Search results for: Frequency tuning range

Authors: B. Gonzalez-Yebra, H. Barajas, P. Palomares, M. Hernandez, O. Torres, M. Ayala, A. L. González, G. Vazquez-Ortiz, M. L. Guzman

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Leukemia arises by molecular alterations of the normal hematopoietic stem cell (HSC) transforming it into a leukemic stem cell (LSC) with high cell proliferation, self-renewal, and cell differentiation. Chronic myeloid leukemia (CML) originates from an LSC-leading to elevated proliferation of myeloid cells and acute lymphoblastic leukemia (ALL) originates from an LSC development leading to elevated proliferation of lymphoid cells. In both cases, LSC can be identified by multicolor flow cytometry using several antibodies. However, to date, LSC levels in peripheral blood (PB) are not established well enough in ALL and CML patients. On the other hand, the detection of the minimal residue disease (MRD) in leukemia is mainly based on the identification of the mRNA bcr-abl gene in CML patients and some other genes in ALL patients. There is no a properly biomarker to detect MDR in both types of leukemia. The objective of this study was to determine mRNA bcr-abl and the percentage of LSC in peripheral blood of patients with CML and ALL and identify a possible association between the amount of LSC in PB and clinical data. We included in this study 19 patients with Leukemia. A PB sample was collected per patient and leukocytes were obtained by Ficoll gradient. The immunophenotype for LSC CD34+ was done by flow cytometry analysis with CD33, CD2, CD14, CD16, CD64, HLA-DR, CD13, CD15, CD19, CD10, CD20, CD34, CD38, CD71, CD90, CD117, CD123 monoclonal antibodies. In addition, to identify the presence of the mRNA bcr-abl by RT-PCR, the RNA was isolated using TRIZOL reagent. Molecular (presence of mRNA bcr-abl and LSC CD34+) and clinical results were analyzed with descriptive statistics and a multiple regression analysis was performed to determine statistically significant association. In total, 19 patients (8 patients with ALL and 11 patients with CML) were analyzed, 9 patients with de novo leukemia (ALL = 6 and CML = 3) and 10 under treatment (ALL = 5 and CML = 5). The overall frequency of mRNA bcr-abl was 31% (6/19), and it was negative in ALL patients and positive in 80% in CML patients. On the other hand, LSC was determined in 16/19 leukemia patients (%LSC= 0.02-17.3). The Novo patients had higher percentage of LSC (0.26 to 17.3%) than patients under treatment (0 to 5.93%). The amount of LSC was significantly associated with the amount of LSC were: absence of treatment, the absence of splenomegaly, and a lower number of leukocytes, negative association for the clinical variables age, sex, blasts, and mRNA bcr-abl. In conclusion, patients with de novo leukemia had a higher percentage of circulating LSC than patients under treatment, and it was associated with clinical parameters as lack of treatment, absence of splenomegaly and a lower number of leukocytes. The mRNA bcr-abl detection was only possible in the series of patients with CML, and molecular detection of LSC could be identified in the peripheral blood of all leukemia patients, we believe the identification of circulating LSC may be used as biomarker for the detection of the MRD in leukemia patients.

Keywords: stem cells, leukemia, biomarkers, flow cytometry

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Authors: Ahmad B. Habieb, Gabriele Milani, Tavio Tavio, Federico Milani

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Housings in developing countries have often inadequate seismic protection, particularly for masonry. People choose this type of structure since the cost and application are relatively cheap. Seismic protection of masonry remains an interesting issue among researchers. In this study, we develop a low-cost seismic isolation system for masonry using fiber reinforced elastomeric isolators. The elastomer proposed consists of few layers of rubber pads and fiber lamina, making it lower in cost comparing to the conventional isolators. We present a finite element (FE) analysis to predict the behavior of the low cost rubber isolators undergoing moderate deformations. The FE model of the elastomer involves a hyperelastic material property for the rubber pad. We adopt a Yeoh hyperelasticity model and estimate its coefficients through the available experimental data. Having the shear behavior of the elastomers, we apply that isolation system onto small masonry housing. To attach the isolators on the building, we model the shear behavior of the isolation system by means of a damped nonlinear spring model. By this attempt, the FE analysis becomes computationally inexpensive. Several ground motion data are applied to observe its sensitivity. Roof acceleration and tensile damage of walls become the parameters to evaluate the performance of the isolators. In this study, a concrete damage plasticity model is used to model masonry in the nonlinear range. This tool is available in the standard package of Abaqus FE software. Finally, the results show that the low-cost isolators proposed are capable of reducing roof acceleration and damage level of masonry housing. Through this study, we are also capable of monitoring the shear deformation of isolators during seismic motion. It is useful to determine whether the isolator is applicable. According to the results, the deformations of isolators on the benchmark one story building are relatively small.

Keywords: masonry, low cost elastomeric isolator, finite element analysis, hyperelasticity, damped non-linear spring, concrete damage plasticity

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Authors: Ozan Kahraman, Hao Feng

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Ultrasonic atomization (UA) is a useful technique for producing a liquid spray for various processes, such as spray drying. Ultrasound generates small droplets (a few microns in diameter) by disintegration of the liquid via cavitation and/or capillary waves, with low range velocity and narrow droplet size distribution. In recent years, UA has been investigated as an alternative for enabling or enhancing ultrasound-mediated unit operations, such as evaporation, separation, and purification. The previous studies on the UA separation of a solvent from a bulk solution were limited to ethanol-water systems. More investigations into ultrasound-mediated separation for other liquid systems are needed to elucidate the separation mechanism. This study was undertaken to investigate the effects of the operational parameters on the ultrasound-mediated separation of three miscible liquid pairs: ethanol-, methanol-, and butanol-water. A 2.4 MHz ultrasonic mister with a diameter of 18 mm and rating power of 24 W was installed on the bottom of a custom-designed cylindrical separation unit. Air was supplied to the unit (3 to 4 L/min.) as a carrier gas to collect the mist. The effects of the initial alcohol concentration, viscosity, and temperature (10, 30 and 50°C) on the atomization rates were evaluated. The alcohol concentration in the collected mist was measured with high performance liquid chromatography and a refractometer. The viscosity of the solutions was determined using a Brookfield digital viscometer. The alcohol concentration of the atomized mist was dependent on the feed concentration, feed rate, viscosity, and temperature. Increasing the temperature of the alcohol-water mixtures from 10 to 50°C increased the vapor pressure of both the alcohols and water, resulting in an increase in the atomization rates but a decrease in the separation efficiency. The alcohol concentration in the mist was higher than that of the alcohol-water equilibrium at all three temperatures. More importantly, for ethanol, the ethanol concentration in the mist went beyond the azeotropic point, which cannot be achieved by conventional distillation. Ultrasound-mediated separation is a promising non-equilibrium method for separating and purifying alcohols, which may result in significant energy reductions and process intensification.

