Search results for: modified Jones model

Authors: Arian Ebneyamini, Hoda Azimi, Jules Thibaults, F. Handan Tezel

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In this study, a modification of Hennepe model for pervaporation separation of butanol from aqueous solutions using Polydimethylsiloxane (PDMS) mixed matrix membranes has been introduced and validated by experimental data. The model was compared to the original Hennepe model and few other models which are applicable for membrane gas separation processes such as Maxwell, Lewis Nielson and Pal. Theoretical modifications for non-ideal interface morphology have been offered to predict the permeability in case of interface void, interface rigidification and pore-blockage. The model was in a good agreement with experimental data.

Keywords: butanol, PDMS, modeling, pervaporation, mixed matrix membranes

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Authors: Fethi Bouksila, Magnus Persson, Ronny Berndtsson, Akissa Bahri

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Under salinity conditions, we evaluate the performance of Hilhost (2000) model to predict pore electrical conductivity ECp from dielectric permittivity and bulk electrical conductivity (ECa) using Time and Frequency Domain Reflectometry sensors (TDR, FDR). Using FDR_WET sensor, RMSE of ECp was 4.15 dS m-1. By replacing the standard soil parameter (K0) in Hilhost model by K0-ECa relationship, the RMSE of ECp decreased to 0.68 dS m-1. WET sensor could give similar accuracy to estimate ECp than TDR if calibrated values of K0 were used instead of standard values in Hilhost model.

Keywords: hilhost model, soil salinity, time domain reflectometry, frequency domain reflectometry, dielectric methods

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Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

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Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

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Authors: Suchitra Sivakumar, Hajime Shingyouchi, Toshinori Okajima, Kyohei Yamaguchi, Jin Kusaka

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The progressing need for powertrain electrification calls for more accurate and reliable simulation models. A battery pack serves as the most vital component for energy storage in an electrified powertrain. Hybrid electric vehicles (HEV) do not behave the same way as they age, and there are several environmental factors that account for the degradation of the battery on a system level. Therefore, in this work, a battery model was proposed to study the state of charge (SOC) variation and the internal dynamic changes that contribute to aging and performance degradation in HEV batteries. An equivalent circuit battery model (ECM) is built using MATLAB Simulink to investigate the output characteristics of the lithium-ion battery. The ECM comprises of circuit elements like a voltage source, a series resistor and a parallel RC network connected in series. A parameter estimation study is conducted on the ECM to study the dependencies of the circuit elements with the state of charge (SOC) and the terminal voltage of the battery. The battery model is extended to simulate the temperature dependence of the individual battery cell and the battery pack with the environment. The temperature dependence model accounts for the heat loss due to internal resistance build up in the battery pack during charging, discharging, and due to atmospheric temperature. The model was validated for a lithium-ion battery pack with an independent drive cycle showing a voltage accuracy of 4% and SOC accuracy of about 2%.

Keywords: battery model, hybrid electric vehicle, lithium-ion battery, thermal model

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Authors: Nataliya Venelinova

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The paper proposes a new interdisciplinary model of reconsidering the role of mass communication effects by coverage of terrorism. The idea of 4P model is based on the synergy, created by the information strategy of threat, predominantly used by terrorist groups, the effects of mediating the symbolic action of the terrorist attacks or the taking of responsibility of any attacks, and the reshaped public perception for security after the attacks being mass communicated. The paper defines the mass communication cycle of terrorism, which leads not only to re-agenda setting of the societies, but also spirally amplifying the effect of propagating fears by over-informing on terrorism attacks. This finally results in the outlining of the so called 4P-model of information terrorism: mass propaganda, panic, paranoia and pandemic.

Keywords: information terrorism, mass communication cycle, public perception, security

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Authors: Hung-Chi Chang

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For an extended EPQ model with random yield, the existent study revealed that both the disaggregating and consolidating shipment policies for the imperfect quality items are independent of holding cost, and recommended a model with economic benefit by comparing the least total cost for each of the three models investigated. To better capture the real situation, we generalize the existent study to include different holding costs for perfect and imperfect quality items. Through analysis, we show that the above shipment policies are dependent on holding costs. Furthermore, we derive a simple decision rule solely based on the thresholds of problem parameters to select a superior model. The results are illustrated analytically and numerically.

