Search results for: deep seated gravitational slope deformation

Authors: Judith Partouche-Sebban, Saeedeh Rezaee Vessal

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Because of the rising number of new cases of cancer, especially among women, it is more than essential to better understand how women experience cancer in order to bring them adapted to support and care and enhance their well-being and patient experience. Cancer stands for a traumatic experience in which the diagnosis, its medical treatments, and the related side effects lead to deep physical and psychological changes that may arouse considerable stress and anxiety. In order to reduce these negative emotions, women tend to use various defense mechanisms, among which denial has been defined as the most frequent mechanism used by breast cancer patients. This study aims to better understand the consequences of the experience of cancer and their link with the adoption of a denial strategy. The empirical research was done among female cancer survivors in France. Since the topic of this study is relatively unexplored, a qualitative methodology and open-ended interviews were employed. In total, 25 semi-directive interviews were conducted with a female with different cancers, different stages of treatment, and different ages. A systematic inductive method was performed to analyze data. The content analysis enabled to highlight three different denial-related behaviors among women with cancer, which serve a self-protective function. First, women who expressed high levels of anxiety confessed they tended to completely deny the existence of their cancer immediately after the diagnosis of their illness. These women mainly exhibit many fears and a deep distrust toward the medical context and professionals. This coping mechanism is defined by the patient as being unconscious. Second, other women deliberately decided to deny partial information about their cancer, whether this information is related to the stages of the illness, the emotional consequences, or the behavioral consequences of the illness. These women use this strategy as a way to avoid the reality of the illness and its impact on the different aspects of their life as if cancer does not exist. Third, some women tend to reinterpret and give meaning to their cancer as a way to reduce its impact on their life. To this end, they may use magical thinking or positive reframing, or reinterpretation. Because denial may lead to delays in medical treatments, this topic deserves a deep investigation, especially in the context of oncology. As denial is defined as a specific defense mechanism, this study contributes to the existing literature in service marketing which focuses on emotions and emotional regulation in healthcare services which is a crucial issue. Moreover, this study has several managerial implications for healthcare professionals who interact with patients in order to implement better care and support for the patients.

Keywords: cancer, coping mechanisms, denial, healthcare services

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Authors: Umut Yıldız, Berkin Kurtuluş, Yunus Emre Muslubaş

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Thrust vectoring, especially in military aviation, is a concept that sees much use to improve maneuverability in already agile aircraft. As this concept is fairly new and cost intensive to design and test, computational methods are useful in easing the preliminary design process. Computational Fluid Dynamics (CFD) can be utilized in many forms to simulate nozzle flow, and there exist various CFD studies in both 2D mechanical and 3D injection based thrust vectoring, and yet, 3D mechanical thrust vectoring analyses, at this point in time, are lacking variety. Additionally, the freely available test data is constrained to limited pitch angles and geometries. In this study, based on a test case provided by NASA, both steady and unsteady 3D CFD simulations are conducted to examine the aerodynamic performance of a mechanical thrust vectoring nozzle model and to validate the utilized numerical model. Steady analyses are performed to verify the flow characteristics of the nozzle at pitch angles of 0, 10 and 20 degrees, and the results are compared with experimental data. It is observed that the pressure data obtained on the inner surface of the nozzle at each specified pitch angle and under different flow conditions with pressure ratios of 1.5, 2 and 4, as well as at azimuthal angle of 0, 45, 90, 135, and 180 degrees exhibited a high level of agreement with the corresponding experimental results. To validate the CFD model, the insights from the steady analyses are utilized, followed by unsteady analyses covering a wide range of pitch angles from 0 to 20 degrees. Throughout the simulations, a mesh morphing method using a carefully calculated mathematical shape deformation model that simulates the vectored nozzle shape exactly at each point of its travel is employed to dynamically alter the divergent part of the nozzle over time within this pitch angle range. The mesh morphing based vectored nozzle shapes were compared with the drawings provided by NASA, ensuring a complete match was achieved. This computational approach allowed for the creation of a comprehensive database of results without the need to generate separate solution domains. The database contains results at every 0.01° increment of nozzle pitch angle. The unsteady analyses, generated using the morphing method, are found to be in excellent agreement with experimental data, further confirming the accuracy of the CFD model.