Keywords: azeotropic mixtures, distillation, evaporation, purification, seperation, ultrasonic atomization

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Authors: Vânia Costa, Nuno De Sá Teixeira, Ana C. Santos, Eduardo Santos

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Financial literacy and numeracy have been regarded as paramount for rational household decision making in the increasing complexity of financial markets. However, financial decisions are often made under sub-optimal circumstances, including cognitive overload. The present study aims to clarify how financial literacy and numeracy, taken as relevant expert knowledge for financial decision-making, modulate possible effects of cognitive load. Participants were required to perform a choice between a sure loss or a gambling pertaining a financial investment, either with or without a competing memory task. Two experiments were conducted varying only the content of the competing task. In the first, the financial choice task was made while maintaining on working memory a list of five random letters. In the second, cognitive load was based upon the retention of six random digits. In both experiments, one of the items in the list had to be recalled given its serial position. Outcomes of the first experiment revealed no significant main effect or interactions involving cognitive load manipulation and numeracy and financial literacy skills, strongly suggesting that retaining a list of random letters did not interfere with the cognitive abilities required for financial decision making. Conversely, and in the second experiment, a significant interaction between the competing mnesic task and level of financial literacy (but not numeracy) was found for the frequency of choice of a gambling option. Overall, and in the control condition, both participants with high financial literacy and high numeracy were more prone to choose the gambling option. However, and when under cognitive load, participants with high financial literacy were as likely as their illiterate counterparts to choose the gambling option. This outcome is interpreted as evidence that financial literacy prevents intuitive risk-aversion reasoning only under highly favourable conditions, as is the case when no other task is competing for cognitive resources. In contrast, participants with higher levels of numeracy were consistently more prone to choose the gambling option in both experimental conditions. These results are discussed in the light of the opposition between classical dual-process theories and fuzzy-trace theories for intuitive decision making, suggesting that while some instances of expertise (as numeracy) are prone to support easily accessible gist representations, other expert skills (as financial literacy) depend upon deliberative processes. It is furthermore suggested that this dissociation between types of expert knowledge might depend on the degree to which they are generalizable across disparate settings. Finally, applied implications of the present study are discussed with a focus on how it informs financial regulators and the importance and limits of promoting financial literacy and general numeracy.

Keywords: decision making, cognitive load, financial literacy, numeracy

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Authors: Maryam Sayadi, Rana Sedighpour, Hossein Rezaie

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In this study, in order to evaluate water quality of Gamasiab and Gharehsoo rivers located in Kermanshah province, the information of a 5-year statistical period during the years 2014-2018 was used. To evaluate the hydrochemistry of water, first the type and hydrogeochemical facies of river water were determined using Stiff and Piper diagrams. Then, based on Gibbs diagram and combination diagrams, the factors controlling the chemical parameters of the two rivers were identified. Saturation indices were used to predict the possibility of dissolution and deposition of some minerals. Then, in order to classify water in different sections, fourteen water quality indicators for different uses along with WHO standard were used. Finally, factor analysis was used to determine the processes affecting the hydrochemistry of the two rivers. The results of this study showed that in both rivers, the predominant type and facies are bicarbonate of calcite. Also, the main factor in changing the chemical quality of water in both Gamasiab and Gharehsoo rivers is the water-rock reaction. According to the results of factor analysis in both rivers, two factors have the greatest impact on water quality in the region. Among the parameters of Gamasiab river in the first factor, HCO3-, Na+ and Cl-, respectively, had the highest factor loads, and in the second factor, SO42- and Mg2+ were selected as the main parameters. The parameters Ca2+, Cl- and Na have the highest factor loads in the first factor and in the second factor Mg2+ and SO42- have the highest factor loads in Gharehsoo river. The dissolution of carbonate formations due to their abundance and expansion in the two basins has a more significant effect on changing water chemistry. It has saturated the water of rivers with aragonite, calcite and dolomite. Due to the low contribution of the second factor in changing the chemical parameters, the water of both rivers is saturated with respect to evaporative minerals such as gypsum, halite and anhydrite in all stations. Based on Schoeller diagrams, Wilcox and other quality indicators in these two sections, the amount of main physicochemical parameters are in the desired range for drinking and agriculture. The results of Langelier, Ryznar, Larson-Skold and Puckorius indices showed that water is corrosive in industry.

Keywords: factor analysis, hydrochemical, saturation index, surface water quality

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Authors: David Muyise

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Mixed Integer Programming (MIP) is an approach that involves the optimization of a range of decision variables in order to minimize or maximize a particular objective function. The main objective of this study was to apply the MIP approach to optimize the deployment of waste plastic to fuel oil processing plants in Uganda. The processing plants are meant to reduce plastic pollution by pyrolyzing the waste plastic into a cleaner fuel that can be used to power diesel/paraffin engines, so as (1) to reduce the negative environmental impacts associated with plastic pollution and also (2) to curb down the energy gap by utilizing the fuel oil. A programming model was established and tested in two case study applications that are, small-scale applications in rural towns and large-scale deployment across major cities in the country. In order to design the supply chain, optimal decisions on the types of waste plastic to be processed, size, location and number of plants, and downstream fuel applications were concurrently made based on the payback period, investor requirements for capital cost and production cost of fuel and electricity. The model comprises qualitative data gathered from waste plastic pickers at landfills and potential investors, and quantitative data obtained from primary research. It was found out from the study that a distributed system is suitable for small rural towns, whereas a decentralized system is only suitable for big cities. Small towns of Kalagi, Mukono, Ishaka, and Jinja were found to be the ideal locations for the deployment of distributed processing systems, whereas Kampala, Mbarara, and Gulu cities were found to be the ideal locations initially utilize the decentralized pyrolysis technology system. We conclude that the model findings will be most important to investors, engineers, plant developers, and municipalities interested in waste plastic to fuel processing in Uganda and elsewhere in developing economy.

Keywords: mixed integer programming, fuel oil plants, optimisation of waste plastics, plastic pollution, pyrolyzing

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Akindele Ojo, Olanike Ojo, Agatha Oseghale

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The empirical study examined the allocative efficiency of small holder tuber crop farmers in North central, Nigeria. Data used for the study were obtained from primary source using a multi-stage sampling technique with structured questionnaires administered to 300 randomly selected tuber crop farmers from the study area. Descriptive statistics, data envelopment analysis and Tobit regression model were used to analyze the data. The DEA result on the classification of the farmers into efficient and inefficient farmers showed that 17.67% of the sampled tuber crop farmers in the study area were operating at frontier and optimum level of production with mean allocative efficiency of 1.00. This shows that 82.33% of the farmers in the study area can still improve on their level of efficiency through better utilization of available resources, given the current state of technology. The results of the Tobit model for factors influencing allocative inefficiency in the study area showed that as the year of farming experience, level of education, cooperative society membership, extension contacts, credit access and farm size increased in the study area, the allocative inefficiency of the farmers decreased. The results on effects of the significant determinants of allocative inefficiency at various distribution levels revealed that allocative efficiency increased from 22% to 34% as the farmer acquired more farming experience. The allocative efficiency index of farmers that belonged to cooperative society was 0.23 while their counterparts without cooperative society had index value of 0.21. The result also showed that allocative efficiency increased from 0.43 as farmer acquired high formal education and decreased to 0.16 with farmers with non-formal education. The efficiency level in the allocation of resources increased with more contact with extension services as the allocative efficeincy index increased from 0.16 to 0.31 with frequency of extension contact increasing from zero contact to maximum of twenty contacts per annum. These results confirm that increase in year of farming experience, level of education, cooperative society membership, extension contacts, credit access and farm size leads to increases efficiency. The results further show that the age of the farmers had 32% input to the efficiency but reduces to an average of 15%, as the farmer grows old. It is therefore recommended that enhanced research, extension delivery and farm advisory services should be put in place for farmers who did not attain optimum frontier level to learn how to attain the remaining 74.39% level of allocative efficiency through a better production practices from the robustly efficient farms. This will go a long way to increase the efficiency level of the farmers in the study area.

