Search results for: activity models

Authors: Nijole Savickiene, Antonella Di Sotto, Gabriela Mazzanti, Rasa Starselskyte, Silvia Di Giacomo, Annabella Vitalone

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Plant lectins are non-enzymic and non-immune origin proteins that specifically recognize and bind to various sugar structures and possess the activity to agglutinate cells and/or precipitate polysaccharides and glycoconjugates. The emerging evidences showed that plant lectins contribute not only to tumour cell recognition but also to cell adhesion and localization, to signal transduction, to mitogenic cytotoxicity and apoptosis. Among chitin-binding lectins, the Urtica dioica agglutinin (UDA), which is a complex of different isoforms, has been poorly studied for its biological activity. In this context and according to the increasing interest for lectins as novel antitumor drugs, present paper aimed at evaluating the potential antimutagenic activity of a lectin-like glycoprotein-enriched fraction from aerial part of Urtica dioica L. Aim: to evaluate the potential chemopreventive properties of a protein - lectin fraction from the aerial part of Urtica dioica. Materials and methods: Protein – lectin fraction has been tested for the antimutagenic activity in bacteria (50–800 mg/plate; Ames test by the preincubation method) and for the cytotoxicity on human hepatoma HepG2 cells (0.06–2 mg/mL; 24 and 48 h incubation). Results: Protein – lectin fraction from stinging nettle was not cytotoxic on HepG2 cells up to 2 mg/mL; conversely, it exhibited a strong antimutagenic activity against the mutagen 2-aminoanthracene (2AA) in all strains tested (maximum inhibition of 56.78 and 61% in TA98, TA100, and WP2uvrA strains, respectively, at 800 mg/plate). Discussion and conclusions: Protein – lectin fraction from Urtica dioica L. possesses antimutagenic and radical scavenging properties. Being 2AA a pro-carcinogenic agent, we hypothesize that the antimutagenicity of it can be due to the inhibition of CYP450-isoenzymes, involved in the mutagen bioactivation.

Keywords: lectins, antimutagenicity, chemoprevention, Urtica dioica

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Authors: I. L. Ibrahim, A. Mann, B. M. Abdullahi

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Infectious diseases are prevalent in developing countries and plant extracts are known to contained bioactive compounds that can be used in the management of these diseases. The entire plant of Chrysanthellum indicum (Linn) was air-dried and pulverized into fine powder and then percolated to give ethanol and aqueous extracts. These extracts were phytochemically screened for metabolites and evaluated antibacterial activity against some pathogenic organisms Klebsilla, pneumonia, Bacillus subtilis, and Pseudomonas aeruginosa using agar dilution method. It was found that crude extracts of C. indicum revealed the presence of saponins, tannins, alkaloids, steroidal nucleus, cardiac glycosides, and coumarin while flavonoids and anthraquinones were absent. The Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) of the active extract of C. indicum shows that the extract could be a potential source of antibacterial agents.

Keywords: antibacterial activity, Chrysanthellum indicum, infectious diseases, phytochemical screening

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Authors: Andreas Aceranti, Simonetta Vernocchi, Marco Colorato, Massimo Briamo, Giovanni Abalsamo

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Most of the benefits of sport come from related physical activity, however, there are secondary psychological positive effects. There are also obvious disadvantages, high tensions related to failure, injuries, eating disorders and burnout. Depressive symptoms and illnesses related to anxiety or stress can be preventable or even simply alleviated through regular activity and exercise. It has been shown that the practice of a sport brings physical benefits, but can also have psychological and spiritual benefits. Reduced performance in male individuals has been linked to sexual activity before competitions in the past. The long-standing debate about the impact of sexual activity on sports performance has been controversial in the mainstream media in recent decades. This salacious topic has generated extensive discussion, although its high-quality data has been limited. Literature has, so far, mainly included subjective assessments from surveys. However, such surveys can be skewed as these assessments are based on individual beliefs, perceptions, and memory. There has been a long discussion over the years but even there objective data has been lacking. One reason behind coaches' bans on sexual activity before sporting events may be the belief that abstinence increases frustration, which in turn is shifted into aggressive behavior toward competitors. However, this assumption is not always valid. In fact, depriving an athlete of a normal activity can cause feelings of guilt and loss of concentration. Sexual activity during training can promote relaxation and positively influence performance. The author concludes that, although there is a need for scientific research in this area, it seems that sexual intercourse does not decrease performance unless it is accompanied by late night socialization, loss of sleep or drinking. Although the effects of sexual engagement on aerobic and strength athletic performance have not been definitively established, most research seems to rule out a direct impact. In order to analyze, as much as possible without bias, whether sexual activity significantly affects an athletic performance or not, we sampled 5 amateur athletes, between 22 and 25 years old and all male. The study was based on the timing of 4 running races of 5 champions. We asked participants to respect guidelines to avoid sexual activity (sex or masturbation) 12 hours before 2 of the 4 competitions, and to practice before the remaining 2 races.In doing so, we were able to compare and analyze the impact of activity and abstinence on performance results. We have come to the conclusion that sexual behavior on athletic performance needs to be better understood, more randomized trials and high-quality controls are strongly needed but available information suggests that sexual activity the day before a race has no negative effects on performance.

