Search results for: perfectly matched layer

Authors: Abu Salim Mustafa

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Brucellosis is a zoonotic disease endemic in Kuwait. Human brucellosis can be caused by several Brucella species with Brucella melitensis causing the most severe and Brucella abortus the least severe disease. Furthermore, relapses are common after successful chemotherapy of patients. The classical biochemical methods of culture and serology for identification of Brucellae provide information about the species and serotypes only. However, to differentiate between relapse and reinfection/epidemiological investigations, the identification of genotypes using molecular methods is essential. In this study, four molecular methods [16S rRNA gene sequencing, real-time PCR, enterobacterial repetitive intergenic consensus (ERIC)-PCR and multilocus variable-number tandem-repeat analysis (MLVA)-16] were evaluated for the identification and typing of 75 strains of Brucella isolated in Kuwait. The 16S rRNA gene sequencing suggested that all the strains were B. melitensis and real-time PCR confirmed their species identity as B. melitensis. The ERIC-PCR band profiles produced a dendrogram of 75 branches suggesting each strain to be of a unique type. The cluster classification, based on ~ 80% similarity, divided all the ERIC genotypes into two clusters, A and B. Cluster A consisted of 9 ERIC genotypes (A1-A9) corresponding to 9 individual strains. Cluster B comprised of 13 ERIC genotypes (B1-B13) with B5 forming the largest cluster of 51 strains. MLVA-16 identified all isolates as B. melitensis and divided them into 71 MLVA-types. The cluster analysis of MLVA-16-types suggested that most of the strains in Kuwait originated from the East Mediterranean Region, a few from the African group and one new genotype closely matched with the West Mediterranean region. In conclusion, this work demonstrates that B. melitensis, the most pathogenic species of Brucella, is prevalent in Kuwait. Furthermore, MLVA-16 is the best molecular method, which can identify the Brucella species and genotypes as well as determine their origin in the global context. Supported by Kuwait University Research Sector grants MI04/15 and SRUL02/13.

Keywords: Brucella, ERIC-PCR, MLVA-16, RT-PCR, 16S rRNA gene sequencing

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Authors: Daham Owaid Matrood, Naqaa Hussein Raheem

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Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

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Authors: Ibrahim Ozkan

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In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.

Keywords: cooperative network, adaptive modulation and coding, hybrid ARQ, correlated fading

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Maha Salah Abdullah Ismail, Manal S. Ismail, Amir M. Saleh

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Reflexology treatment is a method for enhancing body relaxation. It is a widely recognized as an alternative therapy, effective for many health conditions. This study aimed to evaluate the effect of reflexology treatment on arterial blood gases among mechanically ventilated patients. A quasi-experimental (pre and post-test) research design was used. Research hypothesis was mechanically ventilated patients who will receive the reflexology treatment will have improvement in their arterial blood gases than those who will not. The current study was carried out in different Intensive Care Units at the Cairo University Hospitals. A purposeful sample of 100 adults’ mechanically ventilated patients was recruited over a period of three months of data collection. The participants were divided into two equally matched groups; (1) The study group who has received the routine care, in addition, two reflexology sessions on the feet, (2) The control group who has received only the routine care. One tool was utilized to collect data pertinent to the study; mechanically ventilated patients' data sheet that consists of demographic and medical data. Result: Majority (58% of the study group and 82% of the control group) were males, with mean age of 50.9 years in both groups. Patients who received the reflexology treatment significantly increase in the oxygen saturation pre second session (t=5.15, p=.000), immediate post sessions (t=4.4, p=.000) and post two hours (t= 4.7, p= .000). The study group was more likely to have lower PaO2 (F=5.025, p=.015), PaCo2 (F=4.952, p=.025) and higher HCo3 (F=15.211, p=.000) than the control group. Conclusion: This study results support the positive effect of reflexology treatment in improving some arterial blood gases among mechanically ventilated patients’ with the conventional therapy as in the study group there was increase in the oxygen saturation. In differences between groups there decrease PaO2, PaCo2 and increase HCo3 in the study group. Recommendation: Nurses should be trained how to demonstrate the foot reflexology among mechanically ventilated patients.

Keywords: arterial blood gases, foot, mechanical ventilated patient, reflexology

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Authors: Tosin Ige

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Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining.

