Search results for: strength prediction

Authors: Mohammad W. Dewan, Jahangir Alam, Khurshida Sharmin

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Synthetic fibers (carbon, glass, aramid, etc.) are generally utilized to make composite materials for better mechanical and thermal properties. However, they are expensive and non-biodegradable. In the perspective of Bangladesh, jute fibers are available, inexpensive, and comprising good mechanical properties. The improved properties (i.e., low cost, low density, eco-friendly) of natural fibers have made them a promising reinforcement in hybrid composites without sacrificing mechanical properties. In this study, jute and e-glass fiber reinforced hybrid composite materials are fabricated utilizing hand lay-up followed by a compression molding technique. Room temperature cured two-part epoxy resin is used as a matrix. Approximate 6-7 mm thick composite panels are fabricated utilizing 17 layers of woven glass and jute fibers with different fiber layering sequences- only jute, only glass, glass, and jute alternatively (g/j/g/j---) and 4 glass - 9 jute – 4 glass (4g-9j-4g). The fabricated composite panels are analyzed through fiber volume calculation, tensile test, bending test, and water absorption test. The hybridization of jute and glass fiber results in better tensile, bending, and water absorption properties than only jute fiber-reinforced composites, but inferior properties as compared to only glass fiber reinforced composites. Among different fiber layering sequences, 4g-9j-4g fibers layering sequence resulted in better tensile, bending, and water absorption properties. The effect of chemical treatment on the woven jute fiber and chopped glass microfiber infusion are also investigated in this study. Chemically treated jute fiber and 2 wt. % chopped glass microfiber infused hybrid composite shows about 12% improvements in flexural strength as compared to untreated and no micro-fiber infused hybrid composite panel. However, fiber chemical treatment and micro-filler do not have a significant effect on tensile strength.

Keywords: compression molding, chemical treatment, hybrid composites, mechanical properties

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Authors: Nurlaila Effendy

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Companies in Asia face a number of constraints in tight competitiveness in ASEAN Economic Community 2015 and globalization. An economic capitalism system as an integral part of globalization processing brings broad impacts. They need to improve business performance in globalization and ASEAN Economic Community. Organizational development has quite clearly demonstrated that aligning individual’s personal goals with the goals of the organization translates into measurable and sustained performance improvement. Human capital is a key to achieve company performance. Employee Engagement (EE) creates and expresses themselves physically, cognitively and emotionally to achieve company goals and individual goals. One will experience a total involvement when they undertake their jobs and feel a self integration to their job and organization. A leader plays key role in attaining the goals and objectives of a company/organization. Any Manager in a company needs to have leadership competence and global mindset. As one the of positive organizational behavior developments, psychological capital (PsyCap) is assumed to be one of the most important capitals in the global mindset, in addition to intellectual capital and social capital. Textile companies also need to face a number of constraints in tight competitiveness in regional and global. This research involved 42 managers in two textiles and a spinning companies in a group, in Central Java, Indonesia. It is a quantitative research with Partial Least Squares (PLS) studying job resource (Social Support & Organizational Climate) and Personal Resource (4 dimensions of Psychological Capital & Leadership Competence) as prediction of Employee Engagement, also Employee Engagement and leadership competence as prediction of leader’s performance. The performance of a leader is measured by means of achievement on objective strategies in terms of 4 perspectives (financial and non-financial perspectives) in a Balanced Score Card (BSC). It took one year during a business plan of year 2014, from January to December 2014. The result of this research is there is correlation between Job Resource (coefficient value of Social Support is 0.036 & coefficient value of organizational climate is 0.220) and Personal Resource (coefficient value of PsyCap is 0.513 & coefficient value of Leadership Competence is 0.249) with employee engagement. There is correlation between employee engagement (coefficient value is 0.279) and leadership competence (coefficient value is 0.581) with performance.

Keywords: organizational climate, social support, psychological capital leadership competence, employee engagement, performance, integrated textile companies

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Authors: R. Andreola, O. A. A. Santos, L. M. M. Jorge

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An industrial system for the production of white liquor of a paper industry, Klabin Paraná Papé is, formed by ten reactors was modeled, simulated, and analyzed. The developed model considered possible water losses by evaporation and reaction, in addition to variations in volumetric flow of lime mud across the reactors due to composition variations. The model predictions agreed well with the process measurements at the plant and the results showed that the slaking reaction is nearly complete at the third causticizing reactor, while causticizing ends by the seventh reactor. Water loss due to slaking reaction and evaporation occurs more pronouncedly in the slaking reaction than in the final causticizing reactors; nevertheless, the lime mud flow remains nearly constant across the reactors.

