Search results for: Mumford and Shah model

Authors: Mustapha Kamel Mihoubi, Hocine Dahmani

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Over recent years much progress has been achieved in the fields of numerical modeling shoreline processes: waves, currents, waves and current. However, there are still some problems in the existing models to link the on the first, the hydrodynamics of waves and currents and secondly, the sediment transport processes and due to the variability in time, space and interaction and the simultaneous action of wave-current near the shore. This paper is the establishment of a numerical modeling to forecast the sediment transport from development scenarios of harbor structure. It is established on the basis of a numerical simulation of a water-sediment model via a 2D model using a set of codes calculation MIKE 21-DHI software. This is to examine the effect of the sediment transport drivers following the dominant incident wave in the direction to pass input harbor work under different variants planning studies to find the technical and economic limitations to the sediment transport and protection of the harbor structure optimum solution.

Keywords: swell, current, radiation, stress, mesh, mike21, sediment

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Authors: Nadia Adnan, Muhammad Mohsin Raza, Latha Ravindran

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Based on the principles above, the study presents an economic model of food waste for consumers, intermediaries, and producers. We discriminate between purchasing and selling, purchases versus customers consumption, and gross output versus sales for each intermediary. To compensate for waste at each level of the supply chain, agents must charge higher sales prices. The research model can produce more accurate predictions about how actions (public regulations or private efforts) to reduce food waste impact markets, including indirect (cascading) effects. With a formal model, researchers demonstrate the uniqueness of these interaction effects and simulate an empirical model calibrated to market characteristics and waste rates in Saudi Arabia. Researchers demonstrate that the effects of waste reduction differ per commodity, depending on supply and demand elasticities, degree of openness to international commerce, and the beginning rates of food loss and waste at each level of the value chain. Because of the consequential effects related to the supply chain, initiatives to minimize food waste will be strengthened in some circumstances and partially countered in others.

Keywords: food loss, food waste, supply chain management, Saudi Arabia, food supply

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Authors: Tai-Yue Wang, Yi-Ho Chen

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In this study, we investigate a two-echelon supply chain with two suppliers and three retailers among which one retailer dominates other retailers. A price competition demand function is used to model this dominant retailer, which is leading market. The promotion strategies and negotiation schemes are integrated to form decision-making models under different scenarios. These models are then formulated into different mathematical programming models. The decision variables such as promotional costs, retailer prices, wholesale price, and order quantity are included in these models. At last, the distributions of promotion costs under different cost allocation strategies are discussed. Finally, an empirical example used to validate our models. The results from this empirical example show that the profit model will create the largest profit for the supply chain but with different profit-sharing results. At the same time, the more risk a member can take, the more profits are distributed to that member in the utility model.

Keywords: supply chain, price promotion, mathematical models, dominant retailer

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Authors: András Szekrényes

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Delamination is one of the major failure modes in laminated composite structures. Delamination tips are mostly captured by spatial numerical models in order to predict crack growth. This paper presents some mechanical models of delaminated composite shells based on shallow shell theories. The mechanical fields are based on a third-order displacement field in terms of the through-thickness coordinate of the laminated shell. The undelaminated and delaminated parts are captured by separate models and the continuity and boundary conditions are also formulated in a general way providing a large size boundary value problem. The system of differential equations is solved by the state space method for an elliptic delaminated shell having simply supported edges. The comparison of the proposed and a numerical model indicates that the primary indicator of the model is the deflection, the secondary is the widthwise distribution of the energy release rate. The model is promising and suitable to determine accurately the J-integral distribution along the delamination front. Based on the proposed model it is also possible to develop finite elements which are able to replace the computationally expensive spatial models of delaminated structures.