Keywords: consolidating shipments, disaggregating shipments, EPQ, imperfect quality, inventory

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Authors: Elison Eduardo Jardim Bierhals, Claudineia Brazil, Deivid Pires, Rafael Haag, Elton Gimenez Rossini

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This study evaluated the uncertainties involved in the solar radiation projections generated by the Brazilian Earth System Model (BESM) of the Weather and Climate Prediction Center (CPTEC) belonging to Coupled Model Intercomparison Phase 5 (CMIP5), with the aim of identifying efficiency in the projections for solar radiation of said model and in this way establish the viability of its use. Two different scenarios elaborated by Intergovernmental Panel on Climate Change (IPCC) were evaluated: RCP 4.5 (with more optimistic contour conditions) and 8.5 (with more pessimistic initial conditions). The method used to verify the accuracy of the present model was the Nash coefficient and the Statistical bias, as it better represents these atmospheric patterns. The BESM showed a tendency to overestimate the data ​​of solar radiation projections in most regions of the state of Rio Grande do Sul and through the validation methods adopted by this study, BESM did not present a satisfactory accuracy.

Keywords: climate changes, projections, solar radiation, uncertainty

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Authors: Kumlachew Zelalem Walle, Chun-Chen Yang

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Solid-state lithium metal batteries (SSLMBs) are considered promising candidates for next-generation energy storage devices due to their superior energy density and excellent safety. However, recent findings have shown that the formation of lithium (Li) dendrites in SSLMBs still exhibits a terrible growth ability, which makes the development of SSLMBs have to face the challenges posed by the Li dendrite problem. In this work, an inorganic/organic mixture coating material (g-C3N4/ZIF-8/PVDF) was used to modify the surface of lithium metal anode (LMA). Then the modified LMA (denoted as g-C₃N₄@Li) was assembled with lithium nafion (LiNf) coated commercial NCM811 (LiNf@NCM811) using a bilayer hybrid solid electrolyte (Bi-HSE) that incorporated 20 wt.% (vs. polymer) LiNf coated Li6.05Ga0.25La3Zr2O11.8F0.2 ([email protected]) filler faced to the positive electrode and the other layer with 80 wt.% (vs. polymer) filler content faced to the g-C₃N₄@Li. The garnet-type Li6.05Ga0.25La3Zr2O11.8F0.2 (LG0.25LZOF) solid electrolyte was prepared via co-precipitation reaction process from Taylor flow reactor and modified using lithium nafion (LiNf), a Li-ion conducting polymer. The Bi-HSE exhibited high ionic conductivity of 6.8  10–4 S cm–1 at room temperature, and a wide electrochemical window (0–5.0 V vs. Li/Li+). The coin cell was charged between 2.8 to 4.5 V at 0.2C and delivered an initial specific discharge capacity of 194.3 mAh g–1 and after 100 cycles it maintained 81.8% of its initial capacity at room temperature. The presence of a nano-sheet g-C3N4/ZIF-8/PVDF as a composite coating material on the LMA surface suppress the dendrite growth and enhance the compatibility as well as the interfacial contact between anode/electrolyte membrane. The g-C3N4@Li symmetrical cells incorporating this hybrid electrolyte possessed excellent interfacial stability over 1000 h at 0.1 mA cm–2 and a high critical current density (1 mA cm–2). Moreover, the in-situ formation of Li3N on the solid electrolyte interface (SEI) layer as depicted from the XPS result also improves the ionic conductivity and interface contact during the charge/discharge process. Therefore, these novel multi-layered fabrication strategies of hybrid/composite solid electrolyte membranes and modification of the LMA surface using mixed coating materials have potential applications in the preparation of highly safe high-voltage cathodes for SSLMBs.

Keywords: high-voltage cathodes, hybrid solid electrolytes, garnet, graphitic-carbon nitride (g-C3N4), ZIF-8 MOF

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Authors: Sahin Emrah Amrahov, Fatih Aybar, Serhat Dogan

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In this paper we consider the rule reduct generation problem. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) algorithms, that are used to solve this problem, are well-known. Alternative to these algorithms, we develop Pruning Rule Generation (PRG) algorithm. We compare the PRG algorithm with RG and MRG.