Keywords: causticizing, lime, prediction, process

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Authors: Sotirios Longinos, Anna Loskutova, Assel Tolegenova, Assem Imanzhussip, Lei Wang

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Hot, dry rock (HDR) has substantial potential as a thermal energy source. It has been exploited by hydraulic fracturing to extract heat and generate electricity, which is a well-developed technique known for creating the enhanced geothermal systems (EGS). These days, LN2 is being tested as an environmental friendly fracturing fluid to generate densely interconnected crevices to augment heat exchange efficiency and production. This study examines experimentally the efficacy of LN2 cryogenic fracturing for granite samples in Kazakhstan with immersion method. A comparison of two different experimental models is carried out. The first mode is rock heating along with liquid nitrogen treatment (heating with freezing time), and the second mode is multiple times of heating along with liquid nitrogen treatment (heating with LN2 freezing-thawing cycles). The experimental results indicated that with multiple heating and LN2-treatment cycles, the permeability of granite first ameliorates with increasing number of cycles and later reaches a plateau after a certain number of cycles. On the other hand, density, P-wave velocity, uniaxial compressive strength, elastic modulus, and tensile strength indicate a downward trend with increasing heating and treatment cycles. The thermal treatment cycles do not seem to have an obvious effect on the Poisson’s ratio. The changing rate of granite rock properties decreases as the number of cycles increases. The deterioration of granite primarily happens within the early few cycles. The heating temperature during the cycles shows an important influence on the deterioration of granite. More specifically, mechanical deterioration and permeability amelioration become more remarkable as the heating temperature increases.LN2 fracturing generates many positives compared to conventional fracturing methods such as little water consumption, requirement of zero chemical additives, lessening of reservoir damage, and so forth. Based on the experimental observations, LN2 can work as a promising waterless fracturing fluid to stimulate hot, dry rock reservoirs.

Keywords: granite, hydraulic fracturing, liquid nitrogen, Kazakhstan

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Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Busra Mutlu Ipek, Merve Mutlu, Ahmet Mutlu

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Novel coronavirus COVID-19, which has recently influenced the world, poses a great threat to humanity. In order to overcome this challenging situation, scientists are working on developing effective vaccine against coronavirus. Many experts and researchers have also produced articles and done studies on this highly important subject. In this direction, this special topic was chosen for article to make a contribution to this area. The purpose of this article is to perform RNA sequence analysis of selected virus forms in the Coronaviridae family and predict/classify SARS-CoV-2 (COVID-19) from other selected complete genomes in coronaviridae family using proposed Artificial Neural Network(ANN) algorithm.

Keywords: Coronaviridae family, COVID-19, RNA sequencing, ANN, neural network

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Authors: Lívia B. Meirelles, Erika C. A. N. Chrisman, Flávia B. de Andrade, Lilian C. M. de Oliveira

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True Boiling Point distillation (TBP) is one of the most common experimental techniques for the determination of petroleum properties. This curve provides information about the performance of petroleum in terms of its cuts. The experiment is performed in a few days. Techniques are used to determine the properties faster with a software that calculates the distillation curve when a little information about crude oil is known. In order to evaluate the accuracy of distillation curve prediction, eight points of the TBP curve and specific gravity curve (348 K and 523 K) were inserted into the HYSYS Oil Manager, and the extended curve was evaluated up to 748 K. The methods were able to predict the curve with the accuracy of 0.6%-9.2% error (Software X ASTM), 0.2%-5.1% error (Software X Spaltrohr).

Keywords: distillation curve, petroleum distillation, simulation, true boiling point curve

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Authors: Deepak Nathiya1, Ramesh Roop Rai, Pratima Singh1, Preeti Raj1, Supriya Suman, Balvir Singh Tomar

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Background: Sarcopenia is a catabolic state in liver cirrhosis (LC), accelerated with a breakdown of skeletal muscle to release amino acids which adversely affects survival, health-related quality of life, and response to any underlying disease. The primary objective of the study was to investigate the long-term effect of branched-chain amino acids (BCAA) supplementations on parameters associated with improved prognosis in sarcopenic patients with LC, as well as to evaluate its impact on cirrhotic-related events. Methods: We carried out a 24 week, single-center, randomized, open-label, controlled, two cohort parallel-group intervention trial comparing the efficacy of BCAA against lactoalbumin (L-ALB) on 106 sarcopenic liver cirrhotics. The BCAA (intervention) group was treated with 7.2 g BCAA per whereas, the lactoalbumin group was also given 6.3 g of L-Albumin. The primary outcome was to assess the impact of BCAA on parameters of sarcopenia: muscle mass, muscle strength, and physical performance. The secondary outcomes were to study combined survival and maintenance of liver function changes in laboratory and clinical markers in the duration of six months. Results: Treatment with BCAA leads to significant improvement in sarcopenic parameters: muscle strength, muscle function, and muscle mass. The total cirrhotic-related complications and cumulative event-free survival occurred fewer in the BCAA group than in the L-ALB group. Prognostic markers also improved significantly in the study. Conclusion: The current clinical trial demonstrated that long-term BCAAs supplementation improved sarcopenia and prognostic markers in patients with advanced liver cirrhosis.

