Search results for: loss models

Authors: Rufus Temidayo Akinnubi, M. O. Adeniyi

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Existing diurnal air temperature models simulate night time air temperature over Nigeria with high biases. An improved parameterization is presented for modeling the diurnal pattern of air temperature (Ta) which is applicable in the calculation of turbulent heat fluxes in Global climate models, based on Nigeria Micrometeorological Experimental site (NIMEX) surface layer observations. Five diurnal Ta models for estimating hourly Ta from daily maximum, daily minimum, and daily mean air temperature were validated using root-mean-square error (RMSE), Mean Error Bias (MBE) and scatter graphs. The original Fourier series model showed better performance for unstable air temperature parameterizations while the stable Ta was strongly overestimated with a large error. The model was improved with the inclusion of the atmospheric cooling rate that accounts for the temperature inversion that occurs during the nocturnal boundary layer condition. The MBE and RMSE estimated by the modified Fourier series model reduced by 4.45 oC and 3.12 oC during the transitional period from dry to wet stable atmospheric conditions. The modified Fourier series model gave good estimation of the diurnal weather patterns of Ta when compared with other existing models for a tropical environment.

Keywords: air temperature, mean bias error, Fourier series analysis, surface energy balance,

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Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

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An estimating equation technique is an alternative method of the widely used maximum likelihood methods, which enables us to ease some complexity due to the complex characteristics of time-varying covariates. In the situations, when both the time-varying covariates and left-truncation are considered in the model, the maximum likelihood estimation procedures become much more burdensome and complex. To ease the complexity, in this study, the modified estimating equations those have been given high attention and considerations in many researchers under semiparametric transformation model was proposed. The purpose of this article was to develop the modified estimating equation under flexible and general class of semiparametric transformation models for left-truncated and right censored survival data with time-varying covariates. Besides the commonly applied Cox proportional hazards model, such kind of problems can be also analyzed with a general class of semiparametric transformation models to estimate the effect of treatment given possibly time-varying covariates on the survival time. The consistency and asymptotic properties of the estimators were intuitively derived via the expectation-maximization (EM) algorithm. The characteristics of the estimators in the finite sample performance for the proposed model were illustrated via simulation studies and Stanford heart transplant real data examples. To sum up the study, the bias for covariates has been adjusted by estimating density function for the truncation time variable. Then the effect of possibly time-varying covariates was evaluated in some special semiparametric transformation models.

Keywords: EM algorithm, estimating equation, semiparametric transformation models, time-to-event outcomes, time varying covariate

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Authors: Sukhvinder Singh

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Punjabi language is related to the languages of New Indo-Aryan group that, in turn, is related to the branch of Indo-European language family. Punjabi language covers the areas of Western part (that is in Pakistan) and Eastern part (the Punjab state, Haryana, Delhi Himachal and J&K) and abroad (particularly Canada, USA, U.K. and Arab Emirates), where it is spoken widely. Besides India and Pakistan, Punjabi is the third language spoken in Canada after English, French having more than one hundred millions speakers worldwide. It is the fourth language spoken in Canada after English, French, and Chinese. It is also being taught as second language in most of the community school of British Columbia. The total number of Punjabi speakers is more than one hundred millions including India, Pakistan and abroad. Punjabi has a long tradition of linguistic tradition. A large number of scholars have studied Punjabi at different linguistic levels. Various studies are devoted to its special phonological characteristics, especially the tone, which has now started disappearing in favour of aspiration, a rare example of a language change in progress in its reversal direction. This process of language change in progress in reversal is dealt with in this paper a change towards a loss of linguistic indigeneity. The tone being a distinctive linguistic feature of Punjabi language is getting lost due to the increasing influence of Hindi and English particularly in the speech Urban Punjabi and Punjabi settled abroad. In this paper, an attempt has been made to discuss the sociolinguistics and sociology of Punjabi language and Punjab to trace the initiation and progression of this change towards a loss of Linguistic Indigeneity.