Keywords: allocative efficiency, DEA, Tobit regression, tuber crop

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Authors: Michael Nolte, Iwona Kasior, Kala Flagg, Spiro Karavatas

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Background: Cryotherapy has been used as a post-exercise recovery modality for decades. Whole-body cryotherapy (WBC) is an intervention which involves brief exposures to extremely cold air in order to induce therapeutic effects. It is currently being investigated for its effectiveness in treating certain exercise-induced impairments. Purpose: The purpose of this systematic review was to determine whether WBC as a recovery intervention is more, less, or equally as effective as other interventions at reducing perceived levels of muscle soreness and promoting recovery of muscle function after exercise-induced muscle damage (EIMD) from running. Methods: A systematic review of the current literature was performed utilizing the following MeSH terms: cryotherapy, whole-body cryotherapy, exercise-induced muscle damage, muscle soreness, muscle recovery, and running. The databases utilized were PubMed, CINAHL, EBSCO Host, and Google Scholar. Articles were included if they were published within the last ten years, had a CEBM level of evidence of IIb or higher, had a PEDro scale score of 5 or higher, studied runners as primary subjects, and utilized both perceived levels of muscle soreness and recovery of muscle function as dependent variables. Articles were excluded if subjects did not include runners, if the interventions included PBC instead of WBC, and if both muscle performance and perceived muscle soreness were not assessed within the study. Results: Two of the four articles revealed that WBC was significantly more effective than treatment interventions such as far-infrared radiation and passive recovery at reducing perceived levels of muscle soreness and restoring muscle power and endurance following simulated trail runs and high-intensity interval running, respectively. One of the four articles revealed no significant difference between WBC and passive recovery in terms of reducing perceived muscle soreness and restoring muscle power following sprint intervals. One of the four articles revealed that WBC had a harmful effect compared to CWI and passive recovery on both perceived muscle soreness and recovery of muscle strength and power following a marathon. Discussion/Conclusion: Though there was no consensus in terms of WBC’s effectiveness at treating exercise-induced muscle damage following running compared to other interventions, it seems as though WBC may at least have a time-dependent positive effect on muscle soreness and recovery following high-intensity interval runs and endurance running, marathons excluded. More research needs to be conducted in order to determine the most effective way to implement WBC as a recovery method for exercise-induced muscle damage, including the optimal temperature, timing, duration, and frequency of treatment.

Keywords: cryotherapy, physical therapy intervention, physical therapy, whole body cryotherapy

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Authors: Elizabeth Crafton, Donald Ott, Teresa Cutright

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Harmful algal blooms (HABs), more notably cyanobacteria-dominated HABs, compromise water quality, jeopardize access to drinking water and are a risk to public health and safety. HABs are representative of ecosystem imbalance largely caused by environmental changes, such as eutrophication, that are associated with the globally expanding human population. Cyanobacteria-dominated HABs are anticipated to increase in frequency, magnitude, and are predicted to plague a larger geographical area as a result of climate change. The weather pattern is important as storm-driven, pulse-input of nutrients have been correlated to cyanobacteria-dominated HABs. The mobilization of aqueous and particulate nutrients and the response of the phytoplankton community is an important relationship in this complex phenomenon. This relationship is most apparent in high-impact areas of adequate sunlight, > 20ᵒC, excessive nutrients and quiescent water that corresponds to ideal growth of HABs. Typically the impact of particulate phosphorus is dismissed as an insignificant contribution; which is true for areas that are not considered high-impact. The objective of this study was to assess the impact of a simulated storm-driven, pulse-input of reactive phosphorus and the response of three different cyanobacteria assemblages (~5,000 cells/mL). The aqueous and particulate sources of phosphorus and changes in HAB were tracked weekly for 4 weeks. The first cyanobacteria composition consisted of Planktothrix sp., Microcystis sp., Aphanizomenon sp., and Anabaena sp., with 70% of the total population being non-diazotrophic and 30% being diazotrophic. The second was comprised of Anabaena sp., Planktothrix sp., and Microcystis sp., with 87% diazotrophic and 13% non-diazotrophic. The third composition has yet to be determined as these experiments are ongoing. Preliminary results suggest that both aqueous and particulate sources are contributors of total reactive phosphorus in high-impact areas. The results further highlight shifts in the cyanobacteria assemblage after the simulated pulse-input. In the controls, the reactors dosed with aqueous reactive phosphorus maintained a constant concentration for the duration of the experiment; whereas, the reactors that were dosed with aqueous reactive phosphorus and contained soil decreased from 1.73 mg/L to 0.25 mg/L of reactive phosphorus from time zero to 7 days; this was higher than the blank (0.11 mg/L). Suggesting a binding of aqueous reactive phosphorus to sediment, which is further supported by the positive correlation observed between total reactive phosphorus concentration and turbidity. The experiments are nearly completed and a full statistical analysis will be completed of the results prior to the conference.

Keywords: Anabaena, cyanobacteria, harmful algal blooms, Microcystis, phosphorous, response surface methodology

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Authors: Marianna Maiaru, Gregory M. Odegard, Josh Kemppainen, Ivan Gallegos, Michael Olaya

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Ceramic matrix composites (CMCs) are typically used in applications that require long-term mechanical integrity at elevated temperatures. CMCs are usually fabricated using a polymer precursor that is initially polymerized in situ with fiber reinforcement, followed by a series of cycles of pyrolysis to transform the polymer matrix into a rigid glass or ceramic. The pyrolysis step typically generates volatile gasses, which creates porosity within the polymer matrix phase of the composite. Subsequent cycles of monomer infusion, polymerization, and pyrolysis are often used to reduce the porosity and thus increase the durability of the composite. Because of the significant expense of such iterative processing cycles, new generations of CMCs with improved durability and manufacturability are difficult and expensive to develop using standard Edisonian approaches. The goal of this research is to develop a computational process-modeling-based approach that can be used to design the next generation of CMC materials with optimized material and processing parameters for maximum strength and efficient manufacturing. The process modeling incorporates computational modeling tools, including molecular dynamics (MD), to simulate the material at multiple length scales. Results from MD simulation are used to inform the continuum-level models to link molecular-level characteristics (material structure, temperature) to bulk-level performance (strength, residual stresses). Processing parameters are optimized such that process-induced residual stresses are minimized and laminate strength is maximized. The multiscale process modeling method developed with this research can play a key role in the development of future CMCs for high-temperature and high-strength applications. By combining multiscale computational tools and process modeling, new manufacturing parameters can be established for optimal fabrication and performance of CMCs for a wide range of applications.