Keywords: rough sets, decision rules, rule induction, classification

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Authors: Aijaz A. Shaikh, Richard Glavee-Geo, Heikki Karjaluoto

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Adoption of Information Systems (IS) is receiving increasing attention such that its implications have been closely monitored and studied by the IS management community, industry and professional gatekeepers. Building on previous research regarding the adoption of technology, this paper develops and validates an integrated model of the adoption of mobile banking. The model originates from the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB). This paper intends to offer a preliminary scrutiny of the antecedents of the adoption of mobile banking services in the context of a developing country. Data was collected from Pakistan. The findings showed that an integrated TAM and TPB model greatly explains the adoption intention of mobile banking; and perceived behavioural control and its antecedents play a significant role in predicting adoption Theoretical and managerial implications of findings are presented and discussed.

Keywords: developing country, mobile banking service adoption, technology acceptance model, theory of planned behavior

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Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

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Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: social network, link prediction, granular computing, type-2 fuzzy sets

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Authors: Giuliano Raimondo, Jörg Wangemann, Peer Drechsel

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In systems development involving Fuel Cells generators, it is important to have from an early stage of the project a dynamic model for the electrical behavior of the stack to be shared between involved development parties. It allows independent and early design and tests of fuel cell related power electronic. This paper presents an empirical Fuel Cell system model derived from characterization tests on a real system. Moreover, it is illustrated how the obtained model is used to build and validate a real-time Fuel Cell system emulator which is used for aerospace electrical integration testing activities.

Keywords: fuel cell, modelling, real time emulation, testing

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Authors: R. Bakr, M. Elmeligy, A. Ibrahim

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When the building loads are relatively small, challenges are often facing the foundation design especially when inappropriate soil conditions exist. These may be represented in the existence of soft soil in the upper layers of soil while sandy soil or firm cohesive soil exist in the deeper layers. In such cases, the design becomes infeasible if the piles are extended to the deeper layers, especially when there are sandy layers existing at shallower depths underlain by stiff clayey soil. In this research, models of piles terminated in sand underlain by clay soils are numerically simulated by different modelling theories. Finite element software, Plaxis 3-D Foundation was used to evaluate the pile behavior under different loading scenarios. The standard static load test according to ASTM D-1143 was simulated and compared with the real-life loading scenario. The results showed that the pile behavior obtained from the current static load test do not realistically represent that obtained from real-life loading. Attempts were carried out to capture the proper numerical loading scenario that simulates the pile behavior in real-life loading including the long-term effect. A modified method based on this research findings is proposed for the static pile loading tests. Field loading tests were carried out to validate the new method. Results obtained from both numerical and field tests by using the modified method prove that this method is more accurate in predicting the pile behavior in sand soil underlain by clay more than the current standard static load.

Keywords: numerical simulation, static load test, pile behavior, sand underlain with clay, creep

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Authors: Rangoli Goyal, Rama Bhargava

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The triple diffusive boundary layer flow of nanofluid under the action of constant magnetic field over a non-linear stretching sheet has been investigated numerically. The model includes the effect of Brownian motion, thermophoresis, and cross-diffusion; slip mechanisms which are primarily responsible for the enhancement of the convective features of nanofluid. The governing partial differential equations are transformed into a system of ordinary differential equations (by using group theory transformations) and solved numerically by using variational finite element method. The effects of various controlling parameters, such as the magnetic influence number, thermophoresis parameter, Brownian motion parameter, modified Dufour parameter, and Dufour solutal Lewis number, on the fluid flow as well as on heat and mass transfer coefficients (both of solute and nanofluid) are presented graphically and discussed quantitatively. The present study has industrial applications in aerodynamic extrusion of plastic sheets, coating and suspensions, melt spinning, hot rolling, wire drawing, glass-fibre production, and manufacture of polymer and rubber sheets, where the quality of the desired product depends on the stretching rate as well as external field including magnetic effects.

