlinear characteristics in normal signal classification of the patient showed better performance. Today, research is aimed at quantitatively analyzing the linear and non-linear or descriptive and random nature of the heart rate variability signal, because it has been shown that the amount of these properties can be used to indicate the health status of the individual's heart. The study of nonlinear behavior and dynamics of the heart's neural control system in the short and long-term provides new information on how the cardiovascular system functions, and has led to the development of research in this field. Given that the ECG signal contains important information and is one of the common tools used by physicians to diagnose heart disease, but due to the limited accuracy of time and the fact that some information about this signal is hidden from the viewpoint of physicians, the design of the intelligent system proposed in this paper can help physicians with greater speed and accuracy in the diagnosis of normal and patient individuals and can be used as a complementary system in the treatment centers.

Keywords: neart rate variability, signal processing, linear and non-linear features, classification methods, ROC Curve

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Paúl I. Cornejo

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The elastic energy released through Strombolian explosions has been quite studied, detailing various processes, sources, and precursory events at several volcanoes. We realized an analysis based on the relative partitioning of the elastic energy radiated into the atmosphere and ground by Strombolian-type explosions recorded at El Reventador volcano, using infrasound and seismic signals at high and moderate seismicity episodes during intense eruptive stages of explosive and effusive activity. Our results show that considerable values of Volcano Acoustic-Seismic Ratio (VASR or η) are obtained at high seismicity stages. VASR is a physical diagnostic of explosive degassing that we used to compare eruption mechanisms at El Reventador volcano for two datasets of explosions recorded at a Broad-Band BB seismic and infrasonic station located at ~5 kilometers from the vent. We conclude that the acoustic energy EA released during explosive activity (VASR η = 0.47, standard deviation σ = 0.8) is higher than the EA released during effusive activity; therefore, producing the highest values of η. Furthermore, we realized the analysis and characterization of the eruptive intensity for two episodes at high seismicity, calculating a η three-time higher for an episode of effusive activity with an occasional explosive component (η = 0.32, and σ = 0.42), than a η for an episode of only effusive activity (η = 0.11, and σ = 0.18), but more energetic.

Keywords: effusive, explosion quakes, explosive, Strombolian, VASR

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Authors: David Xiaoqian Sun, Khairunnisa Binti Haji Sibah, Jr., Lejak Anak Ambol

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The purpose of the study was to examine the relationships between objectively measured physical activity levels (PALs), enjoyment of physical activity (EPA), and body mass index (BMI) among adolescents. A total of 188 12-14-year-old Bruneian secondary school adolescents (88 boys and 100 girls) voluntarily took part in this study. Subjects wore the RT3 accelerometer for seven consecutive days in order to measure their PALs. Times of students’ engagement in total (TPA), light (LPA), moderate (MPV), and vigorous PA (VPA) were obtained from the accelerometer. Their BMIs were calculated from their body height and weight. Physical Activity Enjoyment Scale (PACES) was administrated to obtain their EPA levels. Four key enjoyment factors including fun factors, positive perceptions, unexciting in doing activities, and negative perceptions were identified. Subjects’ social economic status (SES) was provided by school administration. Results show that all the adolescents did not meet the recommended PA guidelines even though boys were engaged in more MVPA than girls. No relationships were found between BMI and all PALs in both boys and girls. BMI was significantly related to the PACES scores (r = -.22, p = 0.01), fun factors (r = -.20, p = 0.05) and positive perceptions (r =-.21, p < 0.05). The PACES scores were significantly related to LPA (r = .18, p = 0.01) but not related to MVPA (r = .04, p > 0.05). After controlling for age and SES, BMI was only significantly related to the PACES scores in girls (r = -.27, p < .01) but boys (r = -.06, p > 0.05). Fun factors were significantly related to LPA and MVPA (p < .01) in girls while negative perceptions were significantly related to LPA and MVPA (p < .01) in boys. This study provides evidence that enjoyment may be a trigger of LPA but MVPA and may be influenced by their BMI status particularly in girls. Based on these findings, physical and health educators are suggested to not only make PA more enjoyable, but also consider gender differences in promoting adolescents' participation in MVPA.