Keywords: data, privacy, data mining, association rule, privacy preserving, mining technique

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Authors: Shawn G. Gallaher, Lilli E. Hirth

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As permafrost thaws with the continued warming of the Alaskan North Slope, a progressively thicker active thaw layer is evidently releasing previously sequestered nutrients, metals, and particulate matter exposed to fluvial transport. In this study, we estimate material fluxes on the North Slope of Alaska during the 2019-2022 melt seasons. The watershed of the Alaskan North Slope can be categorized into three regions: mountains, tundra, and coastal plain. Precipitation and discharge data were collected from repeat visits to 14 sample sites for biogeochemical surface water samples, 7 point discharge measurements, 3 project deployed meteorology stations, and 2 U. S. Geological Survey (USGS) continuous discharge observation sites. The timing, intensity, and spatial distribution of precipitation determine the material flux composition in the Sagavanirktok and surrounding bodies of water, with geogenic constituents (e.g., dissolved inorganic carbon (DIC)) expected from mountain flushed events and biogenic constituents (e.g., dissolved organic compound (DOC)) expected from transitional tundra precipitation events. Project goals include connecting late summer precipitation events to peak discharge to determine the responses of the watershed to localized atmospheric forcing. Field study measurements showed widespread precipitation in August 2019, generating an increase in total suspended solids, dissolved organic carbon, and iron fluxes from the tundra, shifting the main-stem mountain river biogeochemistry toward tundra source characteristics typically only observed during the spring floods. Intuitively, a large-scale precipitation event (as defined by this study as exceeding 12.5 mm of precipitation on a single observation day) would dilute a body of water; however, in this study, concentrations increased with higher discharge responses on several occasions. These large-scale precipitation events continue to produce peak constituent fluxes as the thaw layer increases in depth and late summer precipitation increases, evidenced by 6 large-scale events in July 2022 alone. This increase in late summer events is in sharp contrast to the 3 or fewer large events in July in each of the last 10 years. Changes in precipitation intensity, timing, and location have introduced late summer peak constituent flux events previously confined to the spring freshet.

Keywords: Alaska North Slope, arctic rivers, material flux, precipitation

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Authors: H. Medoukali, B. Zegnini

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Insulation techniques in high-voltage cables rely heavily on chemically synapsed polyethylene. The latter may contain manufacturing defects such as small cavities, for example. The presence of the cavity affects the distribution of the electric field at the level of the insulating layer; this change in the electric field is affected by the presence of different space charge densities within the insulating material. This study is carried out by performing simulations to determine the distribution of the electric field inside the insulator. The simulations are based on the creation of a two-dimensional model of a high-voltage cable of 154 kV using the COMSOL Multiphysics software. Each time we study the effect of changing the space charge density of on the electromechanical Constraints.

Keywords: COMSOL multiphysics, electric field, HVDC, microcavities, space charges, XLPE

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Authors: Abdelhak-Djamel Seriai, Selim Kebir, Allaoua Chaoui

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During the last decades, object oriented program- ming has been massively used to build large-scale systems. However, evolution and maintenance of such systems become a laborious task because of the lack of object oriented programming to offer a precise view of the functional building blocks of the system. This lack is caused by the fine granularity of classes and objects. In this paper, we use a post object-oriented technology namely software components, to propose an approach based on the identification of the functional building blocks of an object oriented system by analyzing its source code. These functional blocks are specified as software components and the result is a multi-layer component based software architecture.

Keywords: software comprehension, software component, object oriented, software architecture, reverse engineering

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Authors: Daniela Prado, Lexi Frankel, Amalia Ardeljan, Lokesh Manjani, Matthew Cardeiro, Omar Rashid

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Background: Clostridium difficile (C. diff) is a gram-positive bacterium that is known to cause life-threatening diarrhea and severe inflammation of the colon. It originates as an alteration of the gut microbiome and can be transmitted through spores. Recent studies have shown a high association between the development of C. diff in cancer patients due to extensive hospitalization. However, research is lacking regarding C. diff’s association in the causation or prevention of cancer. The objective of this study was to therefore assess the correlation between Clostridium difficile infection (CDI) and the incidence of bone cancer. Methods: This retrospective analysis used data provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to evaluate the patients infected versus patients not infected with C. diff using ICD-10 and ICD-9 codes. Access to the database was granted by the Holy Cross Health, Fort Lauderdale, for the purpose of academic research. Standard statistical methods were used. Results: Between January 2010 and December 2019, the query was analyzed and resulted in 78863 patients in both the infected and control group, respectively. The two groups were matched by age range and CCI score. The incidence of bone cancer was 659 patients (0.835%) in the C. diff group compared to 1941 patients (2.461%) in the control group. The difference was statistically significant by a P-value < 2.2x10^-16 with an odds ratio (OR)= 0.33 (0.31-0.37) with a 95% confidence interval (CI). Treatment for CDI was analyzed for both C. diff infected and noninfected populations. 91 out of 16,676 (0.55%) patients with a prior C. diff infection and treated with antibiotics were compared to the control group were 275 out of 16,676 (1.65%) patients with no history of CDI and received antibiotic treatment. Results remained statistically significant by P-value <2.2x10-16 with an OR= 0.42 (0.37, 0.48). and a 95% CI. Conclusion: The study shows a statistically significant correlation between C. diff and a reduced incidence of bone cancer. Further evaluation is recommended to assess the potential of C. difficile in reducing bone cancer incidence.