Keywords: J-integral, levy method, third-order shell theory, state space solution

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Authors: Andi Darmawan, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Diabetes mellitus (DM) is a chronic disease in human that occurred if pancreas cannot produce enough of insulin hormone or the body uses ineffectively insulin hormone which causes increasing level of glucose in the blood, or it was called hyperglycemia. In Indonesia, DM is a serious disease on health because it can cause blindness, kidney disease, diabetic feet (gangrene), and stroke. The type of DM criteria can also be divided based on the main causes; they are DM type 1, type 2, and gestational. Diabetes type 1 or previously known as insulin-independent diabetes is due to a lack of production of insulin hormone. Diabetes type 2 or previously known as non-insulin dependent diabetes is due to ineffective use of insulin while gestational diabetes is a hyperglycemia that found during pregnancy. The most one type commonly found in patient is DM type 2. The main factors of this disease are genetic (A) and life style (E). Those disease with 2 factors can be constructed with additive genetic and unique environment (AE) model. In this article was discussed parameter estimation of AE model using Bayesian method and the inheritance character simulation on parent-offspring. On the AE model, there are response variable, predictor variables, and parameters were capable of representing the number of population on research. The population can be measured through a taken random sample. The response and predictor variables can be determined by sample while the parameters are unknown, so it was required to estimate the parameters based on the sample. Estimation of AE model parameters was obtained based on a joint posterior distribution. The simulation was conducted to get the value of genetic variance and life style variance. The results of simulation are 0.3600 for genetic variance and 0.0899 for life style variance. Therefore, the variance of genetic factor in DM type 2 is greater than life style.

Keywords: AE model, Bayesian method, diabetes mellitus type 2, genetic, life style

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Authors: V. Pourmostaghimi, M. Zadshakoyan

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Efficiency and productivity of the finish hard turning can be enhanced impressively by utilizing accurate predictive models for cutting tool wear. However, the ability of genetic programming in presenting an accurate analytical model is a notable characteristic which makes it more applicable than other predictive modeling methods. In this paper, the genetic equation for modeling of tool flank wear is developed with the use of the experimentally measured flank wear values and genetic programming during finish turning of hardened AISI D2. Series of tests were conducted over a range of cutting parameters and the values of tool flank wear were measured. On the basis of obtained results, genetic model presenting connection between cutting parameters and tool flank wear were extracted. The accuracy of the genetically obtained model was assessed by using two statistical measures, which were root mean square error (RMSE) and coefficient of determination (R²). Evaluation results revealed that presented genetic model predicted flank wear over the study area accurately (R² = 0.9902 and RMSE = 0.0102). These results allow concluding that the proposed genetic equation corresponds well with experimental data and can be implemented in real industrial applications.

Keywords: cutting parameters, flank wear, genetic programming, hard turning

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Authors: Djarot B. Darmadi

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The wider growing Finite Element Method (FEM) application is caused by its benefits of cost saving and environment friendly. Also, by using FEM a deep understanding of certain phenomenon can be achieved. This paper observed the role of material properties and volumetric change when Solid State Phase Transformation (SSPT) takes place in residual stress formation due to a welding process of ferritic steels through coupled Thermo-Metallurgy-Mechanical (TMM) analysis. The correctness of FEM residual stress prediction was validated by experiment. From parametric study of the FEM model, it can be concluded that the material properties change tend to over-predicts residual stress in the weld center whilst volumetric change tend to underestimates it. The best final result is the compromise of both by incorporates them in the model which has a better result compared to a model without SSPT.

Keywords: residual stress, ferritic steels, SSPT, coupled-TMM

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Authors: Syed Sibtain Hussain Shah

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The Afghan Taliban returned to power corridors in Afghanistan in August 2021 after waging a twenty-year insurgency in the country. U.S.-led forces completed their withdrawal from Afghanistan on August 30, 2021, but the Taliban took control of the whole country till August 15, 2021. At the same time, some of the militant groups such as Tehrik-e-Taliban Pakistan (TTP) and Islamic State Khurasan (IS-K) reappeared in Pakistan’s borders and other areas and by increasing attacks on the armed forces of Pakistan and minorities communities. These groups once again created a crucial challenge to the internal security of the country. Since mid of 2021, many of the terrorist incidents in the countries specified in the areas of Pakistan bordering Afghanistan were committed by TTP and IS-K. The aim of this paper is to investigate the reappearance of TTP and IS-K in 2021 and 2022 as a crucial threat to the internal security of Pakistan. The author will particularly probe threats to the security of military personnel and their installations and threats to human security, including danger to religious minority communities in the different areas of the country, including border areas such as Waziristan, which was once a hub of TTP and other militant groups in the 2000s. The author will employ the relevant method and appropriate theories of security studies, such as religious extremism and terrorism, in this study. TTP, inspired by the Afghan Taliban, initially emerged in Pakistan in 2007 and this group has so far targeted various religious and ethnic communities and government installations in Pakistan. The group is not only against Pakistan’s government policies, but it also committed terrorist attacks on the communities of the other Muslim sects and as well as non-Muslim communities. Most of the prominent figures of this violent group disappeared or escaped to Afghanistan after military actions, such as the larger “Zarb-e-Azb” operation in Pakistan in 2015. IS-K, which established its branch of Khurasan covering Pakistan and Afghanistan in 2015, with its main formation in Iraq and Syria in 2015, by targeting religious minorities such as Shia Muslims, has so far created a vital security challenge for the security of the country.