Keywords: accelerometer, body mass index, enjoyment of physical activity, moderate to vigorous physical activity

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Debasmita Dubey, Rajesh Kumar Meher, Smruti Pragya Samal, Pradeep Kumar Naik

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Background: To determine antioxidant properties and anticancer activity by ROS and mitochondrial transmembrane potential mediated apoptosis against MCF7, MDA-MB-231, cell line. Methods: Leaf sample was extracted using methanol by microwave digestion technique. The antioxidant properties of the methanolic extract were determined by a DPPH scavenging assay. In vitro anticancer activity, mitochondrial transmembrane potential, apoptosis activity and DNA fragmentation study, as well as intracellular ROS activity of most potential leaf extract, were also determined by using the MDA-MB-231cell line. In vivo animal toxicity study was carried out using mice model. Results: Methanolic leaf extract has shown the highest antioxidant, as well as anticancer activity, is based on the assay conducted. For the identification of active phytochemicals from methanolic extract, High-resolution mass spectroscopy-LCMS was used. In vitro cytotoxicity study against MCF-7 and MDA-MB-231 cell line and IC 50 value was found to be 37.5µg/ml. From histopathological studies, no toxicity in liver and kidney tissue was identified. Conclusion: This plant tuber can be used as a regular diet to reduce the chance of breast cancer. Further, more studies should be conducted to isolate and identify the responsible compound.

Keywords: human breast adenocarcinoma, ROS, mitochondrial transmembrane, apoptosis

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Authors: Francis Machumi, Ester Innocent, Pius Yanda, Philip C. Stevenson

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Curative dependence of traditionally used medicinal plants on season of harvest is an alleged claim by traditional health practitioners. This study intended to verify these claims by investigating antifungal activity and chemical composition of traditionally used medicinal plants Turraea holstii and Clausena anisata harvested in rainy season and dry season. The antifungal activities were determined by broth microdilution method whereas chemical profiling of the extracts from the plant materials was done by gas chromatography (GC). Results indicated that extracts of plant materials harvested in dry season showed enhanced antifungal activity as compared to extracts of plant materials harvested in rainy season. GC chromatograms showed overalls increase in number and amount of chemical species for extracts of plant materials harvested in dry season as compared to extracts of plant materials harvested in rainy season.

Keywords: antifungal activity, chemical composition, medicinal plants, seasonal dependence

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Authors: Arsalan Khan, Faisal Jamil

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The unprecedented increase in anthropogenic gases in recent decades has led to climatic changes worldwide. CO2 emissions are the most important factors responsible for greenhouse gases concentrations. This study decomposes the changes in overall CO2 emissions in Pakistan for the period 1990-2012 using Log Mean Divisia Index (LMDI). LMDI enables to decompose the changes in CO2 emissions into five factors namely; activity effect, structural effect, intensity effect, fuel-mix effect, and emissions factor effect. This paper confirms an upward trend of overall emissions level of the country during the period. The study finds that activity effect, structural effect and intensity effect are the three major factors responsible for the changes in overall CO2 emissions in Pakistan with activity effect as the largest contributor to overall changes in the emissions level. The structural effect is also adding to CO2 emissions, which indicates that the economic activity is shifting towards more energy-intensive sectors. However, intensity effect has negative sign representing energy efficiency gains, which indicate a good relationship between the economy and environment. The findings suggest that policy makers should encourage the diversification of the output level towards more energy efficient sub-sectors of the economy.

Keywords: energy consumption, CO2 emissions, decomposition analysis, LMDI, intensity effect

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Authors: Manuela Caciula, Luis Torres, Simion Tomoiaga