Keywords: bone cancer, colitis, clostridium difficile, microbiome

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Authors: Aipeng Zhu, Yun Zhang

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The growing grievous environment problems together with the exhaustion of energy resources put urgent demands for developing high energy density. Considering the factors including capacity, resource and environment, Manganese-based lithium-rich layer-structured cathode materials xLi₂MnO₃⋅(1-x)LiMO₂ (M = Ni, Co, Mn, and other metals) are drawing increasing attention due to their high reversible capacities, high discharge potentials, and low cost. They are expected to be one type of the most promising cathode materials for the next-generation Li-ion batteries (LIBs) with higher energy densities. Unfortunately, their commercial applications are hindered with crucial drawbacks such as poor rate performance, limited cycle life and continuous falling of the discharge potential. With decades of extensive studies, significant achievements have been obtained in improving their cyclability and rate performances, but they cannot meet the requirement of commercial utilization till now. One major problem for lithium-rich layer-structured cathode materials (LLOs) is the side reaction during cycling, which leads to severe surface degradation. In this process, the metal ions can dissolve in the electrolyte, and the surface phase change can hinder the intercalation/deintercalation of Li ions and resulting in low capacity retention and low working voltage. To optimize the LLOs cathode material, the surface coating is an efficient method. Considering the price and stability, Al₂O₃ was used as a coating material in the research. Meanwhile, due to the low initial Coulombic efficiency (ICE), the pristine LLOs was pretreated by KMnO₄ to increase the ICE. The precursor was prepared by a facile coprecipitation method. The as-prepared precursor was then thoroughly mixed with Li₂CO₃ and calcined in air at 500℃ for 5h and 900℃ for 12h to produce Li₁.₂[Ni₀.₂Mn₀.₆]O₂ (LNMO). The LNMO was then put into 0.1ml/g KMnO₄ solution stirring for 3h. The resultant was filtered and washed with water, and dried in an oven. The LLOs obtained was dispersed in Al(NO₃)₃ solution. The mixture was lyophilized to confer the Al(NO₃)₃ was uniformly coated on LLOs. After lyophilization, the LLOs was calcined at 500℃ for 3h to obtain LNMO@LMO@ALO. The working electrodes were prepared by casting the mixture of active material, acetylene black, and binder (polyvinglidene fluoride) dissolved in N-methyl-2-pyrrolidone with a mass ratio of 80: 15: 5 onto an aluminum foil. The electrochemical performance tests showed that the multiple surface modified materials had a higher initial Coulombic efficiency (84%) and better capacity retention (91% after 100 cycles) compared with that of pristine LNMO (76% and 80%, respectively). The modified material suggests that the KMnO₄ pretreat and Al₂O₃ coating can increase the ICE and cycling stability.

Keywords: Li-rich materials, surface coating, lithium ion batteries, Al₂O₃

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Authors: Gilbert O. Osayemwenre, Meyer L. Edson

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Ethylene vinyl acetate (EVA) encapsulation degradation affects the performance of photovoltaic (PV) module. Hotspot formation causes the EVA encapsulation to undergo photothermal deterioration and molecular breakdown by UV radiation. This leads to diffusion of chemical particles into other layers. During outdoor deployment, the EVA encapsulation in the affect region loses its adhesive strength, when this happen the affected region layer undergoes rapid delamination. The presence of photo-thermal degradation is detrimental to PV modules as it causes both optical and thermal degradation. Also, it enables the encapsulant to be more susceptible to chemicals substance and moisture. Our findings show a high concentration of Sodium, Phosphorus and Aluminium which originate from the glass substrate, cell emitter and back contact respectively.

Keywords: ethylene vinyl acetate (EVA), encapsulation, photo-thermal degradation, thermogravimetric analysis (TGA), scanning probe microscope (SPM)

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: Naresh Radaliyagoda, Nigel M. Jennett, Rong Lan, David Parfitt

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A series of length scale engineered multilayer material with temperature robust mechanical properties has been suggested. A range of polycrystalline copper sub-layers with the thickness varying from 1 to 25μm and buried in between two nickel layers was produced using electrodeposition dual bath technique. The structure of the multilayers was characterized using Electron Backscatter Diffraction and Scanning Electron Microscope. The interface effect on the hardness and elastic modulus was tested using Nano-indentation. Results of the grain size and layer thickness measurements, and indentation hardness have been compared. It is found that there is a combined length scale effect that improves mechanical properties in Ni/Cu multilayer structures.