Keywords: sarcopenia, liver cirrhosis, BCAA, quality of life

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Authors: Nicola Milani, Carla Bellissimo, Davide Pogliana, Davide Panzin, Luca Garlaschelli, Giulia Facchinetti, Claudia Casson, Luca Marazzina, Andrea Sartori, Simone Rivaroli, Jeff Konin

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The patient is a 26 year-old male amateur football player from Milan, Italy; (81kg; 185cm; BMI 23.6 kg/m²). He sustained a non-contact anterior cruciate ligament tear to his right knee in June 2021. In September 2021, his right knee ligament was reconstructed using a semitendinosus graft. The injury occurred during a football match on natural grass with typical shoes on a warm day (32 degrees celsius). Playing as a defender he sustained the injury during a change of direction, where the foot was fixated on the grass. He felt pain and was unable to continue playing the match. The surgeon approved his rehabilitation to begin two weeks post-operative. The initial physiotherapist assessment determined performing two training sessions per day within the first three months. In the first three weeks, the pain was 4/10 on Numerical Rating Scale (NRS), no swelling, a range of motion was 0-110°, with difficulty fully extending his knee and minimal quadriceps activation. Crutches were discontinued at four weeks with improved walking. Active exercise, electrostimulator, physical therapy, massages, osteopathy, and passive motion were initiated. At week 6, he completed his first functional movement screen; the score was 16/21 with no pain and no swelling. At week 8, the isokinetic test showed a 23% differential deficit between the two legs in maximum strength (at 90°/s). At week 10, he improved to 15% of injury-induced deficit which suggested he was ready to start running. At week 12, the athlete sustained his first threshold test. At week 16, he performed his first return to sports movement assessment, which revealed a 10% stronger difference between the legs. At week 16, he had his second threshold test. At week 17, his first on-field test revealed a 5% differential deficit between the two legs in the hop test. At week 18, isokinetic test demonstrates that the uninjured leg was 7% stronger than the recovering leg in maximum strength (at 90°/s). At week 20, his second on-field test revealed a 2% difference in hop test; at week 21, his third isokinetic test demonstrated a difference of 5% in maximum strength (at 90°/s). At week 21, he performed his second return to sports movement assessment which revealed a 2% difference between the limbs. Since it was the end of the championship, the team asked him to partake in the playoffs; moreover the player was very motivated to participate in the playoffs also because he was the captain of the team. Together with the player and the team, we decided to let him play even though we were aware of a heightened risk of injury than what is reported in the literature because of two factors: biological recovery times and the results of the tests we performed. In the decision making process about the athlete’s recovery time, it is important to balance the information available from the literature with the desires of the patient to avoid frustration.

Keywords: ACL, football, rehabilitation, return to play

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Authors: Darrol Stanley, Levan Efremidze, Jannie Rossouw

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We investigate the predictability of the USD/ZAR (South African Rand) exchange rate with sample entropy analytics for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. These results are preliminary and do not yet account for reasonable transactions costs, although these are very small in currency markets.

Keywords: currency trading, entropy, market timing, risk factor model

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Authors: Ángel de J. López-Pérez, Sonia E. Ruiz, Vanessa A. Segovia

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Buildings vulnerability due to seismic activity has been highly studied since the middle of last century. As a solution to the structural and non-structural damage caused by intense ground motions, several seismic energy dissipating devices, such as buckling-restrained braces (BRB), have been proposed. BRB have shown to be effective in concentrating a large portion of the energy transmitted to the structure by the seismic ground motion. A design approach for buildings with BRB elements, which is based on a seismic Displacement-Based formulation, has recently been proposed by the coauthors in this paper. It is a practical and easy design method which simplifies the work of structural engineers. The method is used here for the design of the structure-BRB damper system. The objective of the present study is to extend and apply a methodology to find the best combination of design parameters on multiple-degree-of-freedom (MDOF) structural frame – BRB systems, taking into account simultaneously: 1) initial costs and 2) an adequate engineering demand parameter. The design parameters considered here are: the stiffness ratio (α = K_frame/K_total), and the strength ratio (γ = V_damper/V_total); where K represents structural stiffness and V structural strength; and the subscripts "frame", "damper" and "total" represent: the structure without dampers, the BRB dampers and the total frame-damper system, respectively. The selection of the best combination of design parameters α and γ is based on an initial costs analysis and on the structural dynamic response of the structural frame-damper system. The methodology is applied to a 12-story 5-bay steel building with BRB, which is located on the intermediate soil of Mexico City. It is found the best combination of design parameters α and γ for the building with BRB under study.