Keywords: digital engineering, finite elements, manufacturing, molecular dynamics

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Authors: Neumoin Anton Ivanovich, Opra Denis Pavlovich

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Materials based on titanium oxide compounds are widely used in such areas as solar energy, photocatalysis, food industry and hygiene products, biomedical technologies, etc. Demand for them has also formed in the battery industry (an example of this is the commercialization of Li4Ti5O12), where much attention has recently been paid to the development of next-generation systems and technologies, such as sodium-ion batteries. This dictates the need to search for new materials with improved characteristics, as well as ways to obtain them that meet the requirements of scalability. One of the ways to solve these problems can be the creation of nanomaterials that often have a complex of physicochemical properties that radically differ from the characteristics of their counterparts in the micro- or macroscopic state. At the same time, it is important to control the texture (specific surface area, porosity) of such materials. In view of the above, among other methods, the hydrothermal technique seems to be suitable, allowing a wide range of control over the conditions of synthesis. In the present study, a method was developed for the preparation of mesoporous nanostructured sodium trititanate (Na2Ti3O7) with a hierarchical architecture. The materials were synthesized by hydrothermal processing and exhibit a complex hierarchically organized two-layer architecture. At the first level of the hierarchy, materials are represented by particles having a roughness surface, and at the second level, by one-dimensional nanotubes. The products were found to have high specific surface area and porosity with a narrow pore size distribution (about 6 nm). As it is known, the specific surface area and porosity are important characteristics of functional materials, which largely determine the possibilities and directions of their practical application. Electrochemical impedance spectroscopy data show that the resulting sodium trititanate has a sufficiently high electrical conductivity. As expected, the synthesized complexly organized nanoarchitecture based on sodium trititanate with a porous structure can be practically in demand, for example, in the field of new generation electrochemical storage and energy conversion devices.

Keywords: sodium trititanate, hierarchical materials, mesoporosity, nanotubes, hydrothermal synthesis

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Authors: Mohamed Salaheldien Mohamed Alayat, Ahmed Mohamed Elsoudany, Roaa Abdulghani Sroge, Bayan Muteb Aldhahwani

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Background: Neck pain is a common illness that could affect individual’s daily activities. Class IV laser with longer wavelength can stimulate tissues and penetrate more than the classic low-level laser therapy. Objectives: The aim of the study was to investigate the efficacy of class IV diode laser in the treatment of patients with chronic neck pain (CNP). Methods: Fifty-two patients participated and completed the study. Their mean age (SD) was 50.7 (6.2). Patients were randomized into two groups and treated with laser plus exercise (laser + EX) group and placebo laser plus exercise (PL+EX) group. Treatment was performed by Class IV laser in two phases; scanning and trigger point phases. Scanning to the posterior neck and shoulder girdle region with 4 J/cm2 with a total energy of 300 J applied to 75 cm2 in 4 minutes and 16 seconds. Eight trigger points on the posterior neck area were treated by 4 J/cm2 and the time of application was in 30 seconds. Both groups received exercise two times per week for 4 weeks. Exercises included range of motion, isometric, stretching, isotonic resisted exercises to the cervical extensors, lateral bending and rotators muscles with postural correction exercises. The measured variables were pain level using visual analogue scale (VAS), and neck functional activity using neck disability index (NDI) score. Measurements were taken at baseline and after 4 weeks of treatment. The level of statistical significance was set as p < 0.05. Results: There were significant decreases in post-treatment VAS and NDI in both groups as compared to baseline values. Laser + EX effectively decreased VAS (mean difference -6.5, p = 0.01) and NDI scores after (mean difference -41.3, p = 0.01) 4 weeks of treatment compared to PL + EX. Conclusion: Class IV laser combined with exercise is effective treatment for patients with CNP as compared to PL + EX therapy. The combination of laser + EX effectively increased functional activity and reduced pain after 4 weeks of treatment.

Keywords: chronic neck pain, class IV laser, exercises, neck disability index, visual analogue scale

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Authors: Benbiao Song, Yan Gao, Zhuo Liu

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Geostatistical modeling is the key technic for reservoir characterization, the quality of geological models will influence the prediction of reservoir performance greatly, but few studies have been done to quantify the factors impacting geostatistical reservoir modeling accuracy. In this study, 16 fluvial prototype models have been established to represent different geological complexity, 6 cases range from 16 to 361 wells were defined to reproduce all those 16 prototype models by different methodologies including SIS, object-based and MPFS algorithms accompany with different constraint parameters. Modeling accuracy ratio was defined to quantify the influence of each factor, and ten realizations were averaged to represent each accuracy ratio under the same modeling condition and parameters association. Totally 5760 simulations were done to quantify the relative contribution of each factor to the simulation accuracy, and the results can be used as strategy guide for facies modeling in the similar condition. It is founded that data density, geological trend and geological complexity have great impact on modeling accuracy. Modeling accuracy may up to 90% when channel sand width reaches up to 1.5 times of well space under whatever condition by SIS and MPFS methods. When well density is low, the contribution of geological trend may increase the modeling accuracy from 40% to 70%, while the use of proper variogram may have very limited contribution for SIS method. It can be implied that when well data are dense enough to cover simple geobodies, few efforts were needed to construct an acceptable model, when geobodies are complex with insufficient data group, it is better to construct a set of robust geological trend than rely on a reliable variogram function. For object-based method, the modeling accuracy does not increase obviously as SIS method by the increase of data density, but kept rational appearance when data density is low. MPFS methods have the similar trend with SIS method, but the use of proper geological trend accompany with rational variogram may have better modeling accuracy than MPFS method. It implies that the geological modeling strategy for a real reservoir case needs to be optimized by evaluation of dataset, geological complexity, geological constraint information and the modeling objective.

Keywords: fluvial facies, geostatistics, geological trend, modeling strategy, modeling accuracy, variogram

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Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

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Originated from graphite, graphene is a two-dimensional (2D) material that promises to revolutionize technology in many different areas, such as energy, telecommunications, civil construction, aviation, textile, and medicine. This is possible because its structure, formed by carbon bonds, provides desirable optical, thermal, and mechanical characteristics that are interesting to multiple areas of the market. Thus, several research and development centers are studying different manufacturing methods and material applications of graphene, which are often compromised by the scarcity of more agile and accurate methodologies to characterize the material – that is to determine its composition, shape, size, and the number of layers and crystals. To engage in this search, this study proposes a computational methodology that applies deep learning to identify graphene oxide crystals in order to characterize samples by crystal sizes. To achieve this, a fully convolutional neural network called U-net has been trained to segment SEM graphene oxide images. The segmentation generated by the U-net is fine-tuned with a standard deviation technique by classes, which allows crystals to be distinguished with different labels through an object delimitation algorithm. As a next step, the characteristics of the position, area, perimeter, and lateral measures of each detected crystal are extracted from the images. This information generates a database with the dimensions of the crystals that compose the samples. Finally, graphs are automatically created showing the frequency distributions by area size and perimeter of the crystals. This methodological process resulted in a high capacity of segmentation of graphene oxide crystals, presenting accuracy and F-score equal to 95% and 94%, respectively, over the test set. Such performance demonstrates a high generalization capacity of the method in crystal segmentation, since its performance considers significant changes in image extraction quality. The measurement of non-overlapping crystals presented an average error of 6% for the different measurement metrics, thus suggesting that the model provides a high-performance measurement for non-overlapping segmentations. For overlapping crystals, however, a limitation of the model was identified. To overcome this limitation, it is important to ensure that the samples to be analyzed are properly prepared. This will minimize crystal overlap in the SEM image acquisition and guarantee a lower error in the measurements without greater efforts for data handling. All in all, the method developed is a time optimizer with a high measurement value, considering that it is capable of measuring hundreds of graphene oxide crystals in seconds, saving weeks of manual work.