Keywords: nano-indentation, size effect, multilayers, electrodeposition

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Authors: M. Fathi, Z. Farhaninejad, M. Shahedi, M. Sadeghi

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Drying is one of the oldest preservation methods for food and agriculture products. Appropriate control of operation can be obtained by modeling. Limitation of continues models for complex boundary condition and non-regular geometries leading to appearance of discrete novel methods such as cellular automata, which provides a platform for obtaining fast predictions by rule-based mathematics. In this research a one D dimensional CA was used for simulating thin layer drying of banana. Banana slices were dried with a convectional air dryer and experimental data were recorded for validating of final model. The model was programmed by MATLAB, run for 70000 iterations and von-Neumann neighborhood. The validation results showed a good accordance between experimental and predicted data (R=0.99). Cellular automata are capable to reproduce the expected pattern of drying and have a powerful potential for solving physical problems with reasonable accuracy and low calculating resources.

Keywords: banana, cellular automata, drying, modeling

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Authors: Hamed Khosravi, Reza Eslami-Farsani

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Increased interest in lightweight and efficient structural components has created the need for selecting materials with improved mechanical properties. To do so, composite materials are being widely used in many applications, due to durability, high strength and modulus, and low weight. Among the various composite structures, grid-stiffened structures are extensively considered in various aerospace and aircraft applications, because of higher specific strength and stiffness, higher impact resistance, superior load-bearing capacity, easy to repair, and excellent energy absorption capability. Although there are a good number of publications on the design aspects and fabrication of grid structures, little systematic work has been reported on their material modification to improve their properties, to our knowledge. Therefore, the aim of this research is to study the reinforcing effect of silica nanoparticles on the flexural properties of epoxy/E-glass isogrid panels under three-point bending test. Samples containing 0, 1, 3, and 5 wt.% of the silica nanoparticles, with 44 and 48 vol.% of the glass fibers in the ribs and skin components respectively, were fabricated by using a manual filament winding method. Ultrasonic and mechanical routes were employed to disperse the nanoparticles within the epoxy resin. To fabricate the ribs, the unidirectional fiber rovings were impregnated with the matrix mixture (epoxy + nanoparticles) and then laid up into the grooves of a silicone mold layer-by-layer. At once, four plies of woven fabrics, after impregnating into the same matrix mixture, were layered on the top of the ribs to produce the skin part. In order to conduct the ultimate curing and to achieve the maximum strength, the samples were tested after 7 days of holding at room temperature. According to load-displacement graphs, the bellow trend was observed for all of the samples when loaded from the skin side; following an initial linear region and reaching a load peak, the curve was abruptly dropped and then showed a typical absorbed energy region. It would be worth mentioning that in these structures, a considerable energy absorption was observed after the primary failure related to the load peak. The results showed that the flexural properties of the nanocomposite samples were always higher than those of the nanoparticle-free sample. The maximum enhancement in flexural maximum load and energy absorption was found to be for the incorporation of 3 wt.% of the nanoparticles. Furthermore, the flexural stiffness was continually increased by increasing the silica loading. In conclusion, this study suggested that the addition of nanoparticles is a promising method to improve the flexural properties of grid-stiffened fibrous composite structures.

Keywords: grid-stiffened composite structures, nanocomposite, three point flexural test , energy absorption

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Authors: Richard Colwell, Thomas Englert

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In cryogenic liquefaction processes, brazed aluminum core heat exchangers are used to minimize surface area/volume of the exchanger. Aluminum alloy (5083-H321; UNS A95083) piping must transition to higher melting point 304L stainless steel piping outside of the heat exchanger kettle or cold box for safety reasons. Since aluminum alloys and austenitic stainless steel cannot be directly welded to together, a transition joint consisting of 5 layers of different metals explosively bonded are used. Failures of two of these joints resulted in process shut-down and loss of revenue. Failure analyses, FEA analysis, and mock-up testing were performed by multiple teams to gain a further understanding into the failure mechanisms involved.