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Introduction: The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), more commonly referred to as COVID-19, has wreaked havoc on all facets of higher education since its inception in late 2019. College students, in particular, significantly reduced their daily energy expenditure and increased the time spent sitting to listen to online classes and complete their studies from home. This change, in combination with the associated COVID-19 lockdown, presumably decreased physical activity levels, increased mental health symptoms, and led to the promotion of unhealthy eating habits. Objectives: The main objective of this study was to determine the current self-reported physical activity levels, mental health symptoms, and body composition of college students after the COVID-19 lockdown in order to develop future interventions for the overall improvement of health. Methods: All participants completed pre-existing, well-validated surveys for both physical activity (International Physical Activity Questionnaire - long form) and mental health (Hospital Anxiety and Depression Scale). Body composition was assessed in person with the use of an Inbody 570 device. Results: Of the 90 American college students (M age = 22.52 ± 4.54, 50 females) who participated in this study, depressive and anxious symptom scores consistent with 58% (N = 52) heightened symptomatology, 17% (N = 15) moderate borderline symptomatology, and 25% (N = 23) asymptomatology were reported. In regard to physical activity, 79% (N = 71) of the students were highly physically active, 18% (N = 16) were moderately active, and 3% (N = 3) reported low levels of physical activity. Additionally, 46% (N = 41) of the students maintained an unhealthy body fat percentage based on World Health Organization recommendations. Strong, significant relationships were found between anxiety and depression symptomatology and body fat percentage (P = .003) and skeletal muscle mass (P = .015), with said symptomatology increasing with added body fat and decreasing with added skeletal muscle mass. Conclusions: Future health interventions for American college students should be focused on strategies to reduce stress, anxiety, and depressive characteristics, as well as nutritional information on healthy eating, regardless of self-reported physical activity levels.

Keywords: physical activity, mental health, body composition, COVID-19

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Taekyun Hur, Yoonyoung Kim, Junkyu Lim

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Previous works on leisure activities had been categorizing activities arbitrarily and subjectively while focusing on a single dimension (e.g. active-passive, individual-group). To overcome these problems, this study proposed a Korean leisure activities’ matrix model that considered multidimensional features of leisure activities, which was comprised of 3 main factors and 6 sub factors: (a) Active (physical, mental), (b) Relational (quantity, quality), (c) Expert (entry barrier, possibility of improving). We developed items for measuring the degree of each dimension for every leisure activity. Using the developed Leisure Activities Dimensions (LAD) questionnaire, we investigated the presented dimensions of a total of 78 leisure activities which had been enjoyed by most Koreans recently (e.g. watching movie, taking a walk, watching media). The study sample consisted of 1348 people (726 men, 658 women) ranging in age from teenagers to elderlies in their seventies. This study gathered 60 data for each leisure activity, a total of 4860 data, which were used for statistical analysis. First, this study compared 3-factor model (Activity, Relation, Expertise) fit with 6-factor model (physical activity, mental activity, relational quantity, relational quality, entry barrier, possibility of improving) fit by using confirmatory factor analysis. Based on several goodness-of-fit indicators, the 6-factor model for leisure activities was a better fit for the data. This result indicates that it is adequate to take account of enough dimensions of leisure activities (6-dimensions in our study) to specifically apprehend each leisure attributes. In addition, the 78 leisure activities were cluster-analyzed with the scores calculated based on the 6-factor model, which resulted in 8 leisure activity groups. Cluster 1 (e.g. group sports, group musical activity) and Cluster 5 (e.g. individual sports) had generally higher scores on all dimensions than others, but Cluster 5 had lower relational quantity than Cluster 1. In contrast, Cluster 3 (e.g. SNS, shopping) and Cluster 6 (e.g. playing a lottery, taking a nap) had low scores on a whole, though Cluster 3 showed medium levels of relational quantity and quality. Cluster 2 (e.g. machine operating, handwork/invention) required high expertise and mental activity, but low physical activity. Cluster 4 indicated high mental activity and relational quantity despite low expertise. Cluster 7 (e.g. tour, joining festival) required not only moderate degrees of physical activity and relation, but low expertise. Lastly, Cluster 8 (e.g. meditation, information searching) had the appearance of high mental activity. Even though clusters of our study had a few similarities with preexisting taxonomy of leisure activities, there was clear distinctiveness between them. Unlike the preexisting taxonomy that had been created subjectively, we assorted 78 leisure activities based on objective figures of 6-dimensions. We also could identify that some leisure activities, which used to belong to the same leisure group, were included in different clusters (e.g. filed ball sports, net sports) because of different features. In other words, the results can provide a different perspective on leisure activities research and be helpful for figuring out what various characteristics leisure participants have.