Keywords: best combination of design parameters, BRB, buildings with energy dissipating devices, buckling-restrained braces, initial costs

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Authors: Da-Wei Wang, Liang Yang, Xuan-Long Peng, Mei-Chuan Kuo, Jen-Taut Yeh

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The tensile retention and waterproof properties of thermoplastic starch (TPS) resins were significantly enhanced by modifying with proper amounts of citric acid (CA) and by melt-blending with poly(lactic acid) (PLA), although no distinguished chemical reaction occurred between CA and starch molecules. As evidenced by Fourier transform infrared spectroscopy and Solid-state 13C Nuclear Magnetic Resonance analyses, disruption of intra and interhydrogen-bondings within starch molecules did occur during the modification processes of CA modified TPS (i.e. TPS100CAx) specimens. The tensile strength (σf) retention values of TPS specimens reduced rapidly from 27.8 to 20.5 and 0.4 MPa, respectively, as the conditioning time at 20°C/50% relative humidity (RH) increased from 0 to 7 and 70 days, respectively. While the elongation at break (εf) retention values of TPS specimens increased rapidly from 5.9 to 6.5 and 34.8%, respectively, as the conditioning time increased from 0 to 7 and 70 days. After conditioning at 20°C/50% RH for 70 days, the σf and εf retention values of the best prepared (TPS100CA0.1)30PLA70 specimen are equivalent to 85% and 167% of its initial σf and εf values, respectively, and are more than 105 times higher but 48% lower than those of TPS specimens conditioned at 20°C/50% RH for the same amount of time. Demarcated diffraction peaks, new melting endotherms of recrystallized starch crystals and distinguished ductile characteristics with drawn debris were found for many conditioned TPS specimens, however, only slight retrogradation effect and much less drawn debris was found for most conditioned TPS100CAx and/or (TPS100CA0.1)xPLAy specimens. The significantly improved water proof, tensile retention properties and relatively unchanged in retrogradation effect found for most conditioned TPS100CAx and/or (TPS100CA0.1)xPLAy specimens are apparently due to the efficient blocking of the moisture-absorbing hydroxyl groups (free or hydrogen bonded) by hydrogen-bonding CA with starch molecules during their modification processes.

Keywords: thermoplastic starch, hydrogen-bonding, water proof, strength retention

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Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti

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Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: artificial neural network, EDM, metal removal rate, modeling, surface roughness

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Authors: M. N. Nwodo, Y. P. Cheng, N. H. Minh

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In the process of recovering oil in weak sandstone formations, the strength of sandstones around the wellbore is weakened due to the increase of effective stress/load from the completion activities around the cavity. The weakened and de-bonded sandstone may be eroded away by the produced fluid, which is termed sand production. It is one of the major trending subjects in the petroleum industry because of its significant negative impacts, as well as some observed positive impacts. For efficient sand management therefore, there has been need for a reliable study tool to understand the mechanism of sanding. One method of studying sand production is the use of the widely recognized Discrete Element Method (DEM), Particle Flow Code (PFC3D) which represents sands as granular individual elements bonded together at contact points. However, there is limited knowledge of the particle-scale behavior of the weak sandstone, and the parameters that affect sanding. This paper aims to investigate the reliability of using PFC3D and a simple Darcy flow in understanding the sand production behavior of a weak sandstone. An isotropic tri-axial test on a weak oil sandstone sample was first simulated at a confining stress of 1MPa to calibrate and validate the parallel bond models of PFC3D using a 10m height and 10m diameter solid cylindrical model. The effect of the confining stress on the number of bonds failure was studied using this cylindrical model. With the calibrated data and sample material properties obtained from the tri-axial test, simulations without and with fluid flow were carried out to check on the effect of Darcy flow on bonds failure using the same model geometry. The fluid flow network comprised of every four particles connected with tetrahedral flow pipes with a central pore or flow domain. Parametric studies included the effects of confining stress, and fluid pressure; as well as validating flow rate – permeability relationship to verify Darcy’s fluid flow law. The effect of model size scaling on sanding was also investigated using 4m height, 2m diameter model. The parallel bond model successfully calibrated the sample’s strength of 4.4MPa, showing a sharp peak strength before strain-softening, similar to the behavior of real cemented sandstones. There seems to be an exponential increasing relationship for the bigger model, but a curvilinear shape for the smaller model. The presence of the Darcy flow induced tensile forces and increased the number of broken bonds. For the parametric studies, flow rate has a linear relationship with permeability at constant pressure head. The higher the fluid flow pressure, the higher the number of broken bonds/sanding. The DEM PFC3D is a promising tool to studying the micromechanical behavior of cemented sandstones.

Keywords: discrete element method, fluid flow, parametric study, sand production/bonds failure

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Authors: Ramprasad Srinivasan