Keywords: thrust vectoring, computational fluid dynamics, 3d mesh morphing, mathematical shape deformation model

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Abdullah Salameh, Jamil Renno, Khaled Ali

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This paper investigates how fatigue life is influenced by increasing the number and size of bolts under several wind speed averages. The study determined that increasing the size or number of bolts can notably improve the fatigue life of bolted flange connections. Additionally, the curves derived from the assessment data demonstrated a steeper slope for a greater number of bolts, indicating that the percentage increase of adding bolts is not consistent for each additional bolt. Instead, the percentage increment rises exponentially when increasing the number of bolts. However, selecting the most suitable design improvement strategy depends on the specific circumstances. In the majority of cases, the study observed that increasing the number of bolts resulted in significant improvements in fatigue life, regardless of the size of the bolts used.

Keywords: wind turbine tower, flanged connection, number of bolts, size of bolts, fatigue life

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Authors: F. Adjel, C. Bensmail, S. Almi, D. Barkat

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The synergistic solvent extraction of cobalt (II) from 0.33 mol dm^-3 Na2SO4 aqueous solutions with capric acid (HL) in the absence and presence of methyl isobutyl cétone (MIBK) in chloroform at 25°C, has been studied. The extracted species when the capric acid compound was used alone, is CoL2(HL)2. In the presence of MIBK, a remarkable enhancement on the extraction of nickel (II) with 0.02 mol dm^-3 capric acid was observed upon the addition of 0.0025 to 0.01 mol dm^-3 MIBK in chloroform. From a synergistic extraction-equilibrium study, the synergistic enhancement was ascribed to the adduct formation CoL2(HL)2 n(MIBK). The MIBK-HL interaction strongly influences the synergistic extraction efficiency. The synergistic extraction stoichiometry of cobalt (II) with capric acid and MIBK is studied with the methods of slope analysis. The equilibrium constants were determined.

Keywords: solvent extraction, cobalt (II), capric acid, MIBK, synergism

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Authors: A. Ikusika, O. I. Poppola

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This study was carried out to delineate possible structures that may favour the accumulation of tantalite, a magnetic mineral. A ground based technique was employed using proton precision magnetometer G-856 AX. A total of ten geophysical traverses were established in the study area. The acquired magnetic field data were corrected for drift. The trend analysis was adopted to remove the regional gradient from the observed data and the resulting results were presented as profiles. Quantitative interpretation only was adopted to obtain the depth to basement using Peter half slope method. From the geological setting of the area and the information obtained from the magnetic survey, a conclusion can be made that the study area is underlain by a rock unit of accumulated minerals. It is therefore suspected that the overburden is relatively thin within the study area and the metallic minerals are in disseminated quantity and at a shallow depth.

Keywords: basement, drift, magnetic field data, tantalite, traverses

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Yu-Hsien Lin, Yu-Ru Lin, Yung-Chun Wu

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This work demonstrates the ultra-thin poly-Si (polycrystalline Silicon) nanowire junctionless thin film transistors (NWs JL-TFT) with nickel silicide contact. For nickel silicide film, this work designs to use two-step annealing to form ultra-thin, uniform and low sheet resistance (Rs) Ni silicide film. The NWs JL-TFT with nickel silicide contact exhibits the good electrical properties, including high driving current (>10⁷ Å), subthreshold slope (186 mV/dec.), and low parasitic resistance. In addition, this work also compares the electrical characteristics of NWs JL-TFT with nickel silicide and non-silicide contact. Nickel silicide techniques are widely used for high-performance devices as the device scaling due to the source/drain sheet resistance issue. Therefore, the self-aligned silicide (salicide) technique is presented to reduce the series resistance of the device. Nickel silicide has several advantages including low-temperature process, low silicon consumption, no bridging failure property, smaller mechanical stress, and smaller contact resistance. The junctionless thin-film transistor (JL-TFT) is fabricated simply by heavily doping the channel and source/drain (S/D) regions simultaneously. Owing to the special doping profile, JL-TFT has some advantages such as lower thermal the budget which can integrate with high-k/metal-gate easier than conventional MOSFETs (Metal Oxide Semiconductor Field-Effect Transistors), longer effective channel length than conventional MOSFETs, and avoidance of complicated source/drain engineering. To solve JL-TFT has turn-off problem, JL-TFT needs ultra-thin body (UTB) structure to reach fully depleted channel region in off-state. On the other hand, the drive current (Iᴅ) is declined as transistor features are scaled. Therefore, this work demonstrates ultra thin poly-Si nanowire junctionless thin film transistors with nickel silicide contact. This work investigates the low-temperature formation of nickel silicide layer by physical-chemical deposition (PVD) of a 15nm Ni layer on the poly-Si substrate. Notably, this work designs to use two-step annealing to form ultrathin, uniform and low sheet resistance (Rs) Ni silicide film. The first step was promoted Ni diffusion through a thin interfacial amorphous layer. Then, the unreacted metal was lifted off after the first step. The second step was annealing for lower sheet resistance and firmly merged the phase.The ultra-thin poly-Si nanowire junctionless thin film transistors NWs JL-TFT with nickel silicide contact is demonstrated, which reveals high driving current (>10⁷ Å), subthreshold slope (186 mV/dec.), and low parasitic resistance. In silicide film analysis, the second step of annealing was applied to form lower sheet resistance and firmly merge the phase silicide film. In short, the NWs JL-TFT with nickel silicide contact has exhibited a competitive short-channel behavior and improved drive current.