Keywords: leisure, dimensional model, activity, relationship, expertise

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Authors: Xiyang Li, Qi Yu, Lun Zhang

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In the face of natural disasters and other emergency situations, determining the optimal location of rescue centers is crucial for improving rescue efficiency and minimizing impact on affected populations. This paper proposes a method that integrates genetic algorithms (GA) and backpropagation neural networks (BPNN) to address the site selection optimization problem for emergency rescue centers. We utilize BPNN to accurately estimate the cost of delivering supplies from rescue centers to each temporary camp. Moreover, a genetic algorithm with a special partially matched crossover (PMX) strategy is employed to ensure that the number of temporary camps assigned to each rescue center adheres to predetermined limits. Using the population distribution data during the 2022 epidemic in Jiading District, Shanghai, as an experimental case, this paper verifies the effectiveness of the proposed method. The experimental results demonstrate that the BPNN-GA method proposed in this study outperforms existing algorithms in terms of computational efficiency and optimization performance. Especially considering the requirements for computational resources and response time in emergency situations, the proposed method shows its ability to achieve rapid convergence and optimal performance in the early and mid-stages. Future research could explore incorporating more real-world conditions and variables into the model to further improve its accuracy and applicability.

Keywords: emergency rescue centers, genetic algorithms, back-propagation neural networks, site selection optimization

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Authors: S. A. Nirmal, G. S. Asane, S. C. Pal, S. C. Mandal

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Objective: Ficus religiosa Linn (Moraceae) is being used in traditional medicine to improve immunity hence present work was undertaken to validate this use scientifically. Material and Methods: Dried, powdered bark of F. religiosa was extracted successively using petroleum ether and 70% ethanol in soxhlet extractor. The extracts obtained were screened for immunomodulatory activity by delayed type hypersensitivity (DTH), neutrophil adhesion test and cyclophosphamide-induced neutropenia in Swiss albino mice at the dose of 50 and 100 mg/kg, i.p. 70% ethanol extract showed significant immunostimulant activity hence subjected to column chromatography to produce tyrosine rich fraction (TRF). TRF obtained was screened for immunomodulatory activity by above methods at the dose of 10 mg/kg, i.p. Results: TRF showed potentiation of DTH response in terms of significant increase in the mean difference in foot-pad thickness and it significantly increased neutrophil adhesion to nylon fibers by 48.20%. Percentage reduction in total leukocyte count and neutrophil by TRF was found to be 43.85% and 18.72%, respectively. Conclusion: Immunostimulant activity of TRF was more pronounced and thus it has great potential as a source for natural health products.

Keywords: Ficus religiosa, immunomodulatory, cyclophosphamide, neutropenia

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Authors: Khansaa Al-Essa

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Nanosized ceria (CeO₂) and graphitic carbon nitride-loaded ceria (CeO₂/GCN) nanocomposite have been synthesized by the coprecipitation method and studied its photocatalytic activity for methyl red degradation under Visible type radiation. A phase formation study was carried out by using an x-ray diffraction technique, and it revealed that ceria (CeO₂) is properly supported on the surface of GCN. Ceria nanoparticles and CeO₂/GCN nanocomposite were confirmed by transmission electron microscopy technique. The particle size of the CeO₂, CeO₂/GCN nanocomposite is in the range of 10-15 nm. Photocatalytic activity of the CeO₂/g-C3N4 composite was improved as compared to CeO₂. The enhanced photocatalytic activity is attributed to the increased visible light absorption and improved adsorption of the dye on the surface of the composite catalyst.