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Engineers create inventions and put their ideas in concrete terms to design new products. Design drivers must be established, which requires, among other things, a complete understanding of the product design, load paths, etc. For Aerospace Vehicles, weight/strength ratio, strength, stiffness and stability are the important design drivers. A complex built-up structure is made up of an assemblage of primitive structural forms of arbitrary shape, which include 1D structures like beams and frames, 2D structures like membranes, plate and shell structures, and 3D solid structures. Justification through simulation involves a check for all the quantities of interest, namely stresses, deformation, frequencies, and buckling loads and is normally achieved through the finite element (FE) method. Over the past few decades, Fiber-reinforced composites are fast replacing the traditional metallic structures in the weight-sensitive aerospace and aircraft industries due to their high specific strength, high specific stiffness, anisotropic properties, design freedom for tailoring etc. Composite panel constructions are used in aircraft to design primary structure components like wings, empennage, ailerons, etc., while thin-walled composite beams (TWCB) are used to model slender structures like stiffened panels, helicopter, and wind turbine rotor blades, etc. The TWCB demonstrates many non-classical effects like torsional and constrained warping, transverse shear, coupling effects, heterogeneity, etc., which makes the analysis of composite structures far more complex. Conventional FE formulations to model 1D structures suffer from many limitations like shear locking, particularly in slender beams, lower convergence rates due to material coupling in composites, inability to satisfy, equilibrium in the domain and natural boundary conditions (NBC) etc. For 2D structures, the limitations of conventional displacement-based FE formulations include the inability to satisfy NBC explicitly and many pathological problems such as shear and membrane locking, spurious modes, stress oscillations, lower convergence due to mesh distortion etc. This mandates frequent re-meshing to even achieve an acceptable mesh (satisfy stringent quality metrics) for analysis leading to significant cycle time. Besides, currently, there is a need for separate formulations (u/p) to model incompressible materials, and a single unified formulation is missing in the literature. Hence coupled field formulation (CFF) is a unified formulation proposed by the author for the solution of complex 1D and 2D structures addressing the gaps in the literature mentioned above. The salient features of CFF and its many advantages over other conventional methods shall be presented in this paper.

Keywords: coupled field formulation, kinematic and material coupling, natural boundary condition, locking free formulation

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Authors: Sami Mestiri, Abdeljelil Farhat

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The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result.

Keywords: forecasting, credit risk, Penalized Quasi Likelihood, Gibbs Sampler, logistic regression with random effects, curve ROC

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Authors: Mokrane Selma

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The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Min Wei

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The traditional prediction methods of the forecast for tourism market are paid more attention to the accuracy of the forecasts, ignoring the results of the feasibility of forecasting and predicting operability, which had made it difficult to predict the results of scientific testing. With the application of Linear Regression Model, this paper attempts to construct a scientific evaluation system for predictive value, both to ensure the accuracy, stability of the predicted value, and to ensure the feasibility of forecasting and predicting the results of operation. The findings show is that a scientific evaluation system can implement the scientific concept of development, the harmonious development of man and nature co-ordinate.

Keywords: linear regression model, tourism market, forecast, tourism economics

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Authors: Monika Saha, A. T. M. Hasan Zobeyer, Nasreen Jahan

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El Nino-Southern Oscillation (ENSO) is the interaction between atmosphere and ocean in tropical Pacific which causes inconsistent warm/cold weather in tropical central and eastern Pacific Ocean. Due to the impact of climate change, ENSO events are becoming stronger in recent times, and therefore it is very important to study the influence of ENSO in climate studies. Bangladesh, being in the low-lying deltaic floodplain, experiences the worst consequences due to flooding every year. To reduce the catastrophe of severe flooding events, non-structural measures such as flood forecasting can be helpful in taking adequate precautions and steps. Forecasting seasonal flood with a longer lead time of several months is a key component of flood damage control and water management. The objective of this research is to identify the possible strength of teleconnection between ENSO and river flow of Surma and examine the potential possibility of long lead flood forecasting in the wet season. Surma is one of the major rivers of Bangladesh and is a part of the Surma-Meghna river system. In this research, sea surface temperature (SST) has been considered as the ENSO index and the lead time is at least a few months which is greater than the basin response time. The teleconnection has been assessed by the correlation analysis between July-August-September (JAS) flow of Surma and SST of Nino 4 region of the corresponding months. Cumulative frequency distribution of standardized JAS flow of Surma has also been determined as part of assessing the possible teleconnection. Discharge data of Surma river from 1975 to 2015 is used in this analysis, and remarkable increased value of correlation coefficient between flow and ENSO has been observed from 1985. From the cumulative frequency distribution of the standardized JAS flow, it has been marked that in any year the JAS flow has approximately 50% probability of exceeding the long-term average JAS flow. During El Nino year (warm episode of ENSO) this probability of exceedance drops to 23% and while in La Nina year (cold episode of ENSO) it increases to 78%. Discriminant analysis which is known as 'Categoric Prediction' has been performed to identify the possibilities of long lead flood forecasting. It has helped to categorize the flow data (high, average and low) based on the classification of predicted SST (warm, normal and cold). From the discriminant analysis, it has been found that for Surma river, the probability of a high flood in the cold period is 75% and the probability of a low flood in the warm period is 33%. A synoptic parameter, forecasting index (FI) has also been calculated here to judge the forecast skill and to compare different forecasts. This study will help the concerned authorities and the stakeholders to take long-term water resources decisions and formulate policies on river basin management which will reduce possible damage of life, agriculture, and property.