Keywords: language change in reversal, reaspiration, detonalization, new Indo-Aryan group

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Authors: G .D. B. N. Kulasaooriya, H. B. S. Ariyarathne, I. Abeygunawardene, A. A. J. Rafarathne, B. V. Perera

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Elephant is the largest resident of the wild and has small surface to volume ratio as well as less number of sweat glands which cause challenges to the thermoregulation of this mammal. However, this megaherbivore has adopted specialised meachanisms to maintain its thermal balance through behavioral adaptations, ear flapping and well anastomosed arterioles and venules of the ear. Nevertheless, little is known on the involvement of the skin in the process of thermoregulation. The present study was undertaken to monitor the water evaporation rate from the skin of unrestrained wild elephant calves throughout the day and to understand its importance in the thermoregulation. Seven baby elephants housed in the elephant transit home, Udawalawe were used. Ambient temparature, relative humidity (RH) and radiation heat load was monitored throughout the day of the study period. Similarly, surface temparature of the skin was taken at six points including lateral ear pinna, lateral body and the rump during the same period. The skin water evaporation was also measured from the same sites using cobolt chloride method. The surface are of the skin was determined by assigning geometrical shapes to each body part. The results showed that the ambient temperature gradually increased with the day reaching maximum around 3.00 pm. The relative humidity was lowest early in the morning. The radiation heat load did not show any significant change in the study period. The skin temperature was different among lateral ear pinna, lateral body and the rump where the highest temperature was on the rump and the lowest on the lateral ear pinna. The skin temperature gradually increase with increasing ambient temperature but there was not a strong correlation (R2 =53.53) between these two. The skin temperature had strong correlation with RH (p<0.05 R2 =70.84% ) but a significant relationship was not considered since the radiation heat load was not varying in large scale. The skin evaporative water loss had a weak negative correlation with ambient temperature (correlation coefficient= -0.01) whereas strong positive correlation with RH (correlation coefficient= 25.275 ) and no corelation with radiation heat load. It also appeared that skin water loss increases as the skin temperature increased. In the present study, it was observed that on average, skin of the baby elephant looses 403 g/m2/h of water. Based on these observations it can be concluded that a large volume of water is evaporated from the skin of baby elephants and evaporative heat loss may be contributing significantly to the thermoregulation. However, further investigation on the influence of environmental factors on evaporative heat loss has to be conducted to understand the thermoregulatory mechanisms of the baby elephant.

Keywords: thermoregulation, behavioral adaptations, evaporation, elephant

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Authors: Christopher Mkirema Maghanga, Maurice Mghendi Mwamburi

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Parametric modeling provides a means to deeper understand the properties of materials. Drude, Brendel, Lorentz and OJL incorporated in SCOUT® software are some of the models used to study dielectric films. In our work, we utilized Brendel and Drude models to extract the optical constants from spectroscopic data of fabricated undoped and niobium doped titanium oxide thin films. The individual contributions by the two models were studied to establish how they influence the dielectric function. The effect of dopants on their influences was also analyzed. For the undoped films, results indicate minimal contribution from the Drude term due to the dielectric nature of the films. However as doping levels increase, the rise in the concentration of free electrons favors the use of Drude model. Brendel model was confirmed to work well with dielectric films - the undoped titanium Oxide films in our case.

Keywords: modeling, Brendel model, optical constants, titanium oxide, Drude Model

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Authors: Zsanett Bahor, Cristina Nunes-Fonseca, Gillian L. Currie, Emily S. Sena, Lindsay D.G. Thomson, Malcolm R. Macleod

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Psychosis is ranked as the third most disabling medical condition in the world by the World Health Organization. Despite a substantial amount of research in recent years, available treatments are not universally effective and have a wide range of adverse side effects. Since many clinical drug candidates are identified through in vivo modelling, a deeper understanding of these models, and their strengths and limitations, might help us understand reasons for difficulties in psychosis drug development. To provide an unbiased summary of the preclinical psychosis literature we performed a systematic electronic search of PubMed for publications modelling a psychotic disorder in vivo, identifying 14,721 relevant studies. Double screening of 11,000 publications from this dataset so far established 2403 animal studies of psychosis, with the most common model being schizophrenia (95%). 61% of these models are induced using pharmacological agents. For all the models only 56% of publications test a therapeutic treatment. We propose a systematic review of these studies to assess the prevalence of reporting of measures to reduce risk of bias, and a meta-analysis to assess the internal and external validity of these animal models. Our findings are likely to be relevant to future preclinical studies of psychosis as this generation of strong empirical evidence has the potential to identify weaknesses, areas for improvement and make suggestions on refinement of experimental design. Such a detailed understanding of the data which inform what we think we know will help improve the current attrition rate between bench and bedside in psychosis research.