Keywords: FEM, thermophoresis, diffusiophoresis, Brownian motion

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Authors: Hyunsun Lee, Yi Zhu

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Wireless ad-hoc network is a decentralized type of temporary machine-to-machine connection that is spontaneous or impromptu so that it does not rely on any fixed infrastructure and centralized administration. As unmanned aerial vehicles (UAVs), also called drones, have recently become more accessible and widely utilized in military and civilian domains such as surveillance, search and detection missions, traffic monitoring, remote filming, product delivery, to name a few. The communication between these UAVs become possible and materialized through Flying Ad-hoc Networks (FANETs). However, due to the high mobility of UAVs that may cause different types of transmission interference, it is vital to design robust routing protocols for FANETs. In this talk, the multicast routing method based on a modified stochastic branching process is proposed. The stochastic branching process is often used to describe an early stage of an infectious disease outbreak, and the reproductive number in the process is used to classify the outbreak into a major or minor outbreak. The reproductive number to regulate the local transmission rate is adapted and modified for flying ad-hoc network communication. The performance of the proposed routing method is compared with other well-known methods such as flooding method and gossip method based on three measures; average reachability, average node usage and average branching factor. The proposed routing method achieves average reachability very closer to flooding method, average node usage closer to gossip method, and outstanding average branching factor among methods. It can be concluded that the proposed multicast routing scheme is more efficient than well-known routing schemes such as flooding and gossip while it maintains high performance.

Keywords: Flying Ad-hoc Networks, Multicast Routing, Stochastic Branching Process, Unmanned Aerial Vehicles

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Authors: V. Rangarajan, Priyanka Kaushal

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This study investigates the effectiveness of a typical Earth Air Heat Exchanger (EATHE) for energy efficient space cooling in an urban environment typified by space and soil-related constraints that preclude an optimal design. It involves the development of a three-dimensional numerical transient model that is validated by measurements at a live site in India. It is found that the model accurately predicts the soil temperatures at various depths as well as the EATHE outlet air temperature. The study shows that such an EATHE, even when designed under constraints, does provide effective space cooling especially during the hot months of the year.

Keywords: earth air heat exchanger (EATHE), India, MATLAB, model, simulation

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Authors: Tursunay Vassilina, Aigerim Shibikeyeva, Adilet Sakhbek

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Research has been carried out to study the influence of modified zeolite fertilizers on the quantitative and qualitative indicators of cabbage variety Nezhenka. The use of zeolite and mineral fertilizers had a positive effect on both the yield and quality indicators of the studied crop. The maximum increase in yield from fertilizers was 16.5 t/ha. Application of both zeolite and fertilizer increased the dry matter, sugar and vitamin C content of cabbage heads. It was established that the cabbage contains an amount of nitrates that is safe for human health. Among vegetable crops, cabbage has both food and feed value. One of the limiting factors in the sale of vegetable crops is the degradation of soil fertility due to depletion of nutrient reserves and erosion processes, and non-compliance with fertilizer application technologies. Natural zeolites are used as additives to mineral fertilizers for application in the field, which makes it possible to reduce their doses to minimal quantities. Zeolites improve the agrophysical and agrochemical properties of the soil and the quality of plant products. The research was carried out in a field experiment, carried out in 3 repetitions, on dark chestnut soil in 2023. The soil (pH = 7.2-7.3) of the experimental plot is dark chestnut, the humus content in the arable layer is 2.15%, gross nitrogen 0.098%, phosphorus, potassium 0.225 and 2.4%, respectively. The object of the study was the late cabbage variety Nezhenka. Scheme for applying fertilizers to cabbage: 1. Control (without fertilizers); 2. Zeolite 2t/ha; 3. N45P45K45; 4. N90P90K90; 5. Zeolite, 2 t/ha + N45P45K45; 6. Zeolite, 2 t/ha + N90P90K90. Yield accounting was carried out on a plot-by-plot basis manually. In plant samples, the following was determined: dry matter content by thermostatic method (at 105ºC); sugar content by Bertrand titration method, nitrate content by 1% diphenylamine solution, vitamin C by titrimetric method with acid solution. According to the results, it was established that the yield of cabbage was high – 42.2 t/ha in the treatment Zeolite, 2 t/ha + N90P90K90. When determining the biochemical composition of white cabbage, it was found that the dry matter content was 9.5% and increased with fertilized treatments. The total sugar content increased slightly with the use of zeolite (5.1%) and modified zeolite fertilizer (5.5%), the vitamin C content ranged from 17.5 to 18.16%, while in the control, it was 17.21%. The amount of nitrates in products also increased with increasing doses of nitrogen fertilizers and decreased with the use of zeolite and modified zeolite fertilizer but did not exceed the maximum permissible concentration. Based on the research conducted, it can be concluded that the application of zeolite and fertilizers leads to a significant increase in yield compared to the unfertilized treatment; contribute to the production of cabbage with good and high quality indicators.