Keywords: Pakistan, Afghanistan, Afghan Taliban, Pakistani Taliban, Islamic state Khorasan, security threat

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Authors: Gleda Kutrolli, Maksi Kutrolli, Etjon Meco

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SARS-CoV-2 virus is currently one of the most infectious pathogens for humans. It started in China at the end of 2019 and now it is spread in all over the world. The origin and diffusion of the SARS-CoV-2 epidemic, is analysed based on the discussion of viral phylogeny theory. With the aim of understanding the spread of infection in the affected countries, it is crucial to modelize the spread of the virus and simulate its activity. In this paper, the prediction of coronavirus outbreak is done by using SIR model without vital dynamics, applying different numerical technique solving ordinary differential equations (ODEs). We find out that ABM and MRT methods perform better than other techniques and that the activity of the virus will decrease in April but it never cease (for some time the activity will remain low) and the next cycle will start in the middle July 2020 for Norway and Denmark, and October 2020 for Sweden, and September for Finland.

Keywords: forecasting, ordinary differential equations, SARS-COV-2 epidemic, SIR model

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Authors: Sanchay Vaidya, Sourabh Gupta, Gouresh Singhal

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There are various modes of transport in metro cities in India, auto-rickshaws being one of them. Auto-rickshaws provide connectivity to all the places in the city offering last mile connectivity. Among all the modes of transport, the auto-rickshaw industry is the most unorganized and inefficient. Although unions exist in different cities they aren’t good enough to cope up with the upcoming advancements in the field of technology. An introduction of simple technology in this field may do wonder and help increase the revenues. This paper aims to organize this segment under a single umbrella using GPS devices and mobile phones. The paper includes surveys of about 300 auto-rickshaw drivers and 1000 plus commuters across 6 metro cities in India. Carrying out research and analysis provides a base for the development of this model and implementation of this innovative technique, which is discussed in this paper in detail with ample emphasis given on the implementation of this model.

Keywords: auto-rickshaws, business model, GPS device, mobile application

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Authors: M. Brandt, A. Peniak, J. Makarovič, P. Rafajdus

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This paper deals with a novel approach of power transformers diagnostics. This approach identifies the exact location and the range of a fault in the transformer and helps to reduce operation costs related to handling of the faulty transformer, its disassembly and repair. The advantage of the approach is a possibility to simulate healthy transformer and also all faults, which can occur in transformer during its operation without its disassembling, which is very expensive in practice. The approach is based on creating frequency dependent impedance of the transformer by sweep frequency response analysis measurements and by 3D FE parametrical modeling of the fault in the transformer. The parameters of the 3D FE model are the position and the range of the axial short circuit. Then, by comparing the frequency dependent impedances of the parametrical models with the measured ones, the location and the range of the fault is identified. The approach was tested on a real transformer and showed high coincidence between the real fault and the simulated one.

Keywords: transformer, parametrical model of transformer, fault, sweep frequency response analysis, finite element method

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Authors: M. Kiran Reddy, K. Sreenivasa Rao

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The conventional Hidden Markov Model (HMM)-based speech synthesis system (HTS) uses only a pulse excitation model, which significantly differs from natural excitation signal. Hence, buzziness can be perceived in the speech generated using HTS. This paper proposes an efficient excitation modeling method that can significantly reduce the buzziness, and improve the quality of HMM-based speech synthesis. The proposed approach models the pitch-synchronous residual frames extracted from the residual excitation signal. Each pitch synchronous residual frame is parameterized using 30 wavelet coefficients. These 30 wavelet coefficients are found to accurately capture the perceptually important information present in the residual waveform. In synthesis phase, the residual frames are reconstructed from the generated wavelet coefficients and are pitch-synchronously overlap-added to generate the excitation signal. The proposed excitation modeling method is integrated into HMM-based speech synthesis system. Evaluation results indicate that the speech synthesized by the proposed excitation model is significantly better than the speech generated using state-of-the-art excitation modeling methods.