Keywords: animal models, psychosis, systematic review, schizophrenia

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Authors: Frederik Schulte, Stefan Voß

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The adverse effects of air pollution on public health are an increasingly vital problem in planning for urban regions in many parts of the world. The issue is addressed from various angles and by distinct disciplines in research. Epidemiological studies model the relative increase of numerous diseases in response to an increment of different forms of air pollution. A significant share of air pollution in urban regions is related to transport emissions that are often measured and stored in emission inventories. Though, most approaches in transport planning, engineering, and operational design of transport activities are restricted to general emission limits for specific air pollutants and do not consider more nuanced exposure models. We conduct an extensive literature review on exposure models and emission inventories used to study the health impact of transport emissions. Furthermore, we review methods applied in both domains and use emission inventory data of transportation hubs such as ports, airports, and urban traffic for an in-depth analysis of public health impacts deploying medical exposure models. The results reveal specific urban health risks related to transport emissions that may improve urban planning for environmental health by providing insights in actual health effects instead of only referring to general emission limits.

Keywords: emission inventories, exposure models, transport emissions, urban health

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Authors: Nadjib Dahdouh, Samira Amokrane, Elhadj Mekatel, Djamel Nibou

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The removal of Basic Yellow 28 (BY28) from aqueous solutions by plastic wastes PMMA was investigated. The characteristics of plastic wastes PMMA were determined by SEM, FTIR and chemical composition analysis. The effects of solution pH, initial Basic Yellow 28 (BY28) concentration C, solid/liquid ratio R, and temperature T were studied in batch experiments. The Freundlich and the Langmuir models have been applied to the adsorption process, and it was found that the equilibrium followed well Langmuir adsorption isotherm. A comparison of kinetic models applied to the adsorption of BY28 on the PMMA was evaluated for the pseudo-first-order and the pseudo-second-order kinetic models. It was found that used models were correlated with the experimental data. Intraparticle diffusion model was also used in these experiments. The thermodynamic parameters namely the enthalpy ∆H°, entropy ∆S° and free energy ∆G° of adsorption of BY28 on PMMA were determined. From the obtained results, the negative values of Gibbs free energy ∆G° indicated the spontaneity of the adsorption of BY28 by PMMA. The negative values of ∆H° revealed the exothermic nature of the process and the negative values of ∆S° suggest the stability of BY28 on the surface of SW PMMA.

Keywords: removal, Waste PMMA, BY28 dye, equilibrium, kinetic study, thermodynamic study

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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Authors: R. Mahmoodi, A. R. Zolfaghari

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In nuclear power plants, loss of coolant from the primary system is the type of reduced removed capacity that is given most attention; such an accident is referred as Loss of Coolant Accident (LOCA). In the current study, investigation of shell and tube THTL heat exchanger behavior during LOCA is implemented by ANSYS CFX simulation software in both steady state and transient mode of turbulent fluid flow according to experimental conditions. Numerical results obtained from ANSYS CFX simulation show good agreement with experimental data of THTL heat exchanger. The results illustrate that in large break LOCA as short term accident, heat exchanger could not fast response to temperature variables but in the long term, the temperature of shell side of heat exchanger will be increase.

Keywords: shell-and-tube heat exchanger, shell-side, CFD, flow and heat transfer, LOCA

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Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha

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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and time-consuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.