Keywords: El Nino-Southern Oscillation, sea surface temperature, surma river, teleconnection, cumulative frequency distribution, discriminant analysis, forecasting index

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Authors: Muhamad Hafiz Ismail, Ahmad H., Nelfianty M. R.

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The longitudinal study is conducted to identify predictive factors of soccer talent among male players aged 14 to 17 years. Convenience sampling involving elite respondents (n=20) and sub-elite respondents (n=20) male soccer players. Descriptive statistics were reported as frequencies and percentages. The inferential statistical analysis is used to report the status of reliability, independent samples t-test, paired samples t-test, and multiple regression analysis. Generally, there are differences in mean of height, muscular strength, muscular endurance, cardiovascular endurance, task orientation, cognitive anxiety, self-confidence, juggling skills, short pass skills, long pass skills, dribbling skills, and shooting skills for 20 elite players and sub-elite players. Accordingly, there was a significant difference between pre and post-test for thirteen variables of height, weight, fat percentage, muscle strength, muscle endurance, cardiovascular endurance, flexibility, BMI, task orientation, juggling skills, short pass skills, a long pass skills, and dribbling skills. Based on the first predictive factors (physical), second predictive factors (fitness), third predictive factors (psychological), and fourth predictive factors (skills in playing football) pledged to the soccer talent; four multiple regression models were produced. The first predictive factor (physical) contributed 53.5 percent, supported by height and percentage of fat in soccer talents. The second predictive factor (fitness) contributed 63.2 percent and the third predictive factors (psychology) contributed 66.4 percent of soccer talent. The fourth predictive factors (skills) contributed 59.0 percent of soccer talent. The four multiple regression models could be used as a guide for talent scouting for soccer players of the future.

Keywords: soccer talent identification, fitness and physical test, soccer skills test, psychological test

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Authors: Farhad Ahmadi, Hamed Kimiaee, Fariba Rahimi

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This research study focuses on the effect of different levels and sources of calcium on egg production, egg traits, and calcium retention in laying Japanese quail. The study aims to determine the impact of nano calcium carbonate (NCC) and calcium carbonate (CC) on these factors. The research was conducted using a total of 280 laying quail with an average age of 8 weeks. The quails were randomly distributed in a completely randomized design (CRD) with 7 treatments, 4 replications, and 10 quails in each pen. The study lasted for 90 days. The experimental diets included a control group (T1) with a basal diet consisting of 3.17% CaCO₃, and other groups supplemented with different levels (0.5%, 0.1%, and 0.15%) of either calcium carbonate (CC) or nano calcium carbonate (NCC). The quails had free access to water and feed throughout the study period. Findings: The results of the study showed that NCC at the levels of 0.1% and 0.15% (T6 and T7) improved eggshell thickness, shell thickness, and shell breaking strength compared to the control group. Although not statistically significant, there was an increasing trend in quail egg production and calcium retention in the calcareous shell of the egg in birds that consumed the experimental diets containing different levels of NCC compared to the control and other treatment groups. Quail egg production was recorded monthly for each treatment group. At the end of the study, a total of 40 eggs (10 eggs/replicate) from each treatment group were randomly selected for analysis. Parameters such as eggshell thickness, shell thickness, shell breaking strength, and calcium retention were measured. Statistical analysis was performed to compare the results between the different treatment groups. In conclusion, this study suggests that NCC at the levels of 0.1% and 0.15% can improve the quantity and quality of eggs and calcium retention in laying Japanese quail. These findings highlight the potential benefits of using NCC as a calcium source in quail diets. Further research could be conducted to explore the mechanisms behind these improvements and optimize the dosage of NCC for maximum effect.

Keywords: egg, calcium, nanoparticles, physiology

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Authors: I-Yun Cheng, Min-Yu Chiang, Shwu-Jen Chang, San-Yuan Chen

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Osteoarthritis (OA) is among the most challenging joint diseases, and as far as we know, there is currently no exact and effective cure for it because it has low self-repair ability due to lack of blood vessels and low cell density in articular cartilage. So far, there have been several methods developed to treat cartilage disorder. The most common method is to treat the high molecular weight of hyaluronic acid (HA) injection, but it will degrade after a period of time, so the patients need to inject HA repeatedly. In recent years, self-healing hydrogel has drawn considerable attention because it can recover its initial mechanical properties after damaged and further increase the lifetime of the hydrogel. Here, we aim to develop a self-healable composite hydrogel combined with magnetic microspheres to trigger glutathione(GSH) release for promoting cartilage repair. We use HA-cyclodextrin (CD) as host polymer and poly(acrylic acid)-ferrocene (pAA-Fc) as guest polymer to form the self-healable HA-pAA hydrogel by host and guest interaction where various graft amount of pAA-Fc (pAA:Fc= 1:2, 1:1.5, 1:1, 2:1, 4:1) was conducted to develop different mechanical strength hydrogel. The rheology analysis showed that the 4:1 of pAA-Fc has higher mechanical strength than other formulations. On the other hand, iron oxide nanoparticle, poly(lactic-co-glycolic acid) (PLGA) and polyethyleneimine (PEI) were used to synthesize porous magnetic microspheres via double emulsification water-in-oil-in-water (W/O/W) to increase GSH loading which acted as a reductant to control the hydrogel crosslink density and promote hydrogel self-healing. The results show that the porous magnetic microspheres can be loaded with 70% of GSH and sustained release about 50% of GSH after 24 hours. More importantly, the HA-pAA composite hydrogel can self-heal rapidly within 24 hours when suffering external force destruction by releasing GSH from the magnetic microspheres. Therefore, the developed the HA-pAA composite hydrogel combined with GSH-loaded magnetic microspheres can be in-vivo guided to damaged OA surface for inducing the cartilage repair by controlling the crosslinking of self-healing hydrogel via GSH release.