Keywords: explosion bonding, intermetallic compound, thermal strain, titanium-nickel Interface

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Authors: Ramanish Ravishankar, Azar Hussain, Mahmoud Loubani, Mubarak Chaudhry

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Background and Aims: Currently, there are no strict guidelines in cardiopulmonary bypass temperature management in cardiac surgery not involving the aortic arch. This study aims to compare patient outcomes undergoing mild hypothermia and normothermia. The aim of this study was to compare patient outcomes between mild hypothermia and normothermia undergoing on-pump cardiac surgery not involving the aortic arch. Methods: This was a retrospective cohort study from January 2015 until May 2023. Patients who underwent cardiac surgery with cardiopulmonary bypass temperatures ≥32oC were included and stratified into mild hypothermia (32oC – 35oC) and normothermia (>35oC) cohorts. Propensity matching was applied through the nearest neighbour method (1:1) using the risk factors detailed in the EuroScore using RStudio. The primary outcome was mortality. Secondary outcomes included post-op stay, intensive care unit readmission, re-admission, stroke, and renal complications. Patients who had major aortic surgery and off-pump operations were excluded. Results: Each cohort had 1675 patients. There was a significant increase in overall mortality with the mild hypothermia cohort (3.59% vs. 2.32%; p=0.04912). There was also a greater stroke incidence (2.09% vs. 1.13%; p=0.0396) and transient ischaemic attack (TIA) risk (3.1% vs. 1.49%; p=0.0027). There was no significant difference in renal complications (9.13% vs. 7.88%; p=0.2155). Conclusions: Patient’s who underwent mild hypothermia during cardiopulmonary bypass have a significantly greater mortality, stroke, and transient ischaemic attack incidence. Mild hypothermia does not appear to provide any benefit over normothermia and does not appear to provide any neuroprotective benefits. This shows different results to that of other major studies; further trials and studies need to be conducted to reach a consensus.

Keywords: cardiac surgery, therapeutic hypothermia, neuroprotection, cardiopulmonary bypass

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Authors: Qiong Rao, Xiongqi Peng

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In this work, multi-walled carbon nanotubes (MWCNTs) and graphene nanoplates (GNPs) were dispersed into epoxy adhesive to investigate their synergy effects on the shear properties, mode I and mode II fracture toughness of unidirectional composite bonded joints. Testing results showed that the incorporation of MWCNTs and GNPs significantly improved the shear strength, the mode I and mode II fracture toughness by 36.6%, 45% and 286%, respectively. In addition, the fracture surfaces of the bonding area as well as the toughening mechanism of nanofillers were analyzed. Finally, a nonlinear cohesive/friction coupled model for delamination analysis of adhesive layer under shear and normal compression loadings was proposed and implemented in ABAQUS/Explicit via user subroutine VUMAT.

Keywords: nanofillers, adhesive joints, fracture toughness, cohesive zone model

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Authors: H. Fazlinejad, A. Halvaee

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In this study, characteristics of ATIG welds using ZnO flux on aluminum was investigated and compared with TIG welds. Autogenously AC-ATIG bead on plate welding was applied on Al1050 plate with a coating of ZnO as the flux. Different levels of welding current and flux layer thickness was considered to study the effect of heat input and flux quantity on ATIG welds and was compared with those of TIG welds. Geometrical investigation of the weld cross sections revealed that penetration depth of the ATIG welds with ZnO flux, was increased up to 2 times in some samples compared to the TIG welds. Optical metallographic and Scanning Electron Microscopy (SEM) observations revealed similar microstructures in TIG and ATIG welds. Composition of the ATIG welds slag was also analyzed using X-ray diffraction. In both TIG and ATIG samples, the lowest values of microhardness were observed in the HAZ.

Keywords: ATIG, active flux, weld penetration, Al 1050, ZnO

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Diego F. Camacho, Cristian J. Mejia, Carlos Duque-Daza

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The recent widespread use of Unmanned Aerial Vehicles (UAVs) has fueled a renewed interest in efficiency and performance of airfoils, particularly for applications at low and moderate Reynolds numbers, typical of this kind of vehicles. Most of previous efforts in the aeronautical industry, regarding aerodynamic efficiency, had been focused on high Reynolds numbers applications, typical of commercial airliners and large size aircrafts. However, in order to increase the levels of efficiency and to boost the performance of these UAV, it is necessary to explore new alternatives in terms of airfoil design and application of drag reduction techniques. The objective of the present work is to carry out the analysis and comparison of performance levels between a standard NACA0012 profile against another one featuring a wall protuberance or surface perturbation. A computational model, based on the finite volume method, is employed to evaluate the effect of the presence of geometrical distortions on the wall. The performance evaluation is achieved in terms of variations of drag and lift coefficients for the given profile. In particular, the aerodynamic performance of the new design, i.e. the airfoil with a surface perturbation, is examined under conditions of incompressible and subsonic flow in transient state. The perturbation considered is a shaped protrusion prescribed as a small surface deformation on the top wall of the aerodynamic profile. The ultimate goal by including such a controlled smooth artificial roughness was to alter the turbulent boundary layer. It is shown in the present work that such a modification has a dramatic impact on the aerodynamic characteristics of the airfoil, and if properly adjusted, in a positive way. The computational model was implemented using the unstructured, FVM-based open source C++ platform OpenFOAM. A number of numerical experiments were carried out at Reynolds number 5x104, based on the length of the chord and the free-stream velocity, and angles of attack 6° and 12°. A Large Eddy Simulation (LES) approach was used, together with the dynamic Smagorinsky approach as subgrid scale (SGS) model, in order to account for the effect of the small turbulent scales. The impact of the surface perturbation on the performance of the airfoil is judged in terms of changes in the drag and lift coefficients, as well as in terms of alterations of the main characteristics of the turbulent boundary layer on the upper wall. A dramatic change in the whole performance can be appreciated, including an arguably large level of lift-to-drag coefficient ratio increase for all angles and a size reduction of laminar separation bubble (LSB) for a twelve-angle-of-attack.