Keywords: excitation modeling, hidden Markov models, pitch-synchronous frames, speech synthesis, wavelet coefficients

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Authors: Ali Bakari Mohammed

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Transitioning is a continuous process of radical change in a society which involves co-evolution of institutional, technological, socio-cultural, and ecological developments at different scales and levels. Transition management model is a methodology that influences structural change of complex systems over a period (0-30 years) by experimenting and implementing new techniques. A transition management in the context of groundwater is a radical change from the current operate and control system to a next generation integrated and sustainable system that takes into account quality protection and sustained supply into the future. This study evaluates the transition management model in adopting it as a viable tool for the attainment of sustainable groundwater management. The outcome of the evaluation shows that there are three levels (strategic, tactical and operational) of operating the transition management model. At the strategic level, long-term goals for sustainable groundwater management are formulated, at the tactical level activities such as inter institutional networking, negotiation, planning and financing are carried out, and at the operational level, transition experiments and strategic niche management are carried out at the societal level. Overall, different actors and set of activities are required to partake at each management level. The outcome of this paper will provide basis for the implementation of the Sustainable Development Goal (SDG) 6 in Nigeria.

Keywords: transition management, groundwater, sustainable management, tool, Nigeria

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Authors: Parth Shah

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The author examines the recent trend where business organizations are significantly reducing their developmental cycle times to stay competitive in today’s global marketspace. The author proposes a rapid systems engineering framework to address late design changes and allow for flexibility (i.e. to react to unexpected or late changes and its impacts) during the product development cycle using a Systems Engineering approach. A System Engineering approach is crucial in today’s product development to deliver complex products into the marketplace. Design changes can occur due to shortened timelines and also based on initial consumer feedback once a product or service is in the marketplace. The ability to react to change and address customer expectations in a responsive and cost-efficient manner is crucial for any organization to succeed. Past literature, research, and methods such as concurrent development, simultaneous engineering, knowledge management, component sharing, rapid product integration, tailored systems engineering processes, and studies on reducing product development cycles all suggest a research gap exist in specifically addressing late design changes due to the shortening of life cycle environments in increasingly competitive markets. The author’s research suggests that 1) product development cycles time scales are now measured in months instead of years, 2) more and more products have interdepended systems and environments that are fast-paced and resource critical, 3) product obsolesce is higher and more organizations are releasing products and services frequently, and 4) increasingly competitive markets are leading to customization based on consumer feedback. The author will quantify effectiveness with respect to success factors such as time-to-market, return-of-investment, life cycle time and flexibility in late design changes by complexity of product or service, number of late changes and ability to react and reduce late design changes.

Keywords: product development, rapid systems engineering, scalability, systems engineering, systems integration, systems life cycle

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Authors: Shivahari Revathi Venkateswaran

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Segmenting customers plays a significant role in churn prediction. It helps the marketing team with proactive and reactive customer retention. For the reactive retention, the retention team reaches out to customers who already showed intent to disconnect by giving some special offers. When coming to proactive retention, the marketing team uses churn prediction model, which ranks each customer from rank 1 to 100, where 1 being more risk to churn/disconnect (high ranks have high propensity to churn). The churn prediction model is built by using XGBoost model. However, with the churn rank, the marketing team can only reach out to the customers based on their individual ranks. To profile different groups of customers and to frame different marketing strategies for targeted groups of customers are not possible with the churn ranks. For this, the customers must be grouped in different segments based on their profiles, like demographics and other non-controllable attributes. This helps the marketing team to frame different offer groups for the targeted audience and prevent them from disconnecting (proactive retention). For segmentation, machine learning approaches like k-mean clustering will not form unique customer segments that have customers with same attributes. This paper finds an alternate approach to find all the combination of unique segments that can be formed from the user attributes and then finds the segments who have uplift (churn rate higher than the baseline churn rate). For this, search algorithms like fast search and recursive search are used. Further, for each segment, all customers can be targeted using individual churn ranks from the churn prediction model. Finally, a UI (User Interface) is developed for the marketing team to interactively search for the meaningful segments that are formed and target the right set of audience for future marketing campaigns and prevent them from disconnecting.