Keywords: poly-Si, nanowire, junctionless, thin-film transistors, nickel silicide

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Authors: William C. Alexander

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The oscillating water column (OSC) wave energy converter (WEC) with deep water reactance (DWR) consists of a large hollow sphere filled with seawater at the base, referred to as the ‘stabilizer’, a hollow cylinder at the top of the device, with a said cylinder having a bottom open to the sea and a sealed top save for an orifice which leads to an air turbine, and a long, narrow rod connecting said stabilizer with said cylinder. A small amount of ballast at the bottom of the stabilizer and a small amount of floatation in the cylinder keeps the device upright in the sea. The floatation is set such that the mean water level is nominally halfway up the cylinder. The entire device is loosely moored to the seabed to keep it from drifting away. In the presence of ocean waves, seawater will move up and down within the cylinder, producing the ‘oscillating water column’. This gives rise to air pressure within the cylinder alternating between positive and negative gauge pressure, which in turn causes air to alternately leave and enter the cylinder through said top-cover situated orifice. An air turbine situated within or immediately adjacent to said orifice converts the oscillating airflow into electric power for transport to shore or elsewhere by electric power cable. Said oscillating air pressure produces large up and down forces on the cylinder. Said large forces are opposed through the rod to the large mass of water retained within the stabilizer, which is located deep enough to be mostly free of any wave influence and which provides the deepwater reactance. The cylinder and stabilizer form a spring-mass system which has a vertical (heave) resonant frequency. The diameter of the cylinder largely determines the power rating of the device, while the size (and water mass within) of the stabilizer determines said resonant frequency. Said frequency is chosen to be on the lower end of the wave frequency spectrum to maximize the average power output of the device over a large span of time (such as a year). The upper portion of the device (the cylinder) moves laterally (surge) with the waves. This motion is accommodated with minimal loading on the said rod by having the stabilizer shaped like a sphere, allowing the entire device to rotate about the center of the stabilizer without rotating the seawater within the stabilizer. A full-scale device of this type may have the following dimensions. The cylinder may be 16 meters in diameter and 30 meters high, the stabilizer 25 meters in diameter, and the rod 55 meters long. Simulations predict that this will produce 1,400 kW in waves of 3.5-meter height and 12 second period, with a relatively flat power curve between 5 and 16 second wave periods, as will be suitable for an open-ocean location. This is nominally 10 times higher power than similar-sized WEC spar buoys as reported in the literature, and the device is projected to have only 5% of the mass per unit power of other OWC converters.

Keywords: oscillating water column, wave energy converter, spar bouy, stabilizer

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: F. Sabri, J. Jamali

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In this article, the wrinkling failure of orthotropic composite membranes due to in-plane shear deformation is investigated using nonlinear finite element analyses. A nonlinear post-buckling analysis is performed to show the evolution of shear-induced wrinkles. The method of investigation is based on the post-buckling finite element analysis adopted from commercial FEM code; ANSYS. The resulting wrinkling patterns, their amplitude and their wavelengths under the prescribed loads and boundary conditions were confirmed by experimental results. Our study reveals that wrinkles develop when both the magnitudes and coverage of the minimum principal stresses in the laminated composite laminates are sufficiently large to trigger wrinkling.