Keywords: sex, masturbation, male performance, soccer

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Authors: Marco Clericuzio, Alfredo Vizzini

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The secondary metabolites of Russulales (one of the main orders of phylum Basidiomycota), have been studied. They are mainly terpenoids, with sesquiterpenes being the most common ones, but also triterpenoids and prenylated phenols have been isolated. We found that classes of specific compounds seem to be often allied to systematic groupings, so that they may have chemotaxonomic significance. Moreover, the ecological implications of such metabolites, as well as their biological activities, are discussed. Lately, we have focused our attention on the anti-arthropod activity of Russula metabolites, in particular on the toxicity against mites and other crop pests.

Keywords: chemotaxonomy, fungi, insecticidal activity, russulales, terpenoids

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Authors: Snezhana G. Gocheva-Ilieva, Maya P. Stoimenova

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Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.

Keywords: cross-validation, decision tree, lagged variables, short-term forecasting

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Authors: Jessica Lopez Espejel, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane, El Hassane Ettifouri

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Pretrained transformer-based models have shown high performance in natural language generation tasks. However, a new wave of interest has surged: automatic programming language code generation. This task consists of translating natural language instructions to a source code. Despite the fact that well-known pre-trained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformer neural network. It aims to generate java source code from natural language text. JaCoText leverages the advantages of both natural language and code generation models. More specifically, we study some findings from state of the art and use them to (1) initialize our model from powerful pre-trained models, (2) explore additional pretraining on our java dataset, (3) lead experiments combining the unimodal and bimodal data in training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results.

Keywords: java code generation, natural language processing, sequence-to-sequence models, transformer neural networks

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Authors: Angelika Magiera, Weronika Penar, Czesław Klocek

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Intellectual disability (ID) affects approximately 1.55% of children and adults in the society of developed countries. Depending on the ID degree, the patient is burdened with additional disease entities. Intellectual disability does not only limits a person’s opportunities to participate in social life but also affects whole families. People with ID belong to the group of risk of mental illnesses, they are less emotionally stable, while families are predisposed to depression. The study was held in a day care center for people with intellectual disabilities (of various degrees of disability) on 26 people. Nurses and carers also took part. The age range of study groups ranged from 22 to 67 years. Therapeutic classes were held for four independent mixed groups (sex and intellectual disability degree) from 6 to 7 people each, lasting no more than 30 minutes. They were created by the facility's staff to make sure that a group is stable. The animal assisted activity took place with a 2.5-year-old Ragdoll cat. The animal has passed the exam (certificate entitling her to take part in felinotherapy) and had 1.5 years of work experience. Due to the different degrees of ID, an individual emotional state survey was conducted among the caregivers of those who were involved in the activity, to assess the impact of animal assisted activity with a cat on patients. A positive effect on the emotional state of people with different types of intellectual disability was observed. Caregivers and nurses of those participating in the study express willingness to continue these types of classes and consider them necessary for this group of people.

Keywords: intellectual disabilities, animal-assisted activity, cat, feline, emotions

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Anshu Siwach, Qianlai Zhuang, Ratul Baishya