Keywords: articular cartilage, magnetic microsphere, osteoarthritis, self-healing hydrogel

Procedia PDF Downloads 132

Authors: Rudolf Hela, Michala Hubertova

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The paper focuses on research of durability and lifetime of dense light-weight concrete with artificial light-weight aggregate Liapor exposed to various types of aggressive environment. Experimental part describes testing of designed concrete of various strength classes and volume weights exposed to cyclical freezing, frost and chemical de-icers and various types of chemically aggressive environment.

Keywords: aggressive environment, durability, physical-mechanical properties, light-weight concrete

Procedia PDF Downloads 268

Authors: Hitalo Silva

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The most famous corruption scandals in the past four years were taken in Latin America, especially in Brazil, but besides the stain that these countries suffered in an international field, there was a huge effort to create or modernize its national anti-corruption laws. Also, the countries are implementing new standards for investigations and corporate compliance programs, in order to combat corruption and prevent the money laundering. But here is the following question: is here an invisible uniformization/transnationalization of the anti-corruption systems in Latin America? This new scenario reflects the impacts of the corruption investigations conducted in Latin America countries, such as Car Wash Operation in Brazil, Pretelt Case in Colombia, Gasoducto Sur Peruano case and the Mr. Alex Kouri’s case both in Peru. Legal and institutional pro-transparency reforms were made recently, the companies are trying to implement new standards of conduct and investing in their compliance department. In this sense, there is a huge homogeneity in Latin America concerning the structuring of corporate compliance programs, a truly transnationalization not only of laws but also corporate standards among these countries. Although legislative initiatives vary among the countries, there is a tendency to impose rigid liability standards for the companies being investigated for corruption, not only the personal punishments of their executives, which demonstrate the power of authorities to strength the investigative tools. Also, instruments such as leniency agreements and plea bargain are essential to put a central role in enforcement activities in Latin America. In other words, in a region where six former Presidents were convicted for acts of corruption, and, companies such as Odebrecht that is accused of offering bribes to politicians from Argentina to México, passing through Ecuador, Colombia, Guatemala and Panama, this demonstrates the necessity to increase strength of their legal framework in a sense that unify transnational goals. All things considered, this paper will show how anti-corruption regulators are cooperating in Latin America jurisdictions in order to unify their laws and how the private sector is dealing with this new scenario of corporate culture change.

Keywords: compliance, corruption, investigations, Latin America, transnational

Procedia PDF Downloads 125

Authors: Jing Nan

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The air breakdown voltage in compact circuit breakers is a critical factor in the design and reliability of electrical distribution systems. This voltage determines the threshold at which the air insulation between conductors will fail or 'break down,' leading to an arc. This phenomenon is highly sensitive to the conditions within the breaker, such as the temperature and the distance between electrodes. Typically, air breakdown voltage models have been reliable for predicting failure under standard operational temperatures. However, in conditions post-arcing, where temperatures can soar above 2000K, these models face challenges due to the complex physics of ionization and electron behaviour at such high-energy states. Building upon the foundational understanding that the breakdown mechanism is initiated by free electrons and propelled by electric fields, which lead to ionization and, potentially, to avalanche or streamer formation, we acknowledge the complexity introduced by high-temperature environments. Recognizing the limitations of existing experimental data, a notable research gap exists in the accurate prediction of breakdown voltage at elevated temperatures, typically observed post-arcing, where temperatures exceed 2000K.To bridge this knowledge gap, we present a method that integrates gap distance and high-temperature effects into air breakdown voltage assessment. The proposed model is grounded in the physics of ionization, accounting for the dynamic behaviour of free electrons which, under intense electric fields at elevated temperatures, lead to thermal ionization and potentially reach the threshold for streamer formation as Meek's criterion. Employing the Saha equation, our model calculates equilibrium electron densities, adapting to the atmospheric pressure and the hot temperature regions indicative of post-arc temperature conditions. Our model is rigorously validated against established experimental data, demonstrating substantial improvements in predicting air breakdown voltage in the high-temperature regime. This work significantly improves the predictive power for air breakdown voltage under conditions that closely mimic operational stressors in compact circuit breakers. Looking ahead, the proposed methods are poised for further exploration in alternative insulating media, like SF6, enhancing the model's utility for a broader range of insulation technologies and contributing to the future of high-temperature electrical insulation research.