Keywords: composite, FEM, membrane, wrinkling

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Authors: Aziza Barrek, Mohamed Haythem Msaddek, Ismail Chenini

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Excessive groundwater pumping due to the increasing water demand, especially in the agricultural sector, causes groundwater scarcity. Groundwater recharge is the most important process that contributes to the water's durability. This paper is based on the Analytic Hierarchy Process multicriteria analysis to establish a groundwater recharge susceptibility map. To delineate aquifer suitability for groundwater recharge, eight parameters were used: soil type, land cover, drainage density, lithology, NDVI, slope, transmissivity, and rainfall. The impact of each factor was weighted. This method was applied to the El Fahs plain shallow aquifer. Results suggest that 37% of the aquifer area has very good and good recharge suitability. The results have been validated by the Receiver Operating Characteristics curve. The accuracy of the prediction obtained was 89.3%.

Keywords: AHP, El Fahs aquifer, empirical formula, groundwater recharge zone, remote sensing, semi-arid region

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Authors: M. Z. Ramli, A. Adnan, Chee Wei Tan

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To gain a better understanding of earthquake forces on reinforced concrete bridge piers with different bearing condition, a series of experiments was conducted on a realistic, 1:4 scale reinforced concrete bridge pier. The normal practices of laminated seismic isolation bearing is compared with the new design spring seismic isolation bearing where invented by Engineering Seismology and Earthquake Engineering Research (e-SEER), Universiti Teknologi Malaysia. The nonlinear behavior of piers is modeled using the fibre beam theory to verify the experimental works. The hysteresis of bridge pier with different bearing condition was illustrated under different Peak Ground Acceleration (PGAs). The average slope of the hysteresis respectively to the global stiffness was also investigated.

Keywords: bridge, laminated seismic isolation, spring seismic isolation, Peak Ground Acceleration, stiffness

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Authors: Odunjo Oluronke Omolola, Okanlawon Simon Ayorinde

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This study examines the relationship between house characteristics and socio-economic characteristics of developers in Ibadan, southwest, Nigeria. The research is borne out of the fact that social housing has not done much as a result of finance and housing poverty is on the increase in the country. Multistage random sampling was used in selecting 2,646 respondents in the area. The questionnaire forms the basic instrument for data collection and was administered to heads of households to collect information on socio-economic and demographic characteristics as well as characteristics of development. Both descriptive and inferential statistical analyses were employed in the presentation of the findings; MANOVA was used to analyse the relationship between house characteristics measured by wall materials (Y1-Yn) and socio-economic characteristics of developers measured by gender (X1), religion (X2), educational background (X3) and employment status (X4).The study found out that the bulk of the respondents (65.7%) were male, while 51.7% practiced Christianity. Also, 35.9% had HND/1st/Postgraduate degree, while 43.9% were self employed; Most households however, had membership size of 5 (26.9%). The significant wall material in the area was sandcrete block (71.2%) as opposed to mud (19.1%) and brick (0.6%). Multiple Analysis of Variance shows that there is a significant relationship between sandcrete block and each of gender (X1) and employment status (X3). The factor adduced to this is accessibility to cooperative societies which serve as the gravitational force of attraction for housing finance. The study suggests among others that, there should be re-invigoration of existing cooperative societies, while more should be established for the provision of housing finance.

Keywords: relationship, house development, developers, sandcrete block, cooperative societies

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Authors: Mesfin Angaw Tesfay