Keywords: churn prediction modeling, XGBoost model, uplift segments, proactive marketing, search algorithms, retention, k-mean clustering

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Authors: Ankit Shah, R. K. Shrivastava

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Flood and drought are two main features of hydrology which affect the human life. Floods are natural disasters which cause millions of rupees’ worth of damage each year in India and the whole world. Flood causes destruction in form of life and property. An accurate estimate of the flood damage potential is a key element to an effective, nationwide flood damage abatement program. Also, the increase in demand of water due to increase in population, industrial and agricultural growth, has let us know that though being a renewable resource it cannot be taken for granted. We have to optimize the use of water according to circumstances and conditions and need to harness it which can be done by construction of hydraulic structures. For their safe and proper functioning of hydraulic structures, we need to predict the flood magnitude and its impact. Hydraulic structures play a key role in harnessing and optimization of flood water which in turn results in safe and maximum use of water available. Mainly hydraulic structures are constructed on ungauged sites. There are two methods by which we can estimate flood viz. generation of Unit Hydrographs and Flood Frequency Analysis. In this study, Regional Flood Frequency Analysis has been employed. There are many methods for estimating the ‘Regional Flood Frequency Analysis’ viz. Index Flood Method. National Environmental and Research Council (NERC Methods), Multiple Regression Method, etc. However, none of the methods can be considered universal for every situation and location. The Narmada basin is located in Central India. It is drained by most of the tributaries, most of which are ungauged. Therefore it is very difficult to estimate flood on these tributaries and in the main river. As mentioned above Artificial Neural Network (ANN)s and Multiple Regression Method is used for determination of Regional flood Frequency. The annual peak flood data of 20 sites gauging sites of Narmada Basin is used in the present study to determine the Regional Flood relationships. Homogeneity of the considered sites is determined by using the Index Flood Method. Flood relationships obtained by both the methods are compared with each other, and it is found that ANN is more reliable than Multiple Regression Method for the present study area.

Keywords: artificial neural network, index flood method, multi layer perceptrons, multiple regression, Narmada basin, regional flood frequency

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Authors: Assaad Ghazouani, Ati Abdessatar

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In the world, agriculture maintains a social and economic importance in the national economy. Its importance is distinguished by its ripple effects not only downstream but also upstream vis-à-vis the non-agricultural sector. However, the situation is relatively fragile because of weather conditions. In this work, we propose a model to highlight the impacts of climate change (CC) on economic growth in the world where agriculture is considered as a strategic sector. The CC is supposed to directly and indirectly affect economic growth by reducing the performance of the agricultural sector. The model is tested for Tunisia. The results validate the hypothesis that the potential economic damage of the CC is important. Indeed, an increase in CO2 concentration (temperatures and disruption of rainfall patterns) will have an impact on global economic growth particularly by reducing the performance of the agricultural sector. Analysis from a vector error correction model also highlights the magnitude of climate impact on the performance of the agricultural sector and its repercussions on economic growth

Keywords: Climate Change, Agriculture, Economic Growth, World, VECM, Cointegration.

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Authors: Fang Gong

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Distance measures are an indispensable part of many instance-based learning (IBL) and machine learning (ML) algorithms. The value difference metrics (VDM) and inverted specific-class distance measure (ISCDM) are among the top-performing distance measures that address nominal attributes. VDM performs well in some domains owing to its simplicity and poorly in others that exist missing value and non-class attribute noise. ISCDM, however, typically works better than VDM on such domains. To maximize their advantages and avoid disadvantages, in this paper, a selection approach: a discriminative model for nominal attributes-based distance measures is proposed. More concretely, VDM and ISCDM are built independently on a training dataset at the training stage, and the most credible one is recorded for each training instance. At the test stage, its nearest neighbor for each test instance is primarily found by any of VDM and ISCDM and then chooses the most reliable model of its nearest neighbor to predict its class label. It is simply denoted as a discriminative distance measure (DDM). Experiments are conducted on the 34 University of California at Irvine (UCI) machine learning repository datasets, and it shows DDM retains the interpretability and simplicity of VDM and ISCDM but significantly outperforms the original VDM and ISCDM and other state-of-the-art competitors in terms of accuracy.