Keywords: soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil, geotechnical engineering

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Authors: Khalid Bashir, Hammad Zahid

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Most of the investors consider that economic and financial information is the most important at the time of making investment decisions. But it is not true, as in the past two decades, the Behavioral aspects and the behavioral biases have gained an important place in the decision-making process of an investor. This study is basically conducted on this fact. The purpose of this study is to examine the impact of behavioral factors on the decision-making of the individual real estate investor in Pakistan. Some important behavioral factors like overconfidence, anchoring, gambler’s fallacy, home bias, loss aversion, regret aversion, mental accounting, herding and representativeness are used in this study to find their impact on the psychology of individual investors. The targeted population is the real estate investor of Pakistan, and a sample of 650 investors is selected on the basis of convenience sampling technique. The data is collected through the questionnaire with a response rate of 46.15 %. Descriptive statistical techniques and SEM are used to analyze the data by using statistical software. The results revealed the fact that some behavioral factors have a significant impact on the decision-making of investors. Among all the behavioral biases, overconfidence, anchoring, gambler’s fallacy, loss aversion and representativeness have a significant positive impact on the decision-making of the individual investor, while the rest of biases like home bias, regret aversion, mental accounting, herding have less impact on the decision-making process of an individual.

Keywords: behavioral finance, anchoring, gambler’s fallacy, loss aversion

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Tarek Kandil, Ameen Ali

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Distributed generation (DG) has recently gained a lot of momentum in power industry due to market deregulation and environmental concerns. One of the most technical challenges facing DGs is islanding of distributed generators. The current industry practice is to disconnect all distributed generators immediately after the occurrence of islands within 200 to 350 ms after loss of main supply. To achieve such goal, each DG must be equipped with an islanding detection device. Frequency relays are one of the most commonly used loss of mains detection method. However, distribution utilities may be faced with concerns related to false operation of these frequency relays due to improper settings. The commercially available frequency relays are considering standard tight setting. This paper investigates some factors related to relays internal algorithm that contribute to their different operating responses. Further, the relay operation in the presence of multiple distributed at the same network is analyzed. Finally, the relay setting can be accurately determined based on these investigation and analysis.

Keywords: frequency relay, distributed generation, islanding detection, relay setting

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Authors: Catherine Maware, Olufemi Adetunji

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The paper is aimed at developing a model to measure the impact of Lean manufacturing deployment on organizational performance. The model will help industry practitioners to assess the impact of implementing Lean constructs on organizational performance. It will also harmonize the measurement models of Lean performance with the house of Lean that seems to have become the industry standard. The sheer number of measurement models for impact assessment of Lean implementation makes it difficult for new adopters to select an appropriate assessment model or deployment methodology. A literature review is conducted to classify the Lean performance model. Pareto analysis is used to select the Lean constructs for the development of the model. The model is further formalized through the use of Structural Equation Modeling (SEM) in defining the underlying latent structure of a Lean system. An impact assessment measurement model developed can be used to measure Lean performance and can be adopted by different industries.

Keywords: impact measurement model, lean bundles, lean manufacturing, organizational performance

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Authors: Amritee Bora, B. S. Mipun

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Land degradation is often used to describe the land environmental phenomena that reduce land’s original productivity both qualitatively and quantitatively. The study of land degradation vulnerability primarily deals with “Environmentally Sensitive Areas” (ESA) and the amount of topsoil loss due to erosion. In many studies, it is observed that the assessment of the existing status of land degradation is used to represent the vulnerability. Moreover, it is also noticed that in most studies, the primary emphasis of land degradation vulnerability is to assess its sensitivity to soil erosion only. However, the concept of land degradation vulnerability can have different objectives depending upon the perspective of the study. It shows the extent to which changes in land use land cover can imprint their effect on the land. In other words, it represents the susceptibility of a piece of land to degrade its productive quality permanently or in the long run. It is also important to mention that the vulnerability of land degradation is not a single factor outcome. It is a probability assessment to evaluate the status of land degradation and needs to consider both biophysical and human induce parameters. To avoid the complexity of the previous models in this regard, the present study has emphasized on to generate a simplified model to assess the land degradation vulnerability in terms of its current human population pressure, land use practices, and existing biophysical conditions. It is a “Mixed-Method” termed as the land degradation vulnerability index (LDVi). It was originally inspired by the MEDALUS model (Mediterranean Desertification and Land Use), 1999, and Farazadeh’s 2007 revised version of it. It has followed the guidelines of Space Application Center, Ahmedabad / Indian Space Research Organization for land degradation vulnerability. The model integrates the climatic index (Ci), vegetation index (Vi), erosion index (Ei), land utilization index (Li), population pressure index (Pi), and cover management index (CMi) by giving equal weightage to each parameter. The final result shows that the very high vulnerable zone primarily indicates three (3) prominent circumstances; land under continuous population pressure, high concentration of human settlement, and high amount of topsoil loss due to surface runoff within the study sites. As all the parameters of the model are amalgamated with equal weightage further with the help of regression analysis, the LDVi model also provides a strong grasp of each parameter and how far they are competent to trigger the land degradation process.