Keywords: characterization, graphene oxide, nanomaterials, U-net, deep learning

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Authors: Phill-Seung Jung, Dae-Yeon Kim

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Objectives: In young, especially nulliparous women, it is not easy to decide on excisional therapy for cervical intraepithelial neoplasia (CIN). We aimed to evaluate how effective topical imiquimod is in the treatment of high-grade CIN so that excisional therapy can be avoided in young women. Methods: Patients with CIN were allocated to this pilot study. They did not want excisional therapy and agreed with topical imiquimod therapy, which required once-a-week hospital visit for 8 weeks for the application of imiquimod to the cervix by a gynecologic oncologist. If the lesion got worse during treatment, it was decided to convert imiquimod therapy to excisional therapy. Results: A total of 36 patients with a median age of 29 years (range, 22–41 years) agreed to receive topical imiquimod therapy. Of these, 32 patients (88.9%) were positive for high-risk human papillomavirus (HR HPV). Twenty-five patients (69.4%) had low-grade squamous intraepithelial lesion (LSIL), and 11 (30.6%) had high-grade squamous intraepithelial lesion (HSIL) on their initial LBC. Twenty-eight patients underwent punch biopsy, which showed CIN 1 in 7 (19.4%), CIN 2 in 11 (30.6%), and CIN 3 in 10 (27.8%) patients. Twenty patients finished the 8-week imiquimod therapy. Among them, 14 patients had CIN 2 or 3, and 6 patients had CIN 1. HR HPV was positive in 12 patients. On the last examination, 14 patients (70.0%) had negative intraepithelial lesions, 3 (15.0%) had atypical squamous cells of undetermined significance, and 1 (5.0%) had LSIL. Two patients had persistent HSIL: 1 patient underwent loop electrosurgical excision procedure, resulting in CIN 3 with positive resection margin, and the other patient underwent punch biopsy, resulting in intermediate cells and restarted imiquimod therapy. Only 7 patients were negative for HR HPV. Conclusions: This study showed that topical imiquimod therapy was effective for the treatment of high-grade CIN, with a histologic regression rate of 85.7% (14/20) and HPV eradication rate of 25.0% (8/32). Based on our findings, topical imiquimod therapy might have a successful therapeutic effect in young women with CIN 2-3 so that they can avoid excisional therapy. In addition, it could be a more reassuring treatment option for CIN 1 than just follow-up after few months. To confirm its efficacy, a phase II study with larger cohort would be needed.

Keywords: Imiquimod, Cervical Intraepthelial Neoplasia, Cervical Dysplasia, Human Papillomavirus

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Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Balkibekova Venera Serikpaevna

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Background: Autism Spectrum Disorder (ASD) poses significant challenges in both diagnosis and treatment. Existing therapeutic interventions often target specific symptoms, necessitating the exploration of alternative approaches. This study investigates the RITM (Rhythm Integration Tapping Music) developed by Balkibekova, aiming to create imitation, social engagement and a wide range of emotions through brain development. Methods: A randomized controlled trial was conducted with 100 participants diagnosed with ASD, aged 1 to 4 years. Participants were randomly assigned to either the RITM therapy group or a control group receiving standard care. The RITM therapy, rooted in tapping rhythm to music such as: marche on the drums, waltz on bells, lullaby on musical triangle, dancing on tambourine, polka on wooden spoons. Therapy sessions were conducted over a 3 year period, with assessments at baseline, midpoint, and post-intervention. Results: Preliminary analyses reveal promising outcomes in the RITM therapy group. Participants demonstrated significant improvements in social interactions, speech understanding, birth of speech, and adaptive behaviors compared to the control group. Careful examination of subgroup analyses provides insights into the differential effectiveness of the RITM approach across various ASD profiles. Conclusions: The findings suggest that RITM therapy, as developed by Balkibekova, holds promise as intervention for ASD. The integrative nature of the approach, addressing multiple domains simultaneously, may contribute to its efficacy. Further research is warranted to validate these preliminary results and explore the long-term impact of RITM therapy on individuals with ASD. This abstract presents a snapshot of the research, emphasizing the significance, methodology, key findings, and implications of the RITM therapy method for consideration in an autism conference.

Keywords: RITM therapy, tapping rhythm, autism, mirror neurons, bright emotions, social interactions, communications

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Authors: Rafiqul Hasan, A. S. M. Tanim Anwar, Mohammad Azizul Hakim

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Background: Chronic kidney disease (CKD) is a major public health problem that continues to increase in prevalence globally. The prevalence of chronic kidney disease is increasing day by day in low to middle income countries (LMICs). People living in LMICs have the highest need for renal replacement therapy (RRT) despite they have lowest access to various modalities of treatment. As continuous ambulatory peritoneal dialysis (CAPD) does not require advanced technologies, very much infrastructure, dialysis staff support, it should be an ideal form of RRT in LMICs, particularly for those living in remote areas. To authors knowledge there was scarcity of data regarding CAPD performance in remote area of Bangladesh. This study was aimed to report the characteristics and outcomes of CAPD in ESRD patients lived in least developed area of Bangladesh. Methods: This prospective study was conducted in Cox’sbazar Medical College Hospital, Cox’sbazar and Parkview hospital Ltd, Chattogram, Bangladesh. Data were collected by questionnaire from the patients of any age with end-stage renal disease (ESRD) who underwent CAPD in 2018–2021. The baseline characteristics, PD-related complication as well as patient and technique survivals were analyzed. Results: Out of 31 patients who underwent CAPD, 18 (58%) were male on the age range of 15–79 years. The mean follow-up duration was 18 months. Mortality was inversely related with the EF of echocardiography. The peritonitis rate was 0.48 episodes per patient per year. The 1, 3 and 4-year patient survival rates were 64.34% (95% CI = 52.5–81.5), 23.79% (95% CI = 17.9 – 57.4) and 3.22% (95% CI = 31.2–77.5) respectively. Conclusions: In this study, CAPD performance was poorer than usual reference. Cardiac compromised patient and inappropriate dwell might be the main contributing factors behind this scenario. The peritonitis rate was nearly similar to that of developed countries. CAPD was cost effective than HD in remote area. Some accessible measures may be taken to make CAPD a more acceptable RRT modality with improved outcomes in poor socioeconomic backgrounds.