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Context: This study focuses on the influence of moss cover and seasonality on soil microbial biomass and enzymatic activity in different Central Himalayan temperate forest types. Soil microbial biomass and enzymes are key indicators of microbial communities in soil and provide information on soil properties, microbial status, and organic matter dynamics. The activity of microorganisms in the soil varies depending on the vegetation type and environmental conditions. Therefore, this study aims to assess the effects of moss cover, seasons, and different forest types on soil microbial biomass carbon (SMBC), soil microbial biomass nitrogen (SMBN), and soil enzymatic activity in the Central Himalayas, Uttarakhand, India. Research Aim: The aim of this study is to evaluate the levels of SMBC, SMBN, and soil enzymatic activity in different temperate forest types under the influence of two ground covers (soil with and without moss cover) during the rainy and winter seasons. Question Addressed: This study addresses the following questions: 1. How does the presence of moss cover and seasonality affect soil microbial biomass and enzymatic activity? 2. What is the influence of different forest types on SMBC, SMBN, and enzymatic activity? Methodology: Soil samples were collected from different forest types during the rainy and winter seasons. The study utilizes the chloroform-fumigation extraction method to determine SMBC and SMBN. Standard methodologies are followed to measure enzymatic activities, including dehydrogenase, acid phosphatase, aryl sulfatase, β-glucosidase, phenol oxidase, and urease. Findings: The study reveals significant variations in SMBC, SMBN, and enzymatic activity under different ground covers, within the rainy and winter seasons, and among the forest types. Moss cover positively influences SMBC and enzymatic activity during the rainy season, while soil without moss cover shows higher values during the winter season. Quercus-dominated forests, as well as Cupressus torulosa forests, exhibit higher levels of SMBC and enzymatic activity, while Pinus roxburghii forests show lower levels. Theoretical Importance: The findings highlight the importance of considering mosses in forest management plans to improve soil microbial diversity, enzymatic activity, soil quality, and health. Additionally, this research contributes to understanding the role of lower plants, such as mosses, in influencing ecosystem dynamics. Conclusion: The study concludes that moss cover during the rainy season significantly influences soil microbial biomass and enzymatic activity. Quercus and Cupressus torulosa dominated forests demonstrate higher levels of SMBC and enzymatic activity, indicating the importance of these forest types in sustaining soil microbial diversity and soil health. Including mosses in forest management plans can improve soil quality and overall ecosystem dynamics.

Keywords: moss cover, seasons, soil enzymes, soil microbial biomass, temperate forest types

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Authors: Evgeny A. Ermakov, Lyudmila P. Smirnova, Valentina N. Buneva

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Schizophrenia associated with dysregulation of neurotransmitter processes in the central nervous system and disturbances in the humoral immune system resulting in the formation of antibodies (Abs) to the various components of the nervous tissue. Abs to different neuronal receptors and DNA were detected in the blood of patients with schizophrenia. Abs hydrolyzing DNA were detected in pool of polyclonal autoantibodies in autoimmune and infectious diseases, such catalytic Abs were named abzymes. It is believed that DNA-hydrolyzing abzymes are cytotoxic, cause nuclear DNA fragmentation and induce cell death by apoptosis. Abzymes with DNAase activity are interesting because of the mechanism of formation and the possibility of use as diagnostic markers. Therefore, in our work we have set following goals: to determine the level anti-DNA Abs in the serum of patients with schizophrenia and to study DNA-hydrolyzing activity of IgG of patients with schizophrenia. Materials and methods: In our study there were included 41 patients with a verified diagnosis of paranoid or simple schizophrenia and 24 healthy donors. Electrophoretically and immunologically homogeneous IgGs were obtained by sequential affinity chromatography of the serum proteins on protein G-Sepharose and gel filtration. The levels of anti-DNA Abs were determined using ELISA. DNA-hydrolyzing activity was detected as the level of supercoiled pBluescript DNA transition in circular and linear forms, the hydrolysis products were analyzed by agarose electrophoresis followed by ethidium bromide stain. To correspond the registered catalytic activity directly to the antibodies we carried out a number of strict criteria: electrophoretic homogeneity of the antibodies, gel filtration (acid shock analysis) and in situ activity. Statistical analysis was performed in ‘Statistica 9.0’ using the non-parametric Mann-Whitney test. Results: The sera of approximately 30% of schizophrenia patients displayed a higher level of Abs interacting with single-stranded (ssDNA) and double-stranded DNA (dsDNA) compared with healthy donors. The average level of Abs interacting with ssDNA was only 1.1-fold lower than that for interacting with dsDNA. IgG of patient with schizophrenia were shown to possess DNA hydrolyzing activity. Using affinity chromatography, electrophoretic analysis of isolated IgG homogeneity, gel filtration in acid shock conditions and in situ DNAse activity analysis we proved that the observed activity is intrinsic property of studied antibodies. We have shown that the relative DNAase activity of IgG in patients with schizophrenia averaged 55.4±32.5%, IgG of healthy donors showed much lower activity (average of 9.1±6.5%). It should be noted that DNAase activity of IgG in patients with schizophrenia with a negative symptoms was significantly higher (73.3±23.8%), than in patients with positive symptoms (43.3±33.1%). Conclusion: Anti-DNA Abs of patients with schizophrenia not only bind DNA, but quite efficiently hydrolyze the substrate. The data show a correlation with the level of DNase activity and leading symptoms of patients with schizophrenia.

Keywords: anti-DNA antibodies, abzymes, DNA hydrolysis, schizophrenia

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Authors: Chuang-Chun Chiou

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In this study, a systematic framework of graceful ageing has been used to explore the possible human resource utilization models for graceful ageing purpose. This framework is based on the Chinese culture. We call ‘Nine-old’ target. They are ageing gracefully with feeding, accomplishment, usefulness, learning, entertainment, care, protection, dignity, and termination. This study is focused on two areas: accomplishment and usefulness. We exam the current practices of initiatives and laws of promoting labor participation. That is to focus on how to increase Labor Force Participation Rate of the middle aged as well as the elderly and try to promote the elderly to achieve graceful ageing. Then we present the possible models that support graceful ageing.