Keywords: cabbage, dry matter, nitrates, total sugar, yield, vitamin C

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Authors: Seyed Sobhan Alvani, Mohammad Gohari

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By developing cities and increasing the population, traffic congestion has become a vital problem. Due to this crisis, urban designers try to present solutions to decrease this difficulty. On the other hand, predicting the model with perfect accuracy is essential for solution-providing. The current study presents a model based on artificial intelligence which can predict traffic index based on city population, growth rate, and area. The accuracy of the model was evaluated, which is acceptable and it is around 90%. Thus, urban designers and planners can employ it for predicting traffic index in the future to provide strategies.

Keywords: traffic index, population growth rate, cities wideness, artificial neural network

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Authors: Souad Romdhane, Lotfi Belkacem

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When referring to actuarial analysis of lifetime, only models accounting for observable risk factors have been developed. Within this context, Cox proportional hazards model (CPH model) is commonly used to assess the effects of observable covariates as gender, age, smoking habits, on the hazard rates. These covariates may fail to fully account for the true lifetime interval. This may be due to the existence of another random variable (frailty) that is still being ignored. The aim of this paper is to examine the shared frailty issue in the Cox proportional hazard model by including two different parametric forms of frailty into the hazard function. Four estimated methods are used to fit them. The performance of the parameter estimates is assessed and compared between the classical Cox model and these frailty models through a real-life data set from the Quebec Pension Plan and then using a more general simulation study. This performance is investigated in terms of the bias of point estimates and their empirical standard errors in both fixed and random effect parts. Both the simulation and the real dataset studies showed differences between classical Cox model and shared frailty model.

Keywords: life insurance-pension plan, survival analysis, risk factors, cox proportional hazards model, multivariate failure-time data, shared frailty, simulations study

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Authors: Salah Jellali, Nusiba Suliman, Yassine Charabi, Jamal Al-Sabahi, Ahmed Al Raeesi, Malik Al-Wardy, Mejdi Jeguirim

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Huge amounts of agricultural biomasses are worldwide produced. At the same time, large quantities of phosphorus are annually discharged into water bodies with possible serious effects onto the environment quality. The main objective of this work is to turn a local Omani biomass (date palm fronds wastes: DPFW) into an effective material for phosphorus recovery from aqueous and the reuse of this P-loaded material in agriculture as ecofriendly amendment. For this aim, the raw DPFW were firstly impregnated with 1 M salt separated solutions of CaCl₂, MgCl₂, FeCl₃, AlCl₃, and a mixture of MgCl₂/AlCl₃ for 24 h, and then pyrolyzed under N2 flow at 500 °C for 2 hours by using an adapted tubular furnace (Carbolite, UK). The synthetized biochars were deeply characterized through specific analyses concerning their morphology, structure, texture, and surface chemistry. These analyses included the use of a scanning electron microscope (SEM) coupled with an energy-dispersive X-Ray spectrometer (EDS), X-Ray diffraction (XRD), Fourier Transform Infrared (FTIR), sorption micrometrics, and X-ray Fluorescence (XRF) apparatus. Then, their efficiency in recovering phosphorus was investigated in batch mode for various contact times (1 min to 3 h), aqueous pH values (from 3 to 11), initial phosphorus concentrations (10-100 mg/L), presence of anions (nitrates, sulfates, and chlorides). In a second step, dynamic assays, by using laboratory columns (height of 30 cm and diameter of 3 cm), were performed in order to investigate the recovery of phosphorus by the modified biochar with a mixture of Mg/Al. The effect of the initial P concentration (25-100 mg/L), the bed depth height (3 to 8 g), and the flow rate (10-30 mL/min) was assessed. Experimental results showed that the biochars physico-chemical properties were very dependent on the type of the used modifying salt. The main affected parameters concerned the specific surface area, microporosity area, and the surface chemistry (pH of zero-point charge and available functional groups). These characteristics have significantly affected the phosphorus recovery efficiency from aqueous solutions. Indeed, the P removal efficiency in batch mode varies from about 5 mg/g for the Fe-modified biochar to more than 13 mg/g for the biochar functionalized with Mg/Al layered double hydroxides. Moreover, the P recovery seems to be a time dependent process and significantly affected by the pH of the aqueous media and the presence of foreign anions due to competition phenomenon. The laboratory column study of phosphorus recovery by the biochar functionalized with Mg/Al layered double hydroxides showed that this process is affected by the used phosphorus concentration, the flow rate, and especially the column bed depth height. Indeed, the phosphorus recovered amount increased from about 4.9 to more than 9.3 mg/g used biochar mass of 3 and 8 g, respectively. This work proved that salt-modified palm fronds-derived biochars could be considered as attractive and promising materials for phosphorus recovery from aqueous solutions even under dynamic conditions. The valorization of these P-loaded-modified biochars as eco-friendly amendment for agricultural soils is necessary will promote sustainability and circular economy concepts in the management of both liquid and solid wastes.