Keywords: dialysis cost, peritoneal dialysis, peritonitis, CAPD, least developed area, remote area, Bangladesh

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Authors: Sneh Laller, Kamini C. Tanwar

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Human beings are social animals. The scenario is changing and people become angry towards petty things and this may lead to committing a crime. Objective: The aim of the present research is: 1. To find out the difference between convicts and under-trials on different dimensions of Personality, Fear of Negative Evaluation (FNE) and Life-orientation; 2. To find out the difference between male and female jail inmates on different dimensions of Personality, Fear of Negative Evaluation (FNE) and Life-orientation; 3. To find out the relationship between different dimensions of Personality, Fear of Negative Evaluation (FNE) and Life-orientation in convicts and under-trials; 4. To find out the relationship between different dimensions of Personality, Fear of Negative Evaluation (FNE) and Life-orientation in male and female jail inmates. Method: The study was conducted on 100 participants (consisting of 50 convicts- 25 males and 25 females, and 50 under-trials- 25 males and 25 females); age range was 20-60 years. The NEO Five-Factor Inventory-3 by McCrae, Costa (2010), Brief Fear of Negative Evaluation scale- II by Leary (1983) and Life Orientation Test-R by Scheier et al. (1994) was used and purposive sampling technique was done for data collection. The t-test was applied to find out the comparison and Pearson correlation was applied to determine the relationship between personality, FNE and life-orientation in both the groups. Results: There is a significant difference in the dimension of personality that is neuroticism and life-orientation in convicts and under-trials and also, in the dimensions of personality such as neuroticism, extraversion, openness to experience and agreeableness, and FNE in male and female jail inmates. In convicts the dimension of personality, agreeableness shows significant positive correlation with life-orientation (r = 0.430**) whereas, in under-trials the dimension of personality, agreeableness shows significant positive correlation with FNE (r = 0.315*) and another dimension of personality, extraversion shows significant negative correlation with life-orientation (r = -0.409**). In male jail inmates, the dimension of personality, agreeableness shows significant positive correlation with FNE (r = 0.474**) whereas in female jail inmates, the dimension of personality, openness to experience shows significant negative correlation with FNE (r = -0.356*) and significant positive correlation of neuroticism with life-orientation (r = 0.292*). Conclusion: It was found that under-trials are neurotic and life-oriented than convicts, and female jail inmates are also neurotic and exhibit fear of negative evaluation whereas male jail inmates are extravert and agreeable.

Keywords: convicts, fear of negative evaluation, life-orientation, personality, under-trials

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Authors: Shanker Kalakotla, Krishna Mohan Gottumukkala

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Diabetes is a metabolic disorder characterized by hyperglycemia, carbohydrates, altered lipids and proteins metabolism. In recent research nanotechnology is a blazing field for the researchers; latterly there has been prodigious excitement in the nanomedicine and nano pharmacological area for the study of silver nanoparticles synthesis using natural products. Biological methods have been used to synthesize silver nanoparticles in presence of medicinally active antidiabetic plants, and this intention made us assess the biologically synthesized silver nanoparticles from the seed extract of Psoralea corylfolia using 1 mM silver nitrate solution. The synthesized herbal mediated silver nanoparticles (HMSNP’s) then subjected to various characterization techniques such as XRD, SEM, EDX, TEM, DLS, UV and FT-IR respectively. In current study, the silver nanoparticles tested for in-vitro anti-diabetic activity and possible toxic effects in healthy female albino mice by following OECD guidelines-425. Herbal mediated silver nanoparticles were successfully obtained from bioreduction of silver nitrate using Psoralea corylifolia plant extract. Silver nanoparticles have been appropriately characterized and confirmed using different types of equipment viz., UV-vis spectroscopy, XRD, FTIR, DLS, SEM and EDX analysis. From the behavioral observations of the study, the female albino mice did not show sedation, respiratory arrest, and convulsions. Test compounds did not cause any mortality at the dose level tested (i.e., 2000 mg/kg body weight) doses till the end of 14 days of observation and were considered safe. It may be concluded that LD50 of the HMSNPs was 2000mg/kg body weight. Since LD50 of the HMSNPs was 2000mg/kg body weight, so the preferred dose range for HMSNPs falls between the levels of 200 and 400 mg/kg. Further In-vivo pharmacological models and biochemical investigations will clearly elucidate the mechanism of action and will be helpful in projecting the currently synthesized silver nanoparticles as a therapeutic target in treating chronic ailments.

Keywords: herbal mediated silver nanoparticles, HMSNPs, toxicity of silver nanoparticles, PTP1B in-vitro anti-diabetic assay female albino mice, 425 OECD guidelines

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Authors: A. I. Dovgjallo, A. B. Tsapkova, A. A. Shimanov

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The paper is devoted to one of engine types with external heating – a thermoacoustic engine. In thermoacoustic engine heat energy is converted to an acoustic energy. Further, acoustic energy of oscillating gas flow must be converted to mechanical energy and this energy in turn must be converted to electric energy. The most widely used way of transforming acoustic energy to electric one is application of linear generator or usual generator with crank mechanism. In both cases, the piston is used. Main disadvantages of piston use are friction losses, lubrication problems and working fluid pollution which cause decrease of engine power and ecological efficiency. Using of a bidirectional impulse turbine as an energy converter is suggested. The distinctive feature of this kind of turbine is that the shock wave of oscillating gas flow passing through the turbine is reflected and passes through the turbine again in the opposite direction. The direction of turbine rotation does not change in the process. Different types of bidirectional impulse turbines for thermoacoustic engines are analyzed. The Wells turbine is the simplest and least efficient of them. A radial impulse turbine has more complicated design and is more efficient than the Wells turbine. The most appropriate type of impulse turbine was chosen. This type is an axial impulse turbine, which has a simpler design than that of a radial turbine and similar efficiency. The peculiarities of the method of an impulse turbine calculating are discussed. They include changes in gas pressure and velocity as functions of time during the generation of gas oscillating flow shock waves in a thermoacoustic system. In thermoacoustic system pressure constantly changes by a certain law due to acoustic waves generation. Peak values of pressure are amplitude which determines acoustic power. Gas, flowing in thermoacoustic system, periodically changes its direction and its mean velocity is equal to zero but its peak values can be used for bi-directional turbine rotation. In contrast with feed turbine, described turbine operates on un-steady oscillating flows with direction changes which significantly influence the algorithm of its calculation. Calculated power output is 150 W with frequency 12000 r/min and pressure amplitude 1,7 kPa. Then, 3-d modeling and numerical research of impulse turbine was carried out. As a result of numerical modeling, main parameters of the working fluid in turbine were received. On the base of theoretical and numerical data model of impulse turbine was made on 3D printer. Experimental unit was designed for numerical modeling results verification. Acoustic speaker was used as acoustic wave generator. Analysis if the acquired data shows that use of the bi-directional impulse turbine is advisable. By its characteristics as a converter, it is comparable with linear electric generators. But its lifetime cycle will be higher and engine itself will be smaller due to turbine rotation motion.