Keywords: human resource utilization model, labor participation, graceful ageing, employment

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: L. Grinis

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Turbulent flow in complex geometries receives considerable attention due to its importance in many engineering applications. It has been the subject of interest for many researchers. Some of these interests include the design of storm water channels. The design of these channels requires testing through physical models. The main practical limitation of physical models is the so called “scale effect”, that is, the fact that in many cases only primary physical mechanisms can be correctly represented, while secondary mechanisms are often distorted. These observations form the basis of our study, which centered on problems associated with the design of storm water channels near the Dead Sea, in Israel. To help reach a final design decision we used different physical models. Our research showed good coincidence with the results of laboratory tests and theoretical calculations, and allowed us to study different effects of fluid flow in an open channel. We determined that problems of this nature cannot be solved only by means of theoretical calculation and computer simulation. This study demonstrates the use of physical models to help resolve very complicated problems of fluid flow through baffles and similar structures. The study applies these models and observations to different construction and multiphase water flows, among them, those that include sand and stone particles, a significant attempt to bring to the testing laboratory a closer association with reality.

Keywords: open channel, physical modeling, baffles, turbulent flow

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Authors: L. J. de Bessa Neto, V. S. Filho, J. V. Ferreira Nunes, G. C. Bergamo

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There are many situations in which human activities have significant effects on the environment. Damage to the ozone layer is one of them. The objective of this work is to use the Least Squares Method, considering the linear, exponential, logarithmic, power and polynomial models of the second degree, to analyze through the coefficient of determination (R²), which model best fits the behavior of the chlorodifluoromethane (HCFC-142b) in parts per trillion between 1992 and 2018, as well as estimates of future concentrations between 5 and 10 periods, i.e. the concentration of this pollutant in the years 2023 and 2028 in each of the adjustments. A total of 809 observations of the concentration of HCFC-142b in one of the monitoring stations of gases precursors of the deterioration of the ozone layer during the period of time studied were selected and, using these data, the statistical software Excel was used for make the scatter plots of each of the adjustment models. With the development of the present study, it was observed that the logarithmic fit was the model that best fit the data set, since besides having a significant R² its adjusted curve was compatible with the natural trend curve of the phenomenon.

Keywords: chlorodifluoromethane (HCFC-142b), ozone, least squares method, regression models

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Authors: Byeongcheol Kang, Junyi Kim, Hyungsik Jung, Hyungjun Yang, Jaewoo An, Jonggeun Choe

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It is important to figure out reservoir properties for better production management. Due to the limited information, there are geological uncertainties on very heterogeneous or channel reservoir. One of the solutions is to generate multiple equi-probable realizations using geostatistical methods. However, some models have wrong properties, which need to be excluded for simulation efficiency and reliability. We propose a novel method of model selection scheme, based on distance-based clustering for reliable application of production optimization algorithm. Distance is defined as a degree of dissimilarity between the data. We calculate Hausdorff distance to classify the models based on their similarity. Hausdorff distance is useful for shape matching of the reservoir models. We use multi-dimensional scaling (MDS) to describe the models on two dimensional space and group them by K-means clustering. Rather than simulating all models, we choose one representative model from each cluster and find out the best model, which has the similar production rates with the true values. From the process, we can select good reservoir models near the best model with high confidence. We make 100 channel reservoir models using single normal equation simulation (SNESIM). Since oil and gas prefer to flow through the sand facies, it is critical to characterize pattern and connectivity of the channels in the reservoir. After calculating Hausdorff distances and projecting the models by MDS, we can see that the models assemble depending on their channel patterns. These channel distributions affect operation controls of each production well so that the model selection scheme improves management optimization process. We use one of useful global search algorithms, particle swarm optimization (PSO), for our production optimization. PSO is good to find global optimum of objective function, but it takes too much time due to its usage of many particles and iterations. In addition, if we use multiple reservoir models, the simulation time for PSO will be soared. By using the proposed method, we can select good and reliable models that already matches production data. Considering geological uncertainty of the reservoir, we can get well-optimized production controls for maximum net present value. The proposed method shows one of novel solutions to select good cases among the various probabilities. The model selection schemes can be applied to not only production optimization but also history matching or other ensemble-based methods for efficient simulations.