Keywords: distance measure, discriminative model, nominal attributes, nearest neighbor

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Authors: Alix Limoges, William Bruch, Christophe Yohia, Jacques Piazzola

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A number of questions arise concerning the long-term impact of the contribution of marine aerosol fluxes generated at the air-sea interface on the occurrence of intense events (storms, floods, etc.) in the coastal environment. To this end, knowledge is needed on sea-spray emission rates and the atmospheric dynamics of the corresponding particles. Our aim is to implement the mesoscale model MESO-NH on the study area using an accurate sea-spray source function to estimate heat fluxes and impact on the precipitations. Based on an original and complete sea-spray source function, which covers a large size spectrum since taking into consideration the sea-spray produced by both bubble bursting and surface tearing process, we propose a comparison between model simulations and experimental data obtained during an oceanic scientific cruise on board the navy ship Atalante. The results show the relevance of the sea-spray flux calculations as well as their impact on the heat fluxes and AOD.

Keywords: atmospheric models, sea-spray source, sea-spray dynamics, aerosols

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Authors: Jui-Chen Huang, Shou-Hsiung Cheng

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This study examined their willingness to use telecare for people who have had experience falling in the last three months in Taiwan. This study adopted convenience sampling and a structural questionnaire to collect data. It was based on the definition and the constructs related to the Health Belief Model (HBM). HBM is comprised of seven constructs: perceived benefits (PBs), perceived disease threat (PDT), perceived barriers of taking action (PBTA), external cues to action (ECUE), internal cues to action (ICUE), attitude toward using (ATT), and behavioral intention to use (BI). This study adopted Fuzzy Neural Network (FNN) to put forward an effective method. It shows the dependence of ATT on PB, PDT, PBTA, ECUE, and ICUE. The training and testing data RMSE (root mean square error) are 0.028 and 0.166 in the FNN, respectively. The training and testing data RMSE are 0.828 and 0.578 in the regression model, respectively. On the other hand, as to the dependence of ATT on BI, as presented in the FNN, the training and testing data RMSE are 0.050 and 0.109, respectively. The training and testing data RMSE are 0.529 and 0.571 in the regression model, respectively. The results show that the FNN method is better than the regression analysis. It is an effective and viable good way.

Keywords: fall, fuzzy neural network, health belief model, telecare, willingness

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Authors: Tony Mann

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This paper sets out the case for involving and engaging employees/workers/stakeholders/staff in any significant change that is being considered by the senior executives of the organization. It establishes the rationale, the approach, the methodology of engagement and the benefits of a participative approach. It challenges the new norm of imposing change for fear of resistance and instead suggests that involving people has better outcomes and a longer-lasting impact. Various strategic models are introduced and illustrated to explain how the process can be most effective. The paper highlights one model in particular (the Process Iceberg® Organizational Change model) that has proven to be instrumental in developing effective change. Its use is demonstrated in its various forms and explains why so much change fails to address the key elements and how we can be more productive in managing change. ‘Participation’ in change is too often seen as negative, expensive and unwieldy. The paper aims to show that another model: UIA=O+E, can offset the difficulties and, in fact, produce much more positive and effective change.

Keywords: facilitation, stakeholders, buy-in, digital workshops

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Authors: L. Donelle, S. Regan, R. Booth, M. Kerr, J. McMurray, D. Fitzsimmons