Keywords: date palm wastes, Mg/Al double-layered hydroxides functionalized biochars, phosphorus, recovery, sustainability, circular economy

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Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer

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This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.

Keywords: non-stationary, BINARMA(1, 1) model, Poisson innovations, conditional maximum likelihood, CML

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Authors: Makduma Zahan Badhan, M. Abdul Matin

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A study has been undertaken to develop a fluvial stage-discharge rating curve for Jamuna River. Past Cross-sectional survey of Jamuna River reach within Sirajgonj and Tangail has been analyzed. The analysis includes the estimation of discharge carrying capacity, possible maximum scour depth and sediment transport capacity of the selected reaches. To predict the discharge and sediment carrying capacity, stream flow data which include cross-sectional area, top width, water surface slope and median diameter of the bed material of selected stations have been collected and some are calculated from reduced level data. A well-known resistance equation has been adopted and modified to a simple form in order to be used in the present analysis. The modified resistance equation has been used to calculate the mean velocity through the channel sections. In addition, a sediment transport equation has been applied for the prediction of transport capacity of the various sections. Results show that the existing drainage sections of Jamuna channel reach under study have adequate carrying capacity under existing bank-full conditions, but these reaches are subject to bed erosion even in low flow situations. Regarding sediment transport rate, it can be estimated that the channel flow has a relatively high range of bed material concentration. Finally, stage­ discharge curves for various sections have been developed. Based on stage-discharge rating data of various sections, water surface profile and sediment-rating curve of Jamuna River have been developed and also the flooding conditions have been analyzed from predicted water surface profile.

Keywords: discharge rating, flow profile, fluvial, sediment rating

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Authors: Jana Gutierez Chvalkovska, Michal Mejstrik, Matej Urban

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There are pending discussions over the mapping of country export potential in order to refocus export strategy of firms and its evidence-based promotion by the Export Credit Agencies (ECAs) and other permitted vehicles of governments. In this paper we develop our version of an applied model that offers “stepwise” elimination of unattractive markets. We modify and calibrate the model for the particular features of the Czech Republic and specific pilot cases where we apply an individual approach to each sector.

Keywords: export strategy, modeling export, calibration, export promotion

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Authors: Enrique Barbieri

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The basic disease-spread model described by three states denoting the susceptible (S), infectious (I), and removed (recovered and deceased) (R) sub-groups of the total population N, or SIR model, has been considered. Heuristic mitigating action profiles of the pharmaceutical and non-pharmaceutical types may be developed in a control design setting for the purpose of reducing the transmission rate or improving the recovery rate parameters in the model. Even though the transmission and recovery rates are not control inputs in the traditional sense, a linear observer and feedback controller can be tuned to generate an asymptotic estimate of the transmission rate for a linearized, discrete-time version of the SIR model. Then, a set of mitigating actions is suggested to steer the basic reproduction number toward unity, in which case the disease does not spread, and the infected population state does not suffer from multiple waves. The special case of piecewise constant transmission rate is described and applied to a seventh-order SEIQRDP model, which segments the population into four additional states. The offline simulations in discrete time may be used to produce heuristic policies implemented by public health and government organizations.