Keywords: photodegradation, dye, nanocomposite, graphitic carbon nitride-CeO₂

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Authors: Joanna Dzieńdziora

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Lobbying is an instrument of influence in various decision-making processes. It is also the underestimated issue as a research problem. The lack of research on the modern lobbyist competencies is the most crucial element. The paper presents attempts of finding answers to the following questions: Who should run the lobbying activity? What competencies should a lobbyist possess in order to implement lobbying activities effectively? Searching for answers for the mentioned above questions requires positioning the opportunity to change the image of lobbying in the area of competencies of entities that provide lobbying activities. The aim of the paper is presenting the lobbyist competencies profile in the framework of his professional role. The essence of lobbying activity and its significance in the modern economy as well as areas, the scope of lobbying activities, diagnosis of a modern lobbyist’s competences, lobbyist’s competencies profile that is focused on the professionalization of the lobbying activity, will have been presented in this paper. Indicated research tasks let emerge lobbyist’s competencies in the way that allows identifying and elaborating the lobbyist competencies profile. The profile lets improve lobbying activities. Its elaboration is based on the author’s research results analysis. Taking into consideration the shortages within the theory and research on the lobbying activity, the implementation of this research enables to fill the cognitive gap existing in the theory of management sciences.

Keywords: competencies, competencies profile, lobbying, lobbyist

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Authors: Ahuva Even-Zohar, Shoshi Werner

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Context: Sexuality is an important aspect of overall quality of life for individuals across different age groups and health conditions. Sexual interest and activity continue to be important and play a role in people's life as they age. Despite this, there is limited research on the sexual health of older adults. Research Aim: The study aims to examine the knowledge, attitudes, and sexual activity of older adults and to explore the relationship between sexual activity and quality of life among this population. Methodology: The study involved 203 Jewish participants from Israel, with an average age of 69.59. The participants completed questionnaires administered through an Internet panel. The questionnaires measured variables such as knowledge about and attitudes towards sexuality, sexual activity, quality of life, and socio-demographic information. Findings: The study found that a majority of the participants reported engaging in sexual activity, with most of them experiencing full sexual intercourse. Approximately half of the participants expressed high levels of satisfaction with their sexual activity. The results indicated that older adults demonstrated a moderate level of knowledge and permissive attitudes towards sexuality in later life. Moreover, higher levels of knowledge and permissive attitudes were associated with increased sexual activity. The frequency of sexual activity was identified as a predictor of quality of life, with a mediating effect on the relationship between attitudes towards older adults' sexuality and quality of life. Notably, men and older adults who were married or in a relationship reported higher frequencies of sexual activity compared to women and older adults without a partner. Furthermore, a majority of participants did not seek professional help or discuss their sexual concerns with a therapist. Theoretical Importance: This research contributes to our understanding of a topic that is often considered taboo - sexuality among older adults. It highlights that older adults maintain an interest in sexual activity, and that engaging in such activity contributes to their overall quality of life. Data Collection and Analysis Procedures: The data for this study were collected using structured questionnaires administered through an Internet panel. The questionnaires included closed-ended questions, allowing for quantitative data analysis. Descriptive statistics and regression analysis were performed to examine the relationships between the variables. Questions Addressed: This study aimed to address the following questions: What is the level of knowledge and attitudes towards sexuality among older adults? How prevalent is sexual activity among older adults and what factors are associated with it? How does sexual activity impact the quality of life of older adults? Do older adults seek professional help for their sexual concerns? Conclusion: The main conclusion drawn from this research is that sexuality is a crucial aspect of older adults' lives and significantly contributes to their quality of life. The study emphasizes the need for educational programs aimed at older adults and professionals, which promote the understanding and benefits of sexuality in later life. It also suggests that professionals should actively encourage older individuals to seek help and support when experiencing difficulties related to sexuality.

Keywords: men, older adults, quality of life, sexuality, women

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Authors: Malkin Valery, Rogaleva Liudmila

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During the last 50-60 years the sports psychology has become firmly established in sports. At the same time, the sport practice brings evidence that it is only beginning to solve some of the most important problems in sports. It is untimely to say that the sports psychology has become a compulsory and efficient part of the sportsman’s preparation. It seems that the further development of the sports psychology can be seen, on the one hand, in the re-orientation of the psychologists from the regulation of the sportsman’s mentality to the process of forming the subject of the sport activity able to take the overall responsibility for the result of the sport activity, able to independently set objectives and to overcome the psychological difficulties that arise in the process of attaining these objectives. In its turn, it will require the change in the very approach to the psychologist’s work. The psychologist and the couch will turn from the specialists in correcting the negative manifestations of the sportsman’s mentality to the specialists in forming the subjects of the sport activity. It will require the creation of the technologies that can form the subjects on all the age-specific stages of the sport activity, that can form the most important psychological qualities (psychological stability, mental reliability, etc.). Getting these technologies will enable the couch to change from the consumer of the psychological knowledge to the immediate participant of the psychological process.