Keywords: acoustic power, bi-directional pulse turbine, linear alternator, thermoacoustic generator

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Authors: Amel Bouaziz, Seddik Khennouf, Mussa Abu Zarga, Shtayway Abdalla, Saliha Djidel, Assia Bentahar, Saliha Dahamna, Smain Amira

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The present study aimed to evaluate the hypotensive and the in vitro antioxidant activities of Crataegus azarolus L. (Rosaceae), a plant widely used as natural remedy for hypertension in folk medicine. The antioxidant potential of methanolic extract (ME)and its three fractions of Chloroform (CHE), ethyl acetate (EAE)and water (AqE) have been investigated using several assays, including the DPPH scavenging, ABTS scavenging, hydroxyl radical scavenging. Inhibition of lipid peroxidation was performed by the β-carotene bleaching assay, ferric thiocyanate method and thiobarburic acid method. Total phenolic and total flavonoid contents of the extracts were estimated using Folin-Chiocalteu reagent and AlCl3, respectively. EAE extract showed the highest polyphenolic and flavonoids contents (396,04±1.20 mg GAE/g of dry extract and 32,73 ± 0.03mg QE/g of dry extract) respectively. Similarly, this extract possessed the highest scavenging activity for DPPH radical (IC 50 = 0,006±0,0001mg /ml), ABTS radical (IC50=0.0035±0,0007 mg/ml) and hydroxyl radical(IC 50=0,283± 0.01 mg/ml). In addition, the EAE exhibited the highest antioxidant activity in the inhibition of linoleic acid/ß-carotene coupled oxidation (89,21%), lipid peroxidation in the ferric thiocyanate(FTC) method (90.13%), and thio-barbituric acid (TBA) method (74.23%). Intravenous administration of Me and EAE decreased mean arterial blood pressure, systolic and diastolic blood pressure in anesthetized rats dose-dependently, at the dose range of 0.4 to 12 mg/kg. The mean arterial blood pressure dropped by 27.58 and 39.37% for ME and EAE, respectively. In conclusion, The present study supported the significant potential to use C. azarolus by-products as a source of natural antioxidants and provides scientific justification for its traditional uses as cardio-protective and anti-hypertensive remedy.

Keywords: Crataegus azarolus, polyphenols, flavonoids, hypertension, antioxidant activity, free radicals, peroxidation

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Authors: Yongrong Liu, Xin Tang

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Background: Persistent atrial fibrillation is a common arrhythmia closely related to heart failure. Heart rate control is an essential strategy for treating persistent atrial fibrillation. Still, the understanding of the relationship between different heart rate control levels and the risk of heart failure rehospitalization is limited. Objective: The objective of the study is to determine the relationship between different levels of heart rate control in patients with persistent atrial fibrillation and the risk of readmission for heart failure. Methods: We conducted a retrospective dual-centre cohort study, collecting data from patients with persistent atrial fibrillation who received outpatient treatment at two tertiary hospitals in central and western China from March 2019 to March 2020. The collected data included age, gender, body mass index (BMI), medical history, and hospitalization frequency due to heart failure. Patients were divided into three groups based on their heart rate control levels: Group I with a resting heart rate of less than 80 beats per minute, Group II with a resting heart rate between 80 and 100 beats per minute, and Group III with a resting heart rate greater than 100 beats per minute. The readmission rates due to heart failure within one year after discharge were statistically analyzed using propensity score matching in a 1:1 ratio. Differences in readmission rates among the different groups were compared using one-way ANOVA. The impact of varying levels of heart rate control on the risk of readmission for heart failure was assessed using the Cox proportional hazards model. Binary logistic regression analysis was employed to control for potential confounding factors. Results: We enrolled a total of 1136 patients with persistent atrial fibrillation. The results of the one-way ANOVA showed that there were differences in readmission rates among groups exposed to different levels of heart rate control. The readmission rates due to heart failure for each group were as follows: Group I (n=432): 31 (7.17%); Group II (n=387): 11.11%; Group III (n=317): 90 (28.50%) (F=54.3, P<0.001). After performing 1:1 propensity score matching for the different groups, 223 pairs were obtained. Analysis using the Cox proportional hazards model showed that compared to Group I, the risk of readmission for Group II was 1.372 (95% CI: 1.125-1.682, P<0.001), and for Group III was 2.053 (95% CI: 1.006-5.437, P<0.001). Furthermore, binary logistic regression analysis, including variables such as digoxin, hypertension, smoking, coronary heart disease, and chronic obstructive pulmonary disease as independent variables, revealed that coronary heart disease and COPD also had a significant impact on readmission due to heart failure (p<0.001). Conclusion: The correlation between the heart rate control level of patients with persistent atrial fibrillation and the risk of heart failure rehospitalization is positive. Reasonable heart rate control may significantly reduce the risk of heart failure rehospitalization.

Keywords: heart rate control levels, heart failure rehospitalization, persistent atrial fibrillation, retrospective cohort study

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Authors: Mamoon Masud, Suleman Mazhar

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Indus river basin’s irrigation system in Pakistan is extremely complex, spanning over 50,000 km. Maintenance and monitoring of this demands enormous resources. This paper describes the development of a streamlined and low-cost autonomous underwater vehicle (AUV) for the monitoring of irrigation canals including water quality monitoring and water theft detection. The vehicle is a hovering-type AUV, designed mainly for monitoring irrigation canals, with fully documented design and open source code. It has a length of 17 inches, and a radius of 3.5 inches with a depth rating of 5m. Multiple sensors are present onboard the AUV for monitoring water quality parameters including pH, turbidity, total dissolved solids (TDS) and dissolved oxygen. A 9-DOF Inertial Measurement Unit (IMU), GY-85, is used, which incorporates an Accelerometer (ADXL345), a Gyroscope (ITG-3200) and a Magnetometer (HMC5883L). The readings from these sensors are fused together using directional cosine matrix (DCM) algorithm, providing the AUV with the heading angle, while a pressure sensor gives the depth of the AUV. 2 sonar-based range sensors are used for obstacle detection, enabling the vehicle to align itself with the irrigation canals edges. 4 thrusters control the vehicle’s surge, heading and heave, providing 3 DOF. The thrusters are controlled using a proportional-integral-derivative (PID) feedback control system, with heading angle and depth being the controller’s input and the thruster motor speed as the output. A flow sensor has been incorporated to monitor canal water level to detect water-theft event in the irrigation system. In addition to water theft detection, the vehicle also provides information on water quality, providing us with the ability to identify the source(s) of water contamination. Detection of such events can provide useful policy inputs for improving irrigation efficiency and reducing water contamination. The AUV being low cost, small sized and suitable for autonomous maneuvering, water level and quality monitoring in the irrigation canals, can be used for irrigation network monitoring at a large scale.