Keywords: distance-based clustering, geological uncertainty, particle swarm optimization (PSO), production optimization

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Authors: Yuzhi Cai

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In this paper, we discuss a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. Together with a combining forecasts technique, we then predict USD to GBP currency exchange rates. Combined forecasts contain all the information captured by the fitted QAR models at different quantile levels and are therefore better than those obtained from individual models. Our results show that an unequally weighted combining method performs better than other forecasting methodology. We found that a median AR model can perform well in point forecasting when the predictive density functions are symmetric. However, in practice, using the median AR model alone may involve the loss of information about the data captured by other QAR models. We recommend that combined forecasts should be used whenever possible.

Keywords: combining forecasts, MCMC, predictive density functions, quantile forecasting, quantile modelling

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Authors: Hafida Merzouk

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Urinary tract infections are among the most serious public health issues in all age groups. Thus, the empirical therapy should based on local levels of resistance, as indicated in several studies from different countries, to effectively avoid the emergence of multidrug-resistant bacterial strains and recurrent infections. Numerous effective antibiotic treatments are available, but wouldbe ineffective for treating recurrent cystitis caused by a urinary tract infection, as well as the emergence of drug resistance. That iswhy the aim of this study was to highlight the antibacterial and the antioxidant activity of 11 medicinal plants used traditionally in Algeria against E. coli, the most responsible urinary tract infections. First, the extraction of total polyphenols with aqueous acetone showed variable yields. The highest yield was obtained by Asplenium trichomanes with 27%, followed by Petroselinum crispum and Ciannamomum cassia with an equal yield of 21%. Artemisia herba-alba gave the lowest yield (9%). The extracts of different plants showed variable contents of phenolic compounds. Reducing power and DPPH (2,2-diphenyl-1-picrylhydrazyl) scavenging activity revealed that most of the extracts studied had significant activity. The anti-free radical activity was very high in the extract of A splenium adiantum-nigrum compared with the other extracts studied, but Petroselinum crispum and Parietaria officinalis had the lowest reducing activity; Antibacterial activity was determined on E. coli strainsusing the diffusion, MICs (Minimum Inhibitory Concentrations) and MBCs (Minimum Bactericidal concentrations) methods. The strains tested were sensitive to most extracts studied, except Asplenium adiantum-nigrum extract, for which both strains showed resistance.

Keywords: E. coli, medicinal plants, phenolic compounds, urinary infections

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Authors: Fitim Sadiku, Mjellma Rexhepi, Kreshnik Grezda, Jonijana Sadiku Tigani, Merita Qorolli, Blerta Rexhepi-Kelmendi, Zelie Sadiku, Laura Cakolli

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Background: According to the European Alliance of Associations for Rheumatology (EULAR), biologics should be considered alongside traditional treatments in Ankylosing Spondylitis (AS) patients with persistently high disease activity that directly affects functional capacity. Unfortunately, Kosovo’s health system only offers continuous treatment with etanercept (ETN), and most of the patients with AS are referred to be treated with this biological substance. Objectives: This study aims to explore the relationship between comorbidity, disease activity, and functional capacity in AS patients undergoing ETN treatment in Kosovo. Methods: In this cross-sectional study, we included patients diagnosed with AS who were being treated with ETN 50mg per week for at least 6 months at the Rheumatology Clinic of the University Clinical Center of Kosovo. Patients under 18, pregnant women, and patients with spinal fractures were excluded. This study was approved by the Ethics Committee of the Faculty of Medicine, University of Prishtina and a consent form was signed by patients for participating in the study. We collected data (September-December 2023) about socio-demographics and disease history. Moreover, the presence of comorbidities was measured by the Comorbidity Charlson Index; the disease activity was measured by the Ankylosing Spondylitis Disease Activity Score (ASDAS), and the functional capacity was measured by the Bath Ankylosing Spondylitis Functional Index (BASFI). Results: A total of 31 out of the 39 patients with AS receiving etanercept were included aged 18 to above 65 years (M= 40 years, SD= 14.39), and 87% were male. Diagnose delay was, on average, 7 years from the first symptoms (min-max= 0-24), while the disease duration on average was 7.5 years (min-max= 1- 50). Treatment duration with etanercept was from 0.5 to 6 years. The results indicate a significant positive correlation between comorbidity and BASFI (r= .615, p= .01) and disease activity. Additionally, a significant positive correlation exists between disease activity and BASFI (r= .507, p= .004). Regression analysis highlights the significance of both comorbidity and disease activity as predictors of patients’ functional capacity F (1, 29) = 10.047, p= .05 and F(1, 29) = 17.678, p= .01. No notable gender differences were observed. The study found no significant variations in comorbidity, disease activity, and functional capacity concerning the duration of ETN treatment. Conclusion: We found that in Kosovo, it takes at least 7 years for individuals to be diagnosed with AS from the first-time symptoms are experienced. This study showed that there is a positive correlation between comorbidity and functional capacity, disease activity and functional capacity in patients with AS undergoing etanercept treatment. Furthermore, results showed that comorbidity and disease activity are predictors of the functional status of the patients with AS receiving ETN. Gender and treatment duration with etanercept did not show any significant variations in these patients.