Keywords: sports psychology, subject, sportsman’s preparation, psychological knowledge

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

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The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

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This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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Authors: Babak Azari, Afshin Pourahmad, Babak Sadeghi, Masuod Mokhtari

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In this work, Equisetnm arvense plant extract was used for preparing amorphous SiO₂. For preparing of SiO₂/zeolitic imidazolate framework-8 (ZIF-8) nanocomposite by solvothermal method, the synthesized SiO₂ was added to the synthesis mixture ZIF-8. The nanocomposite was characterized using a range of techniques. The photocatalytic activity of SiO₂/ZIF-8 was investigated systematically by degrading crystal violet as a cationic dye under Ultraviolet light irradiation. Among synthesized samples (SiO₂, ZIF-8 and SiO₂/ZIF-8), the SiO₂/ZIF-8 exhibited the highest photocatalytic activity and improved stability compared to pure SiO₂ and ZIF-8. As evidenced by Scanning Electron Microscopy and Transmission electron microscopy images, ZIF-8 particles without aggregation are located over SiO₂. The SiO₂ not only provides structured support for ZIF-8 but also prevents the aggregation of ZIF-8 Metal-organic framework in comparison to the isolated ZIF-8. The superior activity of this photocatalyst was attributed to the synergistic effects from SiO₂ owing to (I) an electron acceptor (from ZIF-8) and an electron donor (to O₂ molecules), (II) preventing recombination of electron-hole in ZIF-8, and (III) maximum interfacial contact ZIF-8 with the SiO₂ surface without aggregation or prevent the accumulation of ZIF-8. The results demonstrate that holes (h+) and •O₂- are primary reactive species involved in the photocatalytic oxidation process. Moreover, the SiO₂/ZIF-8 photocatalyst did not show any obvious loss of photocatalytic activity during five-cycle tests, which indicates that the heterostructured photocatalyst was highly stable and could be used repeatedly.

Keywords: nano, zeolit, potocatalist, nanocomposite

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Authors: M. Umar Dahot, G. S. Mangrio

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In this study Agro-industrial waste was used as a carbon source, which is a low cost substrate. Along with this, various sugars and molasses of 2.5% and 5% were investigated as substrate/carbon source for the growth of A.niger and Pectinase production. Different nitrogen sources were also used. An overview of results obtained show that 5% sucrose, 5% molasses and 0.4% (NH4)2SO4 were found the best carbon and nitrogen sources for the production of pectinase by A. niger. The maximum production of pectinase (26.87units/ml) was observed at pH 6.0 after 72 hrs incubation. The optimum temperature for the maximum production of pectinase was achieved at 35ºC when maximum production of pectinase was obtained as 28.25Units/ml.Pectinase enzyme was purified with ammonium sulphate precipitation and dialyzed sample was finally applied on gel filtration chromatography (Sephadex G-100) and Ion Exchange DEAE A-50. The enzyme was purified 2.5 fold by gel chromatography on Sephadex G-100 and Four fractions were obtained, Fraction 1, 2, 4 showed single band while Fraction -3 showed multiple bands on SDS Page electrophoresis. Fraction -3 was pooled, dialyzed and separated on Sephdex A-50 and two fractions 3a and 3b showed single band. The molecular weights of the purified fractions were detected in the range of 33000 ± 2000 and 38000± 2000 Daltons. The purified enzyme was specifically most active with pure pectin, while pectin, Lemon pectin and orange peel given lower activity as compared to (control). The optimum pH and temperature for pectinase activity was found between pH 5.0 and 6.0 and 40°- 50°C, respectively. The enzyme was stable over the pH range 3.0-8.0. The thermostability of was determined and it was observed that the pectinase activity is heat stable and retains activity more than 40% when incubated at 90°C for 10 minutes. The pectinase activity of F3a and F3b was increased with different metal ions. The Pectinase activity was stimulated in the presence of CaCl2 up to 10-30%. ZnSO4, MnSO4 and Mg SO4 showed higher activity in fractions F3a and F3b, which indicates that the pectinase belongs to metalo-enzymes. It is concluded that A. niger is capable to produce pH stable and thermostable pectinase, which can be used for industrial purposes.