Keywords: the autonomous underwater vehicle, irrigation canal monitoring, water quality monitoring, underwater line tracking

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Authors: John Christian Lequiron, Gerry Bagtasa, Olivia Cabrera, Leoncio Amadore, Tolentino Moya

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Changes in air surface temperature play an important role in the Philippine’s economy, industry, health, and food production. While increasing global mean temperature in the recent several decades has prompted a number of climate change and variability studies in the Philippines, most studies still focus on rainfall and tropical cyclones. This study aims to investigate the trend and variability of observed air surface temperature and determine its major influencing factor/s in the Philippines. A non-parametric Mann-Kendall trend test was applied to monthly mean temperature of 17 synoptic stations covering 56 years from 1960 to 2015 and a mean change of 0.58 °C or a positive trend of 0.0105 °C/year (p < 0.05) was found. In addition, wavelet decomposition was used to determine the frequency of temperature variability show a 12-month, 30-80-month and more than 120-month cycles. This indicates strong annual variations, interannual variations that coincide with ENSO events, and interdecadal variations that are attributed to PDO and CO2 concentrations. Air surface temperature was also correlated with smoothed sunspot number and galactic cosmic rays, the results show a low to no effect. The influence of ENSO teleconnection on temperature, wind pattern, cloud cover, and outgoing longwave radiation on different ENSO phases had significant effects on regional temperature variability. Particularly, an anomalous anticyclonic (cyclonic) flow east of the Philippines during the peak and decay phase of El Niño (La Niña) events leads to the advection of warm southeasterly (cold northeasterly) air mass over the country. Furthermore, an apparent increasing cloud cover trend is observed over the West Philippine Sea including portions of the Philippines, and this is believed to lessen the effect of the increasing air surface temperature. However, relative humidity was also found to be increasing especially on the central part of the country, which results in a high positive trend of heat index, exacerbating the effects on human discomfort. Finally, an assessment of gridded temperature datasets was done to look at the viability of using three high-resolution datasets in future climate analysis and model calibration and verification. Several error statistics (i.e. Pearson correlation, Bias, MAE, and RMSE) were used for this validation. Results show that gridded temperature datasets generally follows the observed surface temperature change and anomalies. In addition, it is more representative of regional temperature rather than a substitute to station-observed air temperature.

Keywords: air surface temperature, carbon dioxide, ENSO, galactic cosmic rays, smoothed sunspot number

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Authors: Sharda Kumari

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As the Covid-19 pandemic continues to leave its mark on the global business landscape, companies have had to adapt to new realities and find ways to sustain their operations amid social distancing measures, government restrictions, and heightened public health concerns. This unprecedented situation has placed considerable stress on both employees and employers, underscoring the need for innovative approaches to manage the risks associated with Covid-19 transmission in the workplace. In response to these challenges, the pandemic has accelerated the adoption of digital technologies, with an increasing preference for remote interactions and virtual collaboration. Customer relationship management (CRM) systems have risen to prominence as a vital resource for organizations navigating the post-pandemic world, providing a range of benefits that include acquiring new customers, generating insightful consumer data, enhancing customer relationships, and growing market share. In the context of pandemic management, CRM systems offer three primary advantages: (1) integration features that streamline operations and reduce the need for multiple, costly software systems; (2) worldwide accessibility from any internet-enabled device, facilitating efficient remote workforce management during a pandemic; and (3) the capacity for rapid adaptation to changing business conditions, given that most CRM platforms boast a wide array of remotely deployable business growth solutions, a critical attribute when dealing with a dispersed workforce in a pandemic-impacted environment. These advantages highlight the pivotal role of CRM systems in helping organizations remain resilient and adaptive in the face of ongoing global challenges.

Keywords: healthcare, CRM, customer relationship management, customer experience, digital transformation, pandemic response, patient monitoring, patient management, healthcare automation, electronic health record, patient billing, healthcare information systems, remote workforce, virtual collaboration, resilience, adaptable business models, integration features, CRM in healthcare, telehealth, pandemic management

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Authors: Recep Kesli, Gulsah Asik, Cengiz Demir, Onur Turkyilmaz

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Objective: Stenotrophomonas maltophilia (S. maltophilia) has recently emerged as an important nosocomial microorganism. Treatment of invasive infections caused by this organism is problematic because this microorganism is usually resistant to a wide range of commonly used antimicrobials. We aimed to evaluate clinical isolates of S. maltophilia in respect to sampling sites and antimicrobial resistant. Method: During a two years period (October 2013 and September 2015) eighteen samples collected from the intensive care unit (ICU) patients hospitalized in Afyon Kocatepe University, ANS Practice and Research Hospital. Identification of the bacteria was determined by conventional methods and automated identification system-VITEK 2 (bio-Mérieux, Marcy l’toile, France). Antibacterial resistance tests were performed by Kirby Bauer disc (Oxoid, England) diffusion method following the recommendations of CLSI. Results: Eighteen S. maltophilia strains were identified as the causative agents of different infections. The main type of infection was lower respiratory tract infection (83,4 %); three patients (16,6 %) had bloodstream infection. While, none of the 18 S. maltophilia strains were found to be resistant against to trimethoprim sulfametaxasole (TMP-SXT) and levofloxacine, eight strains 66.6 % were found to be resistant against ceftazidim. Conclusion: The isolation of S.maltophilia starains resistant to TMP-SXT is vital. In order to prevent or minimize infections due to S. maltophilia such precuations should be utilized: Avoidance of inappropriate antibiotic use, prolonged implementation of foreign devices, reinforcement of hand hygiene practices and the application of appropriate infection control practices. Microbiology laboratories also may play important roles in controlling S. maltophilia infections by monitoring the prevalence, continuously, the provision of local antibiotic resistance paterns data and the performance of synergistic studies also may help to guide appropirate antimicrobial therapy choices.

Keywords: Stenotrophomonas maltophilia, trimethoprim-sulfamethoxazole, antimicrobial resistance, Stenotrophomonas spp.

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Authors: Omar Amoudi, Latifa Al Brashdi

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Construction projects are always subject to risks and uncertainties due to its unique and dynamic nature, outdoor work environment, the wide range of skills employed, various parties involved in addition to situation of construction business environment at large. Altogether, these risks and uncertainties affect projects objectives and lead to cost overruns, delay, and poor quality. Construction projects in Oman often experience cost overruns and delay. Managing these risks and reducing their impacts on construction cost requires firstly identifying these risks, and then analyzing their severity on project cost to obtain deep understanding about these risks. This in turn will assist construction managers in managing and tacking these risks. This paper aims to investigate the main risk factors that affect construction projects cost in the Sultanate of Oman. In order to achieve the main aim, literature review was carried out to identify the main risk factors affecting construction cost. Thirty-three risk factors were identified from the literature. Then, a questionnaire survey was designed and distributed among construction professionals (i.e., client, contractor and consultant) to obtain their opinion toward the probability of occurrence for each risk factor and its possible impact on construction project cost. The collected data was analyzed based on qualitative aspects and in several ways. The severity of each risk factor was obtained by multiplying the probability occurrence of a risk factor with its impact. The findings of this study reveal that the most significant risk factors that have high severity impact on construction project cost are: Change of Oil Price, Delay of Materials and Equipment Delivery, Changes in Laws and Regulations, Improper Budgeting, and Contingencies, Lack of Skilled Workforce and Personnel, Delays Caused by Contractor, Delays of Owner Payments, Delays Caused by Client, and Funding Risk. The results can be used as a basis for construction managers to make informed decisions and produce risk response procedures and strategies to tackle these risks and reduce their negative impacts on construction project cost.

Keywords: construction cost, construction projects, Oman, risk factors, risk management

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