Keywords: ankylosing spondilitis, etanercept, physical wellbeing, comorbidities

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Authors: Rajneekant Bachan, Dhanush Vijay

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The visualization of operating systems has refined the Turing machine, and current trends suggest that the emulation of 32 bit architectures will soon emerge. After years of technical research into Web services, we demonstrate the synthesis of gigabit switches, which embodies the robust principles of theory. Loam, our new algorithm for forward-error correction, is the solution to all of these challenges.

Keywords: software engineering, architectures, introspective models, operating systems

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Authors: Gunilla Dahlgren, Per Liv, Fredrik Öhberg, Lisbeth Slunga Järvholm, Mikael Forsman, Börje Rehn

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Background: Accuracy of risk assessment tools in hand-repetitive work is important. This can support precision in the risk management process and for a sustainable working life for women and men equally. Musculoskeletal disorders, MSDs, from the hand, wrist, and forearm, are common in the working population. Women report a higher prevalence of MSDs in these regions. Objective: The objective of this study was to compare if women and men who performed the identical hand-intensive work task were rated equally using the Hand Activity Threshold Limit Value® (HA-TLV) when self-rated and observer-rated. Method: Fifty-six workers from eight companies participated, with various intensities in hand-repetitive work tasks. In total, 18 unique identical hand-intensive work tasks were executed in 28 pairs of a woman and a man. Hand activity and force levels were assessed. Each worker executed the work task for 15 minutes, which was also video recorded. Data was collected on workers who self-rated directly after the execution of the work task. Also, experienced observers performed ratings from videos of the same work tasks. For comparing means between women and men, paired samples t-tests were used. Results: The main results showed that there was no difference in self-ratings of hand activity level and force by women and men who executed the same work task. Further, there was no difference between observer ratings of hand activity level. However, the observer force ratings of women and men differed significantly (p=0.01). Conclusion: Hand activity and force levels are rated equally in women and men when self-rated, also by observers for hand activity. However, it is an observandum that observer force rating is rated higher for women and lower for men. This indicates the need of comparing force ratings with technical measures.

Keywords: gender, equity, sex differences, repetitive strain injury, cumulative trauma disorders, upper extremity, exposure assessment, workload, health risk assessment, observation, psychophysics

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: Jamila Fliou, Federica Spinola, Ouassima Riffi, Asmaa Zriouel, Ali Amechrouq, Luca Nalbone, Alessandro Giuffrida, Filippo Giarratana

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This study performed a preliminary evaluation of the phytochemical composition and in vitro antibacterial activity of ethanolic extracts of Lavandula x intermedia leaves and flowers collected in the Fez-Meknes region of Morocco. Phytochemical analyses comprised qualitative colourimetric determinations of alkaloids, anthraquinones, and terpenes and quantitative analysis of total polyphenols, flavonoids, and condensed tannins by UV spectrophotometer. Antibacterial activity was evaluated by determining minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values against different ATCC bacterial strains. The phytochemical analysis showed a high amount of total polyphenols, flavonoids, and tannins in the leaf extract and a higher amount of terpenes based on colourimetric reaction than the flower extract. A positive colourimetric reaction for alkaloids and anthraquinones was detected for both extracts. The antibacterial activity of leaves and flower extract was not different against Gram-positive and Gram-negative strains (p<0.05). The results of the present study suggest the possible use of ethanolic extracts of L. x intermedia collected in the Fez-Meknes region of Morocco as a natural agent against bacterial pathogens.

Keywords: antimicrobial activity, Lavandula spp., lavender, lavandin, UV spectrophotometric analysis

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Authors: Vandana Gawande, Chandani Satija

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Gnidia glauca is a semi-woody herb of thymelaeaceae family traditionally used as fish poison in India. It is also found in Sri lanka and Africa. In the present study, potential anticancer effect of n-hexane and ethanolic extracts of Gnidia glauca in human breast cancer cells was investigated. Human breast cancer cells (MCF-7) were cultured as monolayers in RPMI 1640 medium. The cells were cultured for 48 hours to allow growth and achieve about 80% confluence in 96-well culture plates. The cells were treated with various concentrations of Gnidia glauca (0.1-100 mg/mL) for 72 hours. Percentage of viable cells after treatment was assessed using a sulforhodamine B colorimetric assay. Both n-hexane and ethanolic extract showed significant cytotoxic activity on MCF-7 cancer cells. This study supports the notion of using Gnidia glauca as a novel anticancer agent for breast cancer.