Keywords: population pressure, land utilization, soil erosion, land degradation vulnerability

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Authors: Panudet Saengseedam, Nanthachai Kantanantha

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This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.

Keywords: Bayesian method, linear mixed model, multivariate conditional autoregressive model, spatial time series

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Authors: Ecem Deniz Kırkpantur, Ozge Ecmel Onur, Tuba Cimilli Ozturk, Ebru Unal Akoglu

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Moyamoya disease is a unique chronic progressive cerebrovascular disease characterized by bilateral stenosis or occlusion of the arteries around the circle of Willis with prominent arterial collateral circulation. The occurrence of Moyamoya disease is related to immune, genetic and other factors. There is no curative treatment for Moyamoya disease. Secondary prevention for patients with symptomatic Moyamoya disease is largely centered on surgical revascularization techniques. We present here a 62-year old male presented with headache and vision loss for 2 days. He was previously diagnosed with hypertension and glaucoma. On physical examination, left eye movements were restricted medially, both eyes were hyperemic and their movements were painful. Other neurological and physical examination were normal. His vital signs and laboratory results were within normal limits. Computed tomography (CT) showed dilated vascular structures around both lateral ventricles and atherosclerotic changes inside the walls of internal carotid artery (ICA). Magnetic resonance imaging (MRI) and angiography (MRA) revealed dilated venous vascular structures around lateral ventricles and hyper-intense gliosis in periventricular white matter. Ischemic gliosis around the lateral ventricles were present in the Digital Subtracted Angiography (DSA). After the neurology, ophthalmology and neurosurgery consultation, the patient was diagnosed with Moyamoya disease, pulse steroid therapy was started for vision loss, and super-selective DSA was planned for further investigation. Moyamoya disease is a rare condition, but it can be an important cause of stroke in both children and adults. It generally affects anterior circulation, but posterior cerebral circulation may also be affected, as well. In the differential diagnosis of acute vision loss, occipital stroke related to Moyamoya disease should be considered. Direct and indirect surgical revascularization surgeries may be used to effectively revascularize affected brain areas, and have been shown to reduce risk of stroke.

Keywords: headache, Moyamoya disease, stroke, visual loss

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Authors: A.D. Susanti, W. B. Sediawan, S.K. Wirawan, Budhijanto, Ritmaleni

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Oryzanol content in rice bran oil (RBO) naturally has high antioxidant activity. Its reviewed has several health properties and high interested in pharmacy, cosmetics, and nutrition’s. Because of the low concentration of oryzanol in crude RBO (0.9-2.9%) then its need to be further processed for practical usage, such as via adsorption process. In this study, investigation and adjustment of adsorption equilibrium models were conducted through the kinetic data approachments. Mathematical modeling on kinetics of batch adsorption of oryzanol separation from RBO has been set-up and then applied for equilibrium results. The size of adsorbent particles used in this case are usually relatively small then the concentration in the adsorbent is assumed to be not different. Hence, the adsorption rate is controlled by the rate of oryzanol mass transfer from the bulk fluid of RBO to the surface of silica gel. In this approachments, the rate of mass transfer is assumed to be proportional to the concentration deviation from the equilibrium state. The equilibrium models applied were Langmuir, coefficient distribution, and Freundlich with the values of the parameters obtained from equilibrium results. It turned out that the models set-up can quantitatively describe the experimental kinetics data and the adjustment of the values of equilibrium isotherm parameters significantly improves the accuracy of the model. And then the value of mass transfer coefficient per unit adsorbent mass (kca) is obtained by curve fitting.