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Palliative home health care provision within the Canadian context is challenged by: (i) a shortage of registered nurses (RN) and RNs with palliative care expertise, (ii) an aging population, (iii) reliance on unpaid family caregivers to sustain home care services with limited support to conduct this ‘care work’, (iv) a model of healthcare that assumes client self-care, and (v) competing economic priorities. In response, an interprofessional team of service provider organizations, a software/technology provider, and health care providers developed and implemented a technology-enabled model of home care, the eShift model of palliative home care (eShift). The eShift model combines communication and documentation technology with non-traditional utilization of health human resources to meet patient needs for palliative care in the home. The purpose of this study was to investigate the structure, processes, and outcomes of the eShift model of care. Methodology: Guided by Donebedian’s evaluation framework for health care, this qualitative-descriptive study investigated the structure, processes, and outcomes care of the eShift model of palliative home care. Interviews and focus groups were conducted with health care providers (n= 45), decision-makers (n=13), technology providers (n=3) and family care givers (n=8). Interviews were recorded, transcribed, and a deductive analysis of transcripts was conducted. Study Findings (1) Structure: The eShift model consists of a remotely-situated RN using technology to direct care provision virtually to patients in their home. The remote RN is connected virtually to a health technician (an unregulated care provider) in the patient’s home using real-time communication. The health technician uses a smartphone modified with the eShift application and communicates with the RN who uses a computer with the eShift application/dashboard. Documentation and communication about patient observations and care activities occur in the eShift portal. The RN is typically accountable for four to six health technicians and patients over an 8-hour shift. The technology provider was identified as an important member of the healthcare team. Other members of the team include family members, care coordinators, nurse practitioners, physicians, and allied health. (2) Processes: Conventionally, patient needs are the focus of care; however within eShift, the patient and the family caregiver were the focus of care. Enhanced medication administration was seen as one of the most important processes, and family caregivers reported high satisfaction with the care provided. There was perceived enhanced teamwork among health care providers. (3) Outcomes: Patients were able to die at home. The eShift model enabled consistency and continuity of care, and effective management of patient symptoms and caregiver respite. Conclusion: More than a technology solution, the eShift model of care was viewed as transforming home care practice and an innovative way to resolve the shortage of palliative care nurses within home care.

Keywords: palliative home care, health information technology, patient-centred care, interprofessional health care team

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Authors: Ghorbanali Mohammadi

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New approaches to sequencing mixed-model manufacturing systems are present. These approaches have attracted considerable attention due to their potential to deal with difficult optimization problems. This paper presents Multi-Objective Simulated Annealing Algorithms (MOSAA) approaches to the Just-In-Time (JIT) sequencing problem where workload-smoothing (WL) and the number of set-ups (St) are to be optimized simultaneously. Mixed-model assembly lines are types of production lines where varieties of product models similar in product characteristics are assembled. Moreover, this type of problem is NP-hard. Two annealing methods are proposed to solve the multi-objective problem and find an efficient frontier of all design configurations. The performances of the two methods are tested on several problems from the literature. Experimentation demonstrates the relative desirable performance of the presented methodology.

Keywords: scheduling, just-in-time, mixed-model assembly line, sequencing, simulated annealing

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Authors: Abdelkader Hachemi, Boualem Remini

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Seawater intrusion is a significant challenge faced by coastal aquifers in the Mediterranean basin. This study aims to determine the position of the sharp interface between seawater and freshwater in the aquifer of Wadi Nador, located in the Wilaya of Tipaza, Algeria. A numerical areal sharp interface model using the finite element method is developed to investigate the spatial and temporal behavior of seawater intrusion. The aquifer is assumed to be homogeneous and isotropic. The simulation results are compared with geophysical prospection data obtained through electrical methods in 2011 to validate the model. The simulation results demonstrate a good agreement with the geophysical prospection data, confirming the accuracy of the sharp interface model. The position of the sharp interface in the aquifer is found to be approximately 1617 meters from the sea. Two scenarios are proposed to predict the interface position for the year 2024: one without pumping and the other with pumping. The results indicate a noticeable retreat of the sharp interface position in the first scenario, while a slight decline is observed in the second scenario. The findings of this study provide valuable insights into the dynamics of seawater intrusion in the Wadi Nador aquifer. The predicted changes in the sharp interface position highlight the potential impact of pumping activities on the aquifer's vulnerability to seawater intrusion. This study emphasizes the importance of implementing measures to manage and mitigate seawater intrusion in coastal aquifers. The sharp interface model developed in this research can serve as a valuable tool for assessing and monitoring the vulnerability of aquifers to seawater intrusion.

Keywords: seawater intrusion, sharp interface, coastal aquifer, algeria

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Authors: A. Azari, S. S. K. Seyyedi

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One of the unaddressed and open challenges in the nano-networking is the characteristics of noise. The previous analysis, however, has concentrated on end-to-end communication model with no separate modelings for propagation channel and noise. By considering a separate signal propagation and noise model, the design and implementation of an optimum receiver will be much easier. In this paper, we justify consideration of a separate additive Gaussian noise model of a nano-communication system based on the molecular communication channel for which are applicable for MSK and MOSK modulation schemes. The presented noise analysis is based on the Brownian motion process, and advection molecular statistics, where the received random signal has a probability density function whose mean is equal to the mean number of the received molecules. Finally, the justification of received signal magnitude being uncorrelated with additive non-stationary white noise is provided.