Keywords: CFD, LES, Lift-to-drag ratio, LSB, NACA 0012 airfoil

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Authors: Xiao-Mei Zhang, Sian-Hong Tseng, Ming-Yen Lu

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Two-dimensional (2D) transition metal dichalcogenides (TMDCs), such as MoS₂, have attracted considerable attention owing to the unique optical and electronic properties related to its 2D ultrathin atomic layer structure. MoS₂ is becoming prevalent in post-silicon digital electronics and in highly efficient optoelectronics due to its extremely low thickness and its tunable band gap (Eg = 1-2 eV). For low-power, high-performance complementary logic applications, both p- and n-type MoS₂ FETs (NFETs and PFETs) must be developed. NFETs with an electron accumulation channel can be obtained using unintentionally doped n-type MoS₂. However, the fabrication of MoS₂ FETs with complementary p-type characteristics is challenging due to the significant difficulty of injecting holes into its inversion channel. Plasma treatments with different species (including CF₄, SF₆, O₂, and CHF₃) have also been found to achieve the desired property modifications of MoS₂. In this work, we demonstrated a p-type multilayer MoS₂ enabled by selective-area doping using CHF₃ plasma treatment. Compared with single layer MoS₂, multilayer MoS₂ can carry a higher drive current due to its lower bandgap and multiple conduction channels. Moreover, it has three times the density of states at its minimum conduction band. Large-area growth of MoS₂ films on 300 nm thick SiO₂/Si substrate is carried out by thermal decomposition of ammonium tetrathiomolybdate, (NH₄)₂MoS₄, in a tube furnace. A two-step annealing process is conducted to synthesize MoS₂ films. For the first step, the temperature is set to 280 °C for 30 min in an N₂ rich environment at 1.8 Torr. This is done to transform (NH₄)₂MoS₄ into MoS₃. To further reduce MoS₃ into MoS₂, the second step of annealing is performed. For the second step, the temperature is set to 750 °C for 30 min in a reducing atmosphere consisting of 90% Ar and 10% H₂ at 1.8 Torr. The grown MoS₂ films are subjected to out-of-plane doping by CHF₃ plasma treatment using a Dry-etching system (ULVAC original NLD-570). The radiofrequency power of this dry-etching system is set to 100 W and the pressure is set to 7.5 mTorr. The final thickness of the treated samples is obtained by etching for 30 s. Back-gated MoS₂ PFETs were presented with an on/off current ratio in the order of 10³ and a field-effect mobility of 65.2 cm²V⁻¹s⁻¹. The MoS₂ PFETs photodetector exhibited ultraviolet (UV) photodetection capability with a rapid response time of 37 ms and exhibited modulation of the generated photocurrent by back-gate voltage. This work suggests the potential application of the mild plasma-doped p-type multilayer MoS₂ in UV photodetectors for environmental monitoring, human health monitoring, and biological analysis.

Keywords: photodetection, p-type doping, multilayers, MoS₂

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Authors: Vinita Choudhary

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The solidly mounted resonator (SMR) is a bulk acoustic wave-based device consisting of a piezoelectric layer sandwiched between two electrodes upon Bragg reflectors, which then are attached to a substrate. To transform the effective acoustic impedance of the substrate to a near zero value, the Bragg reflectors are composed of alternating high and low acoustic impedance layers of quarter-wavelength thickness. In this work presents the design and investigation of acoustic Bragg reflectors (ABRs) for solidly mounted bulk acoustic wave resonators through analysis and simulation. This performance of the resonator is analyzed using 1D Mason modeling. The performance parameters are the effect of Bragg pairs number on transmissivity, reflectivity, insertion loss, the electromechanical and quality factor of the 5GHz operating resonator.