Keywords: 96 well plate, anticancer activity, Gnidia glauca, MCF-7

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Authors: Jefri Draup, Antoine Ambard, Chi-Toan Nguyen

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Zirconium alloys exhibit solid-state phase transformations under thermal loading. These can lead to a significant evolution of the microstructure and associated mechanical properties of materials used in nuclear fuel cladding structures. Therefore, the ability to capture effects of phase transformation on the material constitutive behavior is of interest during conditions of severe transient thermal loading. Whilst typical Avrami, or Johnson-Mehl-Avrami-Kolmogorov (JMAK), type models for phase transformations have been shown to have a good correlation with the behavior of Zircaloy-4 under constant heating rates, the effects of variable and fast heating rates are not fully explored. The present study utilises the results of in-situ high energy synchrotron X-ray diffraction (SXRD) measurements in order to validate the phase transformation models for Zircaloy-4 under fast variable heating rates. These models are used to assess the performance of fuel cladding structures under loss of coolant accident (LOCA) scenarios. The results indicate that simple Avrami type models can provide a reasonable indication of the phase distribution in experimental test specimens under variable fast thermal loading. However, the accuracy of these models deteriorates under the faster heating regimes, i.e., 100Cs⁻¹. The studies highlight areas for improvement of simple Avrami type models, such as the inclusion of temperature rate dependence of the JMAK n-exponent.

Keywords: accident, fuel, modelling, zirconium

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Authors: Lena Emamjomeh, Sohrab Imani

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Nowadays, the use of encapsulation technology of pesticides causes an increase in the efficiency and controlled release of these substances. Controlled release by nanoencapsulated formulations allows the essential oil to be used more effectively over a given time interval, suitability to the mode of application and minimization of environmental damage. The essential oil from Eucalyptus globulus exhibited an average yield of 1.19% and presented 1,8-cineol (59.08%) as the major component. Nanoemulsion essential oil was carried out by the method of gum - maltodextrin using homogenization and morphology and size were determined by TEM. Several concentrations were prepared, and then third instar larvae of E.kuehniella were introduced into each treatment. Then, repellent activity was determined after 1, 3 and 24 h from commencement. This study reveals that at a concentration of 1.5 ppm, the nanoemulsion of E. globulus essential oil on the flour disc was shown here to possess more repellent activity (85%) than E.kuehniella compared to natural essential oil (5%) before formulation after 24h. The repellent activity varied with application method concentrations and exposure time. The results showed higher repellent rates in nanoemulsion than in essential oil due to controlled-release formulations allowing smaller quantities of essential oil to be used more effectively over a given time interval. Findings led to the conclusion that encapsulated technology of essential oils can enhance their control release and persistence under controlled conditions.

Keywords: nanoemulsion, eucalyptus globulus, ephestia kuehniella, TEM

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Authors: Marjeta Misigoj Durakovic, Maroje Soric, Lovro Stefan

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Background and aim: Physical inactivity has been linked with increased morbidity and premature mortality and adolescence has been recognised as the critical period for a decline in physical activity (PA) level. In order to properly direct interventions aimed at increasing PA, high-risk groups of individuals should be identified. Therefore, the aim of this study is to describe gender differences in: a) PA level; b) weekly PA patterns. Methods: This investigation is a part of the CRO-PALS study which is an on-going longitudinal study conducted in a representative sample of urban youth in Zagreb (Croatia). CRO-PALS involves 903 adolescents and for the purpose of this study data from a subgroup of 190 participants with information on objective PA level were analysed (116 girls; mean age [SD]=15.6[0.3] years). Duration of moderate and vigorous PA was measured during 5 consecutive by a multiple-sensor physical activity monitor (SenseWear Armband, BodyMedia inc., Pittsburgh, USA). Gender differences in PA level were evaluated using independent samples t-test. Differences in school week and weekend levels of activity were assessed using mixed ANOVA with gender as between-subjects factor. The amount of vigorous PA had to be log-transformed to achieve normality in the distribution. Results: Boys were more active than girls. Duration of moderate-to-vigorous PA averaged 111±44 min/day in boys and 80±38 min/day in girls (mean difference=31 min/day, 95%CI=20-43 min/day). Vigorous PA was 2.5 times higher in boys compared to girls (95%CI=1.9-3.5). Participants were more active during school days than on weekends. The magnitude of the difference in moderate-to-vigorous PA was similar in both gender (p value for time*gender interaction = 0.79) and averaged 19 min/day (95%CI=11-27 min/day). Similarly, vigorous PA was 36% lower on weekends compared with school days (95%CI=22-46%) with no gender difference (p value for time*gender interaction = 0.52). Conclusion: PA level was higher in boys than in girls throughout the week. Still, in both boys and girls, the amount of PA reduced markedly on weekends compared with school days.