Keywords: adsorption equilibrium, adsorption kinetics, oryzanol, rice bran oil

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Authors: Harish K. Arya, Kulwant Singh, R. K. Saxena

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The heat flow in weldment changes its nature from 2D to 3D with the increase in plate thickness. For welding of thicker plates the heat loss in thickness direction increases the cooling rate of plate. Since the cooling rate changes, the various bead parameters like bead penetration, bead height and bead width also got affected by it. The present study incorporates the effect of variable plate thickness on penetration and bead width. The penetration reduces with increase in plate thickness due to heat loss in thickness direction for same heat input, while bead width increases for thicker plate due to faster cooling.

Keywords: submerged arc welding, plate thickness, bead geometry, cooling rate

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Authors: Desmond Adair, Kairat Ismailov, Martin Jaeger

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Modelling of Timoshenko beams on elastic foundations has been widely used in the analysis of buildings, geotechnical problems, and, railway and aerospace structures. For the elastic foundation, the most widely used models are one-parameter mechanical models or two-parameter models to include continuity and cohesion of typical foundations, with the two-parameter usually considered the better of the two. Knowledge of free vibration characteristics of beams on an elastic foundation is considered necessary for optimal design solutions in many engineering applications, and in this work, the efficient and accurate variational iteration method is developed and used to calculate natural frequencies of a Timoshenko beam on a two-parameter foundation. The variational iteration method is a technique capable of dealing with some linear and non-linear problems in an easy and efficient way. The calculations are compared with those using a finite-element method and other analytical solutions, and it is shown that the results are accurate and are obtained efficiently. It is found that the effect of the presence of the two-parameter foundation is to increase the beam’s natural frequencies and this is thought to be because of the shear-layer stiffness, which has an effect on the elastic stiffness. By setting the two-parameter model’s stiffness parameter to zero, it is possible to obtain a one-parameter foundation model, and so, comparison between the two foundation models is also made.

Keywords: Timoshenko beam, variational iteration method, two-parameter elastic foundation model

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Authors: Jiaqi Huang, Yuheng Wang

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Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.

Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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Authors: Brahim Boukabcha, Amar Rebbouh

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The bootstrap method based on the idea of exploiting all the information provided by the initial sample, allows us to study the properties of estimators. In this article we will present a theoretical study on the different methods of bootstrapping and using the technique of re-sampling in statistics inference to calculate the standard error of means of an estimator and determining a confidence interval for an estimated parameter. We apply these methods tested in the regression models and Pareto model, giving the best approximations.

Keywords: bootstrap, error standard, bias, jackknife, mean, median, variance, confidence interval, regression models

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Authors: Mohammad Z. Ansari, Lisa Brodie, Marilyn Goh

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Change facilitators are individuals who utilize change philosophy to make a positive change to organizations. The application of change facilitators can be seen in various change models; Lewin, Lippitt, etc. The facilitators within numerous change models are considered as internal/external consultants. Whilst most of the scholarly paper considers change facilitation as a consensus attempt to improve organization, there is a lack of a framework that develops both the organization and the change facilitator creating a self-sustaining change environment. This research paper introduces the development of the framework for change Leaders, Planners, and Executers (LPE), aiming at various organizational levels (Process, Departmental, and Organisational). The LPE framework is derived by exploring interrelated characteristics between facilitator(s) and the organization through qualitative research for understanding change management techniques and facilitator(s) behavioral aspect from existing Change Management models and Organisation behavior works of literature. The introduced framework assists in highlighting and identify the most appropriate change team to successfully deliver the change initiative within any organization (s).