Keywords: bragg reflectors, SMR, insertion loss, quality factor

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Authors: Ibrahim Turfanda, Soniya Rai, Karan Vadher

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Objectives— The study aims to compare real-world adherence to and persistence with dulaglutide and liraglutide in patients with type 2 diabetes (T2D) initiating treatment in UAE. Methods— This was a retrospective, non-interventional study (observation period: 01 March 2017–31 August 2019) using the UAE Dubai e-Claims database. Included: adult patients initiating dulaglutide/liraglutide 01 September 2017–31 August 2018 (index period) with: ≥1 claim for T2D in the 6 months before index date (ID); ≥1 claim for dulaglutide/liraglutide during index period; and continuous medical enrolment for ≥6 months before and ≥12 months after ID. Key endpoints, assessed 3/6/12 months after ID: adherence to treatment (proportion of days covered [PDC; PDC ≥80% considered ‘adherent’], per-group mean±standard deviation [SD] PDC); and persistence (number of continuous therapy days from ID until discontinuation [i.e., >45 days gap] or end of observation period). Patients initiating dulaglutide/liraglutide were propensity score matched (1:1) based on baseline characteristics. Between-group comparison of adherence was analysed using the McNemar test (α=0.025). Persistence was analysed using Kaplan–Meier estimates with log-rank tests (α=0.025) for between-group comparisons. This study presents 12-month outcomes. Results— Following propensity score matching, 263 patients were included in each group. Mean±SD PDC for all patients at 12 months was significantly higher in the dulaglutide versus the liraglutide group (dulaglutide=0.48±0.30, liraglutide=0.39±0.28, p=0.0002). The proportion of adherent patients favored dulaglutide (dulaglutide=20.2%, liraglutide=12.9%, p=0.0302), as did the probability of being adherent to treatment (odds ratio [97.5% CI]: 1.70 [0.99, 2.91]; p=0.03). Proportion of persistent patients also favoured dulaglutide (dulaglutide=15.2%, liraglutide=9.1%, p=0.0528), as did the probability of discontinuing treatment 12 months after ID (p=0.027). Conclusions— Based on the UAE Dubai e-Claims database data, dulaglutide initiators exhibited significantly greater adherence in terms of mean PDC versus liraglutide initiators. The proportion of adherent patients and the probability of being adherent favored the dulaglutide group, as did treatment persistence.

Keywords: adherence, dulaglutide, effectiveness, liraglutide, persistence

Procedia PDF Downloads 126

Authors: B. McGovern, J. F. Luck, A. Gade, I. V. Lake, D. O’Connell, H. C. Cutcliffe, K. P. Shah, E. E. Ginalis, C. M. Lambert, N. Christian, J. R. Kait, A. W. Yu, C. P. Eckersley, C. R. Bass

Abstract:

Mild traumatic brain injury (mTBI) in the form of post-concussive injuries and attention deficit / hyperactivity disorder (ADHD) share similar cognitive impairments, including impaired working memory and executive function. The memory-guided oculomotor task separates working memory and inhibitory components to provide further information on the nature of these deficits in each pathology. Eleven subjects with ADHD, fifteen control subjects, and ten subjects with recent concussive injury were matched on age, gender, and education (all high school-age males). Eye movements were recorded during memory-guided oculomotor tasks with varying delays using EyeLink 1000 (SR Research). The percentage of premature saccades and the latency of correct response are the analyzed measures for response inhibition and working memory, respectively. No significant differences were found in latencies between controls subjects and subjects with ADHD or post-concussive injuries, in accordance with previous studies. Subjects with ADHD and post-concussive injuries both demonstrated a trend of increased percentages of premature saccades compared to control subjects in the same oculomotor task. This trend reached statistical significance between the post-concussive and control groups (p < 0.05). These findings support the primary nature of the executive function deficits in response inhibition in ADHD and mTBI. The interpretation of results is limited by the small sample size and the exploratory nature of the study. Further investigation into oculomotor performance differences in mTBI and ADHD may help in differentiating these pathologies in consequent diagnoses and provide insight into the interaction of these deficits in mTBI.

Keywords: attention deficit / hyperactivity disorder (ADHD), concussion, diagnosis, oculomotor, pediatrics

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Authors: Jaya Shrivastava

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The particle aspect approach is adopted to investigate the trajectories of charged particles in the electromagnetic field of kinetic Alfven wave. Expressions are found for the dispersion relation, growth/damping rate and associated currents in the presence of electron beam in homogenous plasma. Kinetic effects of electrons and ions are included to study kinetic Alfven wave because both are important in the transition region. The plasma parameters appropriate to plasma sheet boundary layer are used. It is found that downward electron beam affects the dispersion relation, growth/damping-rate and associated currents in cold electron limit.