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L-Xylulose is a potentially valuable rare sugar used as starting material for antiviral and anticancer drug development in pharmaceutical industries. L-Xylulose exist in a very low concentration in nature and have to be synthesized from cheap starting materials such as xylitol through biotechnological approaches. In this study, cofactor engineering and deep eutectic solvent were applied to improve the efficiency of L-xylulose production from xylitol. A water-forming NAD+ regeneration enzyme (NADH oxidase) from Streptococcus mutans ATCC 25175 was introduced into E. coli with xylitol-4-dehydrogenase (XDH) of Pantoea ananatis resulting in recombinant cells harboring the vector pETDuet-xdh-SmNox. Further, three deep eutectic solvents (DES) including, Choline chloride/glycerol (ChCl/G), Choline chloride/urea (ChCl/U), and Choline chloride/ethylene glycol (ChCl/EG) have been employed to facilitate the conversion efficiency of L-xylulose from xylitol. The co-expression system exhibited optimal activity at a temperature of 37 ℃ and pH 8.5, and the addition of Mg2+ enhanced the catalytic activity by 1.19-fold. Co-expression of NADH oxidase with XDH enzyme resulted in increased L-xylulose concentration and productivity from xylitol as well as the intracellular NAD+ concentration. Two of the DES used (ChCl/U and ChCl/EG) show positive effects on product yield and the ChCl/G has inhibiting effects. The optimum concentration of ChCl/U was 2.5%, which increased the L-xylulose yields compared to the control without DES. In a 1 L fermenter the final concentration and productivity of L-xylulose from 50 g/L of xylitol reached 48.45 g/L, and 2.42 g/L.h respectively, which was the highest report. Overall, this study is a suitable approach for large-scale production of L-xylulose from xylitol using the engineered E. coli cell.

Keywords: Xylitol-4-dehydrogenase, NADH oxidase, L-xylulose, Xylitol, Coexpression, DESs

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: Mohamad H. Atyeh, Wael Alrashed, Steven Telford

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The main objective of this research is to measure if there have been any notable changes in the trading actives of the Kuwait stock Exchange (KSE) after the privatization process that took place on the 25th of April 2016. The data that are used to test if there is any change in the KSE market performance are the daily indices for the period from the 25th of April 2016 till the 24th of October 2016 (after privatization) and a similar six months period before the date of the privatization from the 24th of October 2015 till the 24th of April 2016. In addition, as a control, the study included a period that is a period parallel to the six months period after the privatization. The results indicate that privatization is associated with lower variability for the majority of variables, but that the observed switch in slope direction is not actually a product of privatization, but rather one of serial correlation.

Keywords: privatization, Kuwait stock exchange (KSE), market capitalization (MCAP), capital markets authority (CMA), Boursa Kuwait securities company (BKSC)

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Authors: Ehsan Mehryaar

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The rate of penetration is (RoP) one of the vital factors in the cost and time of tunnel boring projects; therefore, predicting it can lead to a substantial increase in the efficiency of the project. RoP is heavily dependent geological properties of the project site and TBM properties. In this study, 151-point data from Queen’s water tunnel is collected, which includes unconfined compression strength, peak slope index, angle with weak planes, and distance between planes of weaknesses. Since the size of the data is small, it was observed that it is imbalanced. To solve that problem synthetic minority oversampling technique is utilized. The model based on the model tree is proposed, where each leaf consists of a support vector machine model. Proposed model performance is then compared to existing empirical equations in the literature.

Keywords: Model tree, SMOTE, rate of penetration, TBM(tunnel boring machine), SVM

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Su Yi Ming, Hou Ying, Zou Guang Ping

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Metal-net rubber is a new dry friction damping material, compared with the traditional metal rubber, which has high mechanization degree, and the mechanical performance of metal-net rubber is more stable. Through the sine sweep experiment and random vibration experiment of metal-net rubber vibration isolator, the influence of several important factors such as the lines slope, relative density and wire diameter on the transfer rate, natural frequency and root-mean-square response acceleration of metal-net rubber vibration isolation system, were studied through the method of control variables. Also, several relevant change curves under different vibration levels were derived, and the effects of vibration level on the natural frequency and root-mean-square response acceleration were analyzed through the curves.

Keywords: metal-net rubber vibration isolator, relative density, vibration level, wire diameter

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Authors: Yuexi Xiong, Jingwu He

Abstract:

The structural components in an energy facility such as steam turbine machines are operated under high stress and elevated temperature in an endured time period and thus the creep deformation and creep rupture failure are important issues that need to be addressed in the design of such components. There are numerous creep models being used for creep analysis that have both advantages and disadvantages in terms of accuracy and efficiency. The Isochronous Creep Analysis is one of the simplified approaches in which a full-time dependent creep analysis is avoided and instead an elastic-plastic analysis is conducted at each time point. This approach has been established based on the rupture dependent creep equations using the well-known Larson-Miller parameter. In this paper, some fundamental aspects of creep deformation and the rupture dependent creep models are reviewed and the analysis procedures using isochronous creep curves are discussed. Four rupture failure criteria are examined from creep fundamental perspectives including criteria of Stress Damage, Strain Damage, Strain Rate Damage, and Strain Capability. The accuracy of these criteria in predicting creep life is discussed and applications of the creep analysis procedures and failure predictions of simple models will be presented. In addition, a new failure criterion is proposed to improve the accuracy and effectiveness of the existing criteria. Comparisons are made between the existing criteria and the new one using several examples materials. Both strain increase and stress relaxation form a full picture of the creep behaviour of a material under high temperature in an endured time period. It is important to bear this in mind when dealing with creep problems. Accordingly there are two sets of rupture dependent creep equations. While the rupture strength vs LMP equation shows how the rupture time depends on the stress level under load controlled condition, the strain rate vs rupture time equation reflects how the rupture time behaves under strain-controlled condition. Among the four existing failure criteria for rupture life predictions, the Stress Damage and Strain Damage Criteria provide the most conservative and non-conservative predictions, respectively. The Strain Rate and Strain Capability Criteria provide predictions in between that are believed to be more accurate because the strain rate and strain capability are more determined quantities than stress to reflect the creep rupture behaviour. A modified Strain Capability Criterion is proposed making use of the two sets of creep equations and therefore is considered to be more accurate than the original Strain Capability Criterion.

Keywords: creep analysis, high temperature mateials, rapture evalution, steam turbine machines

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Authors: Amrit Ladhani

Abstract:

The cyclic universe theory is a model of cosmic evolution according to which the universe undergoes endless cycles of expansion and cooling, each beginning with a “big bang” and ending in a “big crunch”. In this paper, we propose a unique property of Space-time. This particular and marvelous nature of space shows us that space can stretch, expand, and shrink. This property of space is caused by the size of the universe change over time: growing or shrinking. The observed accelerated expansion, which relates to the stretching of Shrunk space for the new theory, is derived. This theory is based on three underlying notions: First, the Big Bang is not the beginning of Space-time, but rather, at the very beginning fraction of a second, there was an infinite force of infinite Shrunk space in the cosmic singularity that force gave rise to the big bang and caused the rapidly growing of space, and all other forms of energy are transformed into new matter and radiation and a new period of expansion and cooling begins. Second, there was a previous phase leading up to it, with multiple cycles of contraction and expansion that repeat indefinitely. Third, the two principal long-range forces are the gravitational force and the repulsive force generated by shrink space. They are the two most fundamental quantities in the universe that govern cosmic evolution. They may provide the clockwork mechanism that operates our eternal cyclic universe. The universe will not continue to expand forever; no need, however, for dark energy and dark matter. This new model of Space-time and its unique properties enables us to describe a sequence of events from the Big Bang to the Big Crunch.

Keywords: dark matter, dark energy, cosmology, big bang and big crunch

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Authors: I. Hossain, Huda H. Kassim, Fadhil I. Sharrad, Said A. Mansour

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In this conference, we apply the interacting boson model-1 (IBM-1) formula for U(5) symmetry in order to calculate the energy levels and reduced transition probabilities for a few yrast transitions in Ru with neutron N=60, 62. The neutron rich even-even isotopes of Ru are very interesting to investigate using IBM-1, because even 104,106Ru isotopes are great consequence due to excited near the magic number 50. The calculation of ground state band and B(E2) values using IBM-1 for Z=44 are not calculated to describe the valuable information of nuclear structure by U(5) limit. The parameters in the formula are deduced based on the experimental energy level and value of B(E2, 2+->0+). The yrast states and transition strength B(E2) from 1st 4+ to 1st 2+, 1st 6+ to 1st 4+ and 1st 8+ to 1st 6+ states of Ru for even N= 60, 62 were calculated. The quadrupole moments, deformation parameters and U(5) limit were discussed for those nuclei.