Keywords: air breakdown voltage, high-temperature insulation, compact circuit breakers, electrical discharge, saha equation

Procedia PDF Downloads 84

Authors: M. Lebrini, L. Salhi, C. Deyrat, C. Roos, O. Nait-Rabah

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The paper presents a large experimental study on the corrosion of zinc in tropical soil and in the ground water at the various depths. Through this study, the corrosion rate prediction was done on the basis of two methods the electrochemical method and the gravimetric. The electrochemical results showed that the corrosion rate is more important at the depth levels 0 m to 0.5 m and 0.5 m to 1 m and beyond these depth levels, the corrosion rate is less important. The electrochemical results indicated also that a passive layer is formed on the zinc surface. The found SEM and EDX micrographs displayed that the surface is extremely attacked and confirmed that a zinc oxide layer is present on the surface whose thickness and relief increase as the contact with soil increases.

Keywords: soil corrosion, galvanized steel, electrochemical technique, SEM and EDX

Procedia PDF Downloads 127

Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

Procedia PDF Downloads 161

Authors: Fahimeh Palizban, Farshad Noravesh, Amir Hossein Saeidian, Mahya Mehrmohamadi

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In the recent decade, the emergence of liquid biopsy has significantly improved cancer monitoring and detection. Dying cells, including those originating from tumors, shed their DNA into the blood and contribute to a pool of circulating fragments called cell-free DNA. Accordingly, identifying the tissue origin of these DNA fragments from the plasma can result in more accurate and fast disease diagnosis and precise treatment protocols. Open chromatin regions are important epigenetic features of DNA that reflect cell types of origin. Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides insights into tissue-specific and disease-specific regulatory mechanisms. There have been several studies in the area of cancer liquid biopsy that integrate distinct genomic and epigenomic features for early cancer detection along with tissue of origin detection. However, multimodal analysis requires several types of experiments to cover the genomic and epigenomic aspects of a single sample, which will lead to a huge amount of cost and time. To overcome these limitations, the idea of predicting OCRs from WGS is of particular importance. In this regard, we proposed a computational approach to target the prediction of open chromatin regions as an important epigenetic feature from cell-free DNA whole genome sequence data. To fulfill this objective, local sequencing depth will be fed to our proposed algorithm and the prediction of the most probable open chromatin regions from whole genome sequencing data can be carried out. Our method integrates the signal processing method with sequencing depth data and includes count normalization, Discrete Fourie Transform conversion, graph construction, graph cut optimization by linear programming, and clustering. To validate the proposed method, we compared the output of the clustering (open chromatin region+, open chromatin region-) with previously validated open chromatin regions related to human blood samples of the ATAC-DB database. The percentage of overlap between predicted open chromatin regions and the experimentally validated regions obtained by ATAC-seq in ATAC-DB is greater than 67%, which indicates meaningful prediction. As it is evident, OCRs are mostly located in the transcription start sites (TSS) of the genes. In this regard, we compared the concordance between the predicted OCRs and the human genes TSS regions obtained from refTSS and it showed proper accordance around 52.04% and ~78% with all and the housekeeping genes, respectively. Accurately detecting open chromatin regions from plasma cell-free DNA-seq data is a very challenging computational problem due to the existence of several confounding factors, such as technical and biological variations. Although this approach is in its infancy, there has already been an attempt to apply it, which leads to a tool named OCRDetector with some restrictions like the need for highly depth cfDNA WGS data, prior information about OCRs distribution, and considering multiple features. However, we implemented a graph signal clustering based on a single depth feature in an unsupervised learning manner that resulted in faster performance and decent accuracy. Overall, we tried to investigate the epigenomic pattern of a cell-free DNA sample from a new computational perspective that can be used along with other tools to investigate genetic and epigenetic aspects of a single whole genome sequencing data for efficient liquid biopsy-related analysis.

Keywords: open chromatin regions, cancer, cell-free DNA, epigenomics, graph signal processing, correlation clustering

Procedia PDF Downloads 150

Authors: Djehich Mohamed Yousri

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In this research paper, the future of international relations is believed to have an important place on the theoretical and applied levels because policy makers in the world are in dire need of such analyzes that are useful in drawing up the foreign policies of their countries, and protecting their national security from potential future threats, and in this context, The topic raised a lot of scientific controversy and intellectual debate, especially in terms of the extent of the effectiveness, accuracy, and ability of foresight methods to identify potential futures, and this is what attributed the controversy to the scientific foundations for foreseeing international relations. An arena for intellectual discussion between different thinkers in international relations belonging to different theoretical schools, which confirms to us the conceptual and implied development of prediction in order to reach the scientific level.

Keywords: foresight, forecasting, international relations, international relations theory, concept of international relations

Procedia PDF Downloads 214