Keywords: pectinase, a. niger, production, purification, characterization

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Authors: Noura Shehab-Eldeen, Mohamed Elsherbeeny, Hossam Elmetwally, Mohamed Salama, Ahmed Lotfy, Mohamed Elgamal, Hussein Sheashaa, Mohamed Sobh

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Mitochondrial toxins including papaverine may be implicated in the etiology and pathogenesis of Parkinson's disease. The aim was to detect the effect of papaverine on the proliferation and viability of neural stem cells. Rat neural progenitor cells were isolated from embryos (E14) brains. The dispersed tissues were allowed to settle, then, The supernatant was centrifuged at 1,000 g for 5 min. The pellet was placed in Hank’s solution cultured as free-floating neurospheres Dulbecco’s modified Eagle medium (DMEM) and Hams F12 (3:1) supplemented with B27 (Invitrogen GmBH, Karlsruhe, Germany), 20 ng/mL epidermal growth factor (EGF; Biosource, Karlsruhe, Germany), 20 ng/mL recombinant human fibroblast growth factor (rhFGF; R&D Systems, Wiesbaden-Nordenstadt, Germany), and penicillin and streptomycin (1:100; Invitrogen) at 37°C with 7.5% CO2 . Differentiation was initiated by growth factor withdrawal and plating onto a poly-d-lysine/ laminin matrix. The neurospheres were fed every 2-3 days by replacing 50% of the culture media with fresh media. The culture suspension was transferred to a dish containing 16 wells. The wells were divided as follows: 4 wells received no papaverine (control), 4 wells 1 u, 4 wells 5 u and 4 wells 10 u of papaverine solution. In the next 2 weeks, photography (0,4,5,11days) and viability test were done. The photographs were analysed. Results : papaverine didn't affect proliferation of neurospheres, while it affected viability compared to control , this was dose related. Conclusion: This indicates the harmful effect of papaverine suggesting it to be a candidate neurotoxin causing Parkinsonism.

Keywords: neurospheres, neural stem cells, papaverine, Parkinsonism

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Authors: Paweł Konieczka, Lech Raczyński, Wojciech Wiślicki, Oleksandr Fedoruk, Konrad Klimaszewski, Przemysław Kopka, Wojciech Krzemień, Roman Shopa, Jakub Baran, Aurélien Coussat, Neha Chug, Catalina Curceanu, Eryk Czerwiński, Meysam Dadgar, Kamil Dulski, Aleksander Gajos, Beatrix C. Hiesmayr, Krzysztof Kacprzak, łukasz Kapłon, Grzegorz Korcyl, Tomasz Kozik, Deepak Kumar, Szymon Niedźwiecki, Dominik Panek, Szymon Parzych, Elena Pérez Del Río, Sushil Sharma, Shivani Shivani, Magdalena Skurzok, Ewa łucja Stępień, Faranak Tayefi, Paweł Moskal

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This paper develops the transformation of non-image data into 2-dimensional matrices, as a preparation stage for classification based on convolutional neural networks (CNNs). In positron emission tomography (PET) studies, CNN may be applied directly to the reconstructed distribution of radioactive tracers injected into the patient's body, as a pattern recognition tool. Nonetheless, much PET data still exists in non-image format and this fact opens a question on whether they can be used for training CNN. In this contribution, the main focus of this paper is the problem of processing vectors with a small number of features in comparison to the number of pixels in the output images. The proposed methodology was applied to the classification of PET coincidence events.

Keywords: convolutional neural network, kernel principal component analysis, medical imaging, positron emission tomography

Procedia PDF Downloads 144