Keywords: B(E2), energy level, ¹⁰⁴Ru, ¹⁰⁶Ru

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Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

Abstract:

Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

Abstract:

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: Praveen Kumar, Nitin Kumar, Hemant Kumar

Abstract:

The modernization of computer technology and commercial computational fluid dynamic (CFD) simulation has given better detailed results as compared to experimental investigation techniques. CFD techniques are widely used in different field due to its flexibility and performance. Evaluation of pipeline erosion is complex phenomenon to solve by numerical arithmetic technique, whereas CFD simulation is an easy tool to resolve that type of problem. Erosion wear behaviour due to solid–liquid mixture in the slurry pipeline has been investigated using commercial CFD code in FLUENT. Multi-phase Euler-Lagrange model was adopted to predict the solid particle erosion wear in 22.5° pipe bend for the flow of bottom ash-water suspension. The present study addresses erosion prediction in three dimensional 22.5° pipe bend for two-phase (solid and liquid) flow using finite volume method with standard k-ε turbulence, discrete phase model and evaluation of erosion wear rate with varying velocity 2-4 m/s. The result shows that velocity of solid-liquid mixture found to be highly dominating parameter as compared to solid concentration, density, and particle size. At low velocity, settling takes place in the pipe bend due to low inertia and gravitational effect on solid particulate which leads to high erosion at bottom side of pipeline.

Keywords: computational fluid dynamics (CFD), erosion, slurry transportation, k-ε Model

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

Abstract:

The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

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Authors: Satoko Fujiwara, Kiyotaka Obunai, Kazuya Okubo

Abstract:

A skew is one of important problems for designing the conveyor and transmission with frictional flat belt, in which running belt is deviated in width direction due to the transverse force applied to the belt. The skew often not only degrades the stability of the path of belt but also causes some damages of the belt and auxiliary machines. However, the transverse behavior such as the skew has not been discussed quantitatively in detail for frictional belts. The objective of this study is to clarify the transverse behavior of frictional flat belt driven by tapered pulley. Commercially available rubber flat belt reinforced by polyamide film was prepared as the test belt where the thickness and length were 1.25 mm and 630 mm, respectively. Test belt was driven between two pulleys made of aluminum alloy, where diameter and inter-axial length were 50 mm and 150 mm, respectively. Some tapered pulleys were applied where tapered angles were 0 deg (for comparison), 2 deg, 4 deg, and 6 deg. In order to alternatively investigate the transverse behavior, the transverse force applied to the belt was measured when the skew was constrained at the string under driving state. The transverse force was measured by a load cell having free rollers contacting on the side surface of the belt when the displacement in the belt width direction was constrained. The conditions of observed bending stiffness in-plane of the belt were changed by preparing three types of belts (the width of the belt was 20, 30, and 40 mm) where their observed stiffnesses were changed. The contributions of the bending stiffness in-plane of belt and initial inter-axial force to the transverse were discussed in experiments. The inter-axial force was also changed by setting a distance (about 240 mm) between the two pulleys. Influence of observed bending stiffness in-plane of the belt and initial inter-axial force on the transverse force were investigated. The experimental results showed that the transverse force was increased with an increase of observed bending stiffness in-plane of the belt and initial inter-axial force. The transverse force acting on the belt running on the tapered pulley was classified into multiple components. Those were components of forces applied with the deflection of the inter-axial force according to the change of taper angle, the resultant force by the bending moment applied on the belt winding around the tapered pulley, and the reaction force applied due to the shearing deformation. The calculation result of the transverse force was almost agreed with experimental data when those components were formulated. It was also shown that the most contribution was specified to be the shearing deformation, regardless of the test conditions. This study found that transverse behavior of frictional flat belt driven by tapered pulley was explained by the summation of those components of forces.

Keywords: skew, frictional flat belt, transverse force, tapered pulley

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Authors: N. Reppas, Y. L. Gui, B. Wetenhall, C. T. Davie, J. Ma

Abstract:

A theoretical constitutive model describing the stress-strain behavior of rock subjected to different confining pressures is presented. A bounding surface plastic model with hardening effects is proposed which includes the effect of temperature drop. The bounding surface is based on a mapping rule and the temperature effect on rock is controlled by Poisson’s ratio. Validation of the results against available experimental data is also presented. The relation of deviatoric stress and axial strain is illustrated at different temperatures to analyze the effect of temperature decrease in terms of stiffness of the material.

Keywords: bounding surface, cooling of rock, plasticity model, rock deformation, elasto-plastic behavior

Procedia PDF Downloads 128