Keywords: molecular, noise, diffusion, channel

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Authors: Falguni Vasavada, Pradyumna Malladi

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The research focuses on studying the ecosystem of the youth using Ogilvy's 3E model, Ethnography and Thematic Analysis. It has been found that urban Indian youth today is an honest generation, hungry for success, living life by the moment, fiercely independent, are open about sex, sexuality and embrace individual differences. Technology and social media dominate their life. However, they are also phobic about commitments, often drifting along life and engage in unsubstantiated brave-talk.

Keywords: ethnography, youth, culture, track, buyer behavior

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Authors: Davood Rajabi, Mojgan Yazdani

Abstract:

Non-linear Muskingum model is an efficient method for flood routing, however, the efficiency of this method is influenced by three applied parameters. Therefore, efficiency assessment of Imperialist Competition Algorithm (ICA) to evaluate optimum parameters of non-linear Muskingum model was addressed through this study. In addition to ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were also used aiming at an available criterion to verdict ICA. In this regard, ICA was applied for Wilson flood routing; then, routing of two flood events of DoAab Samsami River was investigated. In case of Wilson flood that the target function was considered as the sum of squared deviation (SSQ) of observed and calculated discharges. Routing two other floods, in addition to SSQ, another target function was also considered as the sum of absolute deviations of observed and calculated discharge. For the first floodwater based on SSQ, GA indicated the best performance, however, ICA was on first place, based on SAD. For the second floodwater, based on both target functions, ICA indicated a better operation. According to the obtained results, it can be said that ICA could be used as an appropriate method to evaluate the parameters of Muskingum non-linear model.

Keywords: Doab Samsami river, genetic algorithm, imperialist competition algorithm, meta-exploratory algorithms, particle swarm optimization, Wilson flood

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Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

Abstract:

Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

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Authors: Virendra J. Majarikar, Harikrishnan N. Unni

Abstract:

This paper sets to demonstrate a modeling of electrokinetic mixing employing electroosmotic stationary and time-dependent microchannel using alternate zeta patches on the lower surface of the micromixer in a lab on chip microfluidic device. Electroosmotic flow is amplified using different 2D and 3D model designs with alternate and geometric zeta potential values such as 25, 50, and 100 mV, respectively, to achieve high concentration mixing in the electrokinetically-driven microfluidic system. The enhancement of electrokinetic mixing is studied using Finite Element Modeling, and simulation workflow is accomplished with defined integral steps. It can be observed that the presence of alternate zeta patches can help inducing microvortex flows inside the channel, which in turn can improve mixing efficiency. Fluid flow and concentration fields are simulated by solving Navier-Stokes equation (implying Helmholtz-Smoluchowski slip velocity boundary condition) and Convection-Diffusion equation. The effect of the magnitude of zeta potential, the number of alternate zeta patches, etc. are analysed thoroughly. 2D simulation reveals that there is a cumulative increase in concentration mixing, whereas 3D simulation differs slightly with low zeta potential as that of the 2D model within the T-shaped micromixer for concentration 1 mol/m³ and 0 mol/m³, respectively. Moreover, 2D model results were compared with those of 3D to indicate the importance of the 3D model in a microfluidic design process.

Keywords: COMSOL Multiphysics®, electrokinetic, electroosmotic, microfluidics, zeta potential

Procedia PDF Downloads 242

Authors: Vasileios Anastopoulos

Abstract:

Cyber threat intelligence assists organizations in understanding the threats they face and helps them make educated decisions on preparing their defenses. Sharing of threat intelligence and threat information is increasingly leveraged by organizations and enterprises, and various software solutions are already available, with the open-source malware information sharing platform (MISP) being a popular one. In this work, a methodology for the production of cyber threat intelligence using the threat information stored in MISP is proposed. The methodology leverages the discipline of social network analysis and the diamond model, a model used for intrusion analysis, to produce cyber threat intelligence. The workings are demonstrated with a case study on a production MISP instance of a real organization. The paper concluded with a discussion on the proposed methodology and possible directions for further research.

Keywords: cyber threat intelligence, diamond model, malware information sharing platform, social network analysis

Procedia PDF Downloads 178