Keywords: magnetospheric physics, plasma waves and instabilities, electron beam, space plasma physics, wave-particle interactions

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Authors: Kim Kennedy, Daren Gibson, Stephanie Flukes, Chandra Diwakarla, Lisa Spalding, Leanne Pilkington, Andrew Redfern

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Introduction: It is well known that patients with Head and Neck Cancer (HNC) are at increased risk of subsequent head and neck cancers due to various aetiologies. Aim: We sought to determine the factors contributing to an increased risk of subsequent HNC primaries, and also to evaluate whether Aboriginal patients are at increased risk. Methods: We performed a retrospective cohort analysis of 320 HNC patients from a single centre in Western Australia, identifying 80 Aboriginal patients and 240 non-Aboriginal patients matched on a 1:3 ratio by site, histology, rurality, and age. We collected patient data including smoking and alcohol consumption, tumour and treatment data, and data on subsequent HNC primaries. Results: A subsequent HNC primary was seen in 37 patients (11.6%) overall. There was no significant difference in the rate of second primary HNCs between Aboriginal patients (12.5%) and nonAboriginal patients (11.2%) (p=0.408). Subsequent HNCs, were strongly associated with smoking and alcohol consumption however, with 95% of patients with a second primary being ever-smokers, and 54% of patients with a second primary having a history of excessive alcohol consumption. In the 37 patients with multiple HNC primaries, there were a total of 57 HNCs, with 29 patients having two primaries, six patients having 3 HNC primaries, one patient with four, and one with six. 54 out of the 57 cancers were in ever smokers (94.7%). There were only two multiple HNC primaries in a never smoker, non-drinker, and these cases were of unknown etiology with HPV/p16 status unknown in both cases. In the whole study population, there were 32 HPV-positive HNCs, and 67 p16-positive HNCs, with only two 2 nd HNCs in a p16-positive case, giving a rate of 3% in the p16+ population, which is actually much lower than the rate of second primaries seen in the overall population (11.6%), and was highest in the p16-negative population (15.7%). This suggests that p16-positivity is not a strong risk factor for subsequent primaries, and in fact p16-negativity appeared to be associated with increased risk, however this data is limited by the large number of patients without documented p16 status (45.3% overall, 12% for oropharyngeal, and 59.6% for oral cavity primaries had unknown p16 status). Summary: Subsequent HNC primaries were strongly associated with smoking and alcohol excess. Second and later HNC primaries did not appear to occur at increased rates in Aboriginal patients compared with non-Aboriginal patients, and p16-positivity did not predict increased risk, however p16-negativity was associated with an increased risk of subsequent HNCs.

Keywords: head and neck cancer, multiple primaries, aboriginal, p16 status, smoking, alcohol

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Ali Bayram, Remzi Yiğiter

Abstract:

Objective: Alzheimer's disease (AD), the most common cause of dementia, is a progressive neurodegenerative disease. At present, diagnosis of AD is rather late in the disease. Therefore, we attempted to find peripheral biomarkers for the early diagnosis of AD. Herein, we conducted a study to investigate the unc-51 like autophagy activating kinase-1 (ULK1) mRNA expression levels in human peripheral blood mononuclear cells from patients with Alzheimer's disease. Method: To determine whether ULK1 gene expression are altered in AD patients, we measured their gene expression in human peripheral blood cell in 50 patients with AD and 50 age and gender matched healthy controls by quantitative real-time PCR technique. Results: We found that both ULK1 gene expression in peripheral blood cell were significantly decreased in patients with AD as compared with controls (p <0.05). Lower levels of ULK1 gene expression were significantly associated with the increased risk for AD. Conclusions: Serine/threonine-protein kinase involved in autophagy in response to starvation. Acts upstream of phosphatidylinositol 3-kinase PIK3C3 to regulate the formation of autophagophores, the precursors of autophagosomes. Part of regulatory feedback loops in autophagy: acts both as a downstream effector and negative regulator of mammalian target of rapamycin complex 1 (mTORC1) via interaction with RPTOR. Activated via phosphorylation by AMPK and also acts as a regulator of AMPK by mediating phosphorylation of AMPK subunits PRKAA1, PRKAB2, and PRKAG1, leading to negatively regulate AMPK activity. May phosphorylate ATG13/KIAA0652 and RPTOR; however such data need additional evidences. Plays a role early in neuronal differentiation and is required for granule cell axon formation. Alzheimer is the most common neurodegenerative disease. Our results provide useful information that the ULK1 gene expression is decreased in the neurodegeneration and AD patients with, indicating their possible systemic involvement in AD.

Keywords: Alzheimer’s sisease, ULK1, mRNA expression, RT-PCR

Procedia PDF Downloads 398