Keywords: change initiative, LPE framework, change facilitator(s), sustainable change

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Authors: J. Jasmee, I. Roslina, A. Mohammed Yaziz & A.H Juazer Rizal

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The need of an effective building information collection method is vital to support a diversity of land development activities. At present, advances in remote sensing such as airborne LiDAR (Light Detection and Ranging) is an established technology for building information collection, location, and elevation of the reflecting laser points towards the construction of 3D building models. In this study, LiDAR datasets and terrestrial photographic images of buildings towards the construction of 3D building models is explored. It is found that, the quantitative accuracy of the constructed 3D building model, namely in the horizontal and vertical components were ± 0.31m (RMSEx,y) and ± 0.145m (RMSEz) respectively. The accuracies were computed based on sixty nine (69) horizontal and twenty (20) vertical surveyed points. As for the qualitative assessment, it is shown that the appearance of the 3D building model is adequate to support the requirements of LOD3 presentation based on the OGC (Open Geospatial Consortium) standard CityGML.

Keywords: LiDAR datasets, DSM, DTM, 3D building models

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: data mining, knowledge discovery in databases, prediction models, student success

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Authors: Othmane Touri, Sanaa El Filali, El Habib Benlahmar

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Natural Language Processing (NLP) has gained significant attention lately. It has proved its ability to analyze and extract insights from unstructured text data in various languages. It is found that one of the most popular NLP applications is sentiment analysis which aims to identify the sentiment expressed in a piece of text, such as positive, negative, or neutral, in multiple languages. While there are several multilingual NLP models available for sentiment analysis, there is a need to investigate their effectiveness in different contexts and applications. In this study, we aim to investigate the effectiveness of different multilingual NLP models for sentiment analysis on a dataset of online product reviews in multiple languages. The performance of several NLP models, including Google Cloud Natural Language API, Microsoft Azure Cognitive Services, Amazon Comprehend, Stanford CoreNLP, spaCy, and Hugging Face Transformers are being compared. The models based on several metrics, including accuracy, precision, recall, and F1 score, are being evaluated and compared to their performance across different categories of product reviews. In order to run the study, preprocessing of the dataset has been performed by cleaning and tokenizing the text data in multiple languages. Then training and testing each model has been applied using a cross-validation approach where randomly dividing the dataset into training and testing sets and repeating the process multiple times has been used. A grid search approach to optimize the hyperparameters of each model and select the best-performing model for each category of product reviews and language has been applied. The findings of this study provide insights into the effectiveness of different multilingual NLP models for Multilingual Sentiment Analysis and their suitability for different languages and applications. The strengths and limitations of each model were identified, and recommendations for selecting the most performant model based on the specific requirements of a project were provided. This study contributes to the advancement of research methods in multilingual NLP and provides a practical guide for researchers and practitioners in the field.

Keywords: NLP, multilingual, sentiment analysis, texts

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Authors: Basma Hussein Abdelaziz Hassan, Kareem Kamel, Philobater Bahgat Adly Awad, Karim Fahmy

Abstract:

Background: Laparoscopic adjustable gastric band was one of the most applied and common bariatric procedures in the last 8 years. However; the failure rate was very high, reaching approximately 60% of the patients not achieving the desired weight loss. Most patients sought another revisional surgery. In which, we compared two of the most common weight loss surgeries performed nowadays: the laparoscopic sleeve gastrectomy and laparoscopic one- anastomosis gastric bypass. Objective: To compare the weight loss and postoperative outcomes among patients undergoing conversion laparoscopic one-anastomosis gastric bypass (cOAGB) and laparoscopic sleeve gastrectomy (cSG) after a failed laparoscopic adjustable gastric band (LAGB). Patients and Methods: A prospective cohort study was conducted from June 2020 to June 2022 at a single medical center, which included 77 patients undergoing single-stage conversion to (cOAGB) vs (cSG). Patients were reassessed for weight loss, comorbidities remission, and post-operative complications at 6, 12, and 18 months. Results: There were 77 patients with failed LAGB in our study. Group (I) was 43 patients who underwent cOAGB and Group (II) was 34 patients who underwent cSG. The mean age of the cOAGB group was 38.58. While in the cSG group, the mean age was 39.47 (p=0.389). Of the 77 patients, 10 (12.99%) were males and 67 (87.01%) were females. Regarding Body mass index (BMI), in the cOAGB group the mean BMI was 41.06 and in the cSG group the mean BMI was 40.5 (p=0.042). The two groups were compared postoperative in relation to EBWL%, BMI, and the co-morbidities remission within 18 months follow-up. The BMI was calculated post-operative at three visits. After 6 months of follow-up, the mean BMI in the cOAGB group was 34.34, and the cSG group was 35.47 (p=0.229). In 12-month follow-up, the mean BMI in the cOAGB group was 32.69 and the cSG group was 33.79 (p=0.2). Finally, the mean BMI after 18 months of follow-up in the cOAGB group was 30.02, and in the cSG group was 31.79 (p=0.001). Both groups had no statistically significant values at 6 and 12 months follow-up with p-values of 0.229, and 0.2 respectively. However, patients who underwent cOAGB after 18 months of follow-up achieved lower BMI than those who underwent cSG with a statistically significant p-value of 0.005. Regarding EBWL% there was a statistically significant difference between the two groups. After 6 months of follow-up, the mean EBWL% in the cOAGB group was 35.9% and the cSG group was 33.14%. In the 12-month follow-up, the EBWL % mean in the cOAGB group was 52.35 and the cSG group was 48.76 (p=0.045). Finally, the mean EBWL % after 18 months of follow-up in the cOAGB group was 62.06 ±8.68 and in the cSG group was 55.58 ±10.87 (p=0.005). Regarding comorbidities remission; Diabetes mellitus remission was found in 22 (88%) patients in the cOAGB group and 10 (71.4%) patients in the cSG group with (p= 0.225). Hypertension remission was found in 20 (80%) patients in the cOAGB group and 14 (82.4%) patients in the cSG group with (p=1). In addition, dyslipidemia remission was found in 27(87%) patients in cOAGB group and 17(70%) patients in the cSG group with (p=0.18). Finally, GERD remission was found in about 15 (88.2%) patients in the cOAGB group and 6 (60%) patients in the cSG group with (p=0.47). There are no statistically significant differences between the two groups in the post-operative data outcomes. Conclusion: This study suggests that the conversion of LAGB to either cOAGB or cSG could be feasibly performed in a single-stage operation. cOAGB had a significant difference as regards the weight loss results than cSG among the mid-term follow-up. However, there is no significant difference in the postoperative complications and the resolution of the co-morbidities. Therefore, cOAGB could provide a reliable alternative but needs to be substantiated in future long-term studies.

Keywords: laparoscopic, gastric banding, one-anastomosis gastric bypass, Sleeve gastrectomy, revisional surgery, weight loss

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Authors: Abdul Malik Dawlatzai, Shafiqullah Rahmani

Abstract:

Splash erosion in field has affected by factors; slope, rain intensity, soil properties, and plant cover. And also, soil erosion affects not only farmland productivity but also water quality downstream. There are a number of potential soil conservation practices, but many of these are complicated and relatively expensive, such as buffer strips, agro-forestry, counter banking, catchment canal, terracing, surface mulching, reduced tillage, etc. However, mitigation soil and water loss in agricultural land, particularly in arid and semi-arid climatic conditions, is indispensable for environmental protection and agricultural production. The objective of this study is to evaluate the effects of adding gypsum mineral on mitigating splash erosion caused by rain drop. The research was conducted in soil laboratory Badam Bagh Agricultural Researching Farm, Kabul, Afghanistan. The stainless steel cores were used, and constant water pressure was controlled by a Mariotte’s bottle with kinetic energy of raindrops 2.36 x 10⁻⁵J. Gypsum mineral was applied at a rate of 5 and 10 t ha⁻¹ and using a sandy loam and loam soil textures. The result was showed an average soil loss from sandy loam soil texture; control was 8.22%, 4.31% and 4.06% similar from loam soil texture, control was 7.26%, 2.89%, and 2.72% respectively. The application of gypsum mineral significantly (P < 0.05) reduced dispersion of soil particles caused by the impact of raindrops compared to control. Therefore, it was concluded that the addition of gypsum was effective as a measure for mitigating splash erosion.

Keywords: gypsum, soil loss, splash erosion, Afghanistan

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