Keywords: adolescence, multiple-sensor physical activity monitor, physical activity level, weekly physical activity pattern

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Authors: Yong Bum Shin

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This case focuses on Weighted additive difference, Conjunctive, Disjunctive, and Elimination by aspects methodologies in consumer decision-making models and the Simple additive weighting (SAW) approach in the multi-criteria decision-making (MCDM) area. Most decision-making models illustrate that the rank reversal phenomenon is unpreventable. This paper presents that rank reversal occurs in popular managerial methods such as Weighted Additive Difference (WAD), Conjunctive Method, Disjunctive Method, Elimination by Aspects (EBA) and MCDM methods as well as such as the Simple Additive Weighting (SAW) and finally Unified Commensurate Multiple (UCM) models which successfully addresses these rank reversal problems in most popular MCDM methods in decision-making area.

Keywords: multiple criteria decision making, rank inconsistency, unified commensurate multiple, analytic hierarchy process

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Authors: Maryam Maleki, Esther Mead, Mohammad Arani, Nitin Agarwal

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This study is conducted to examine how opposing viewpoints related to COVID-19 were diffused on Twitter. To accomplish this, six datasets using two epidemiological models, SIR (Susceptible, Infected, Recovered) and SEIZ (Susceptible, Exposed, Infected, Skeptics), were analyzed. The six datasets were chosen because they represent opposing viewpoints on the COVID-19 pandemic. Three of the datasets contain anti-subject hashtags, while the other three contain pro-subject hashtags. The time frame for all datasets is three years, starting from January 2020 to December 2022. The findings revealed that while both models were effective in evaluating the propagation trends of these polarizing viewpoints, the SEIZ model was more accurate with a relatively lower error rate (6.7%) compared to the SIR model (17.3%). Additionally, the relative error for both models was lower for anti-subject hashtags compared to pro-subject hashtags. By leveraging epidemiological models, insights into the propagation trends of polarizing viewpoints on Twitter were gained. This study paves the way for the development of methods to prevent the spread of ideas that lack scientific evidence while promoting the dissemination of scientifically backed ideas.

Keywords: mathematical modeling, epidemiological model, seiz model, sir model, covid-19, twitter, social network analysis, social contagion

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Authors: Jiabei Zhang, Ying Qi

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The purpose of this study was to develop a descriptive profile of the adapted physical activity research using single subject experimental designs. All research articles using single subject experimental designs published in the journal of Adapted Physical Activity Quarterly from 1984 to 2013 were employed as the data source. Each of the articles was coded in a subcategory of seven categories: (a) the size of sample; (b) the age of participants; (c) the type of disabilities; (d) the type of data analysis; (e) the type of designs, (f) the independent variable, and (g) the dependent variable. Frequencies, percentages, and trend inspection were used to analyze the data and develop a profile. The profile developed characterizes a small portion of research articles used single subject designs, in which most researchers used a small sample size, recruited children as subjects, emphasized learning and behavior impairments, selected visual inspection with descriptive statistics, preferred a multiple baseline design, focused on effects of therapy, inclusion, and strategy, and measured desired behaviors more often, with a decreasing trend over years.

Keywords: adapted physical activity research, single subject experimental designs, physical education, sport science

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Authors: Ramona Zharfpeykan, Paul Rouse

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Organizational sustainability is important to both organizations themselves and their stakeholders. Despite its increasing popularity and increasing numbers of organizations reporting sustainability, research on evaluating and comparing the sustainability performance of companies is limited. The aim of this study was to develop models to measure sustainability performance for both cross-sectional and longitudinal comparisons across companies in the same or different industries. A secondary aim was to see if sustainability reports can be used to evaluate sustainability performance. The study used both a content analysis of Australian sustainability reports in mining and metals and financial services for 2011-2014 and a survey of Australian and New Zealand organizations. Two methods ranging from a composite index using uniform weights to data envelopment analysis (DEA) were employed to analyze the data and develop the models. The results show strong statistically significant relationships between the developed models, which suggests that each model provides a consistent, systematic and reasonably robust analysis. The results of the models show that for both industries, companies that had sustainability scores above or below the industry average stayed almost the same during the study period. These indices and models can be used by companies to evaluate their sustainability performance and compare it with previous years, or with other companies in the same or different industries. These methods can also be used by various stakeholders and sustainability ranking companies such as the Global Reporting Initiative (GRI).

Keywords: data envelopment analysis, sustainability, sustainability performance measurement system, sustainability performance index, global reporting initiative

Procedia PDF Downloads 159