Keywords: control of SIR, observer, SEIQRDP, disease spread

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Authors: Anastasis D. Petrou

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Innovation and strategy discussions do highlight open innovation as a new paradigm in business. Yet, a number of stumbling blocks in the form of closed innovation principles weaved into the fabric of a traditional business model stand in the way of the new paradigm’s momentum to increase value in various business contexts. The paper argues that businesses considering an engagement with the open innovation paradigm would need to take steps to improve their multiplicative, absorptive and relational capabilities, respectively. The needed improvements would amount to a business model evolutionary transformation and eventually bring about a paradigm overhaul in business. The transformation is worth staging over time to ensure that open innovation is developed across interconnected and partnered areas of strategic importance. This article develops an open innovation strategy (OIS) capabilities model, and employs examples from different industries to briefly discuss OIS’s potential to augment business value in a number of suggested areas for future research.

Keywords: close innovation, open innovation paradigm, open innovation strategy (OIS) paradigm, OIS capabilities model, multiplicative capability, absorptive capability, relational capability

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Authors: Xian Li, Qing-Guo Wang, Jiangshuai Huang, Jidong Liu, Ming Yu, Tan Kok Poh

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In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly.

Keywords: power industry, electricity demand, modeling, forecasting

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Authors: B. Van Kessel, P. T. Kockelkorn, T. R. Speelman, T. C. Wierikx, C. Mai Van, T. A. Bogaard

Abstract:

Saltwater intrusion is a common problem in estuaries around the world, as it could hinder the freshwater supply of coastal zones. This problem is likely to grow due to climate change and sea-level rise. The influence of these factors on the saltwater intrusion was investigated for the Cai River in the Khanh Hoa province in Vietnam. In addition, the Cai River has high seasonal fluctuations in discharge, leading to increased saltwater intrusion during the dry season. Sea level rise, river discharge changes, river mouth widening and a proposed saltwater intrusion prevention dam can have influences on the saltwater intrusion but have not been quantified for the Cai River estuary. This research used both an analytical and numerical model to investigate the effect of the aforementioned factors. The analytical model was based on a model proposed by Savenije and was calibrated using limited in situ data. The numerical model was a 3D hydrodynamic model made using the Delft3D4 software. The analytical model and numerical model agreed with in situ data, mostly for tidally average data. Both models indicated a roughly similar dependence on discharge, also agreeing that this parameter had the most severe influence on the modeled saltwater intrusion. Especially for discharges below 10 m/s3, the saltwater was predicted to reach further than 10 km. In the models, both sea-level rise and river widening mainly resulted in salinity increments up to 3 kg/m3 in the middle part of the river. The predicted sea-level rise in 2070 was simulated to lead to an increase of 0.5 km in saltwater intrusion length. Furthermore, the effect of the saltwater intrusion dam seemed significant in the model used, but only for the highest position of the gate.

Keywords: Cai River, hydraulic models, river discharge, saltwater intrusion, tidal barriers

Procedia PDF Downloads 112

Authors: Omar Alzeley, Sergey Utev

Abstract:

Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

Procedia PDF Downloads 469

Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

Abstract:

In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: information theory, prediction model, prosthetic alignment, transtibial prosthesis

Procedia PDF Downloads 257

Authors: Zhilin Wang, Zhihao Zheng, Linxin Liu

Abstract:

This study proposes a low-code model-building approach that aims to simplify the development and deployment of artificial intelligence (AI) models. With an intuitive way to drag and drop and connect components, users can easily build complex models and integrate multiple algorithms for training. After the training is completed, the system automatically generates a callable model service API. This method not only lowers the technical threshold of AI development and improves development efficiency but also enhances the flexibility of algorithm integration and simplifies the deployment process of models. The core strength of this method lies in its ease of use and efficiency. Users do not need to have a deep programming background and can complete the design and implementation of complex models with a simple drag-and-drop operation. This feature greatly expands the scope of AI technology, allowing more non-technical people to participate in the development of AI models. At the same time, the method performs well in algorithm integration, supporting many different types of algorithms to work together, which further improves the performance and applicability of the model. In the experimental part, we performed several performance tests on the method. The results show that compared with traditional model construction methods, this method can make more efficient use, save computing resources, and greatly shorten the model training time. In addition, the system-generated model service interface has been optimized for high availability and scalability, which can adapt to the needs of different application scenarios.

Keywords: low-code, model building, artificial intelligence, algorithm integration, model deployment

Procedia PDF Downloads 31