Search results for: proposed drought severity index

Authors: Piyush V. Surjagade, Jiamei Deng, Paul Doney, S. R. Shimjith, A. John Arul

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In sophisticated and complex systems such as nuclear power plants, maintaining the system's stability in the presence of uncertainties and disturbances and obtaining a fast dynamic response are the most challenging problems. Thus, to ensure the satisfactory and safe operation of nuclear power plants, this work proposes a new fast, robust optimal switching control strategy for pressurized water reactor-type nuclear power plants. The proposed control strategy guarantees a substantial degree of robustness, fast dynamic response over the entire operational envelope, and optimal performance during the nominal operation of the plant. To improve the robustness, obtain a fast dynamic response, and make the system optimal, a bank of controllers is designed. Various controllers, like a baseline proportional-integral-derivative controller, an optimal linear quadratic Gaussian controller, and a robust adaptive L1 controller, are designed to perform distinct tasks in a specific situation. At any instant of time, the most suitable controller from the bank of controllers is selected using the switching logic unit that designates the controller by monitoring the health of the nuclear power plant or transients. The proposed switching control strategy optimizes the overall performance and increases operational safety and efficiency. Simulation studies have been performed considering various uncertainties and disturbances that demonstrate the applicability and effectiveness of the proposed switching control strategy over some conventional control techniques.

Keywords: switching control, robust control, optimal control, nuclear power control

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Authors: Charles Kwasi Antwi, Mohammad Naushad Emmambux, Natalia Rosa-Sibakov

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The consumption of high-fibre snacks is beneficial in reducing the prevalence of most non-communicable diseases and improving human health. However, using high-fibre flour to produce snacks by extrusion cooking reduces the expansion ratio of snacks, thereby decreasing sensory properties and consumer acceptability of the snack. The study determines the effects of adding Viscozyme®-treated sorghum bran on the properties of extruded sorghum snacks with the aim of producing high-fibre expanded snacks with acceptable quality. With a twin-screw extruder, sorghum endosperm flour [by decortication] with and without sorghum bran and with enzyme-treated sorghum bran was extruded at high shear rates with feed moisture of 20%, feed rate of 10 kg/hr, screw speed of 500 rpm, and temperature zones of 60°C, 70°C, 80°C, 140°C, and 140°C toward the die. The expanded snacks that resulted from this process were analysed in terms of their physical (expansion ratio, bulk density, colour profile), chemical (soluble and insoluble dietary fibre), and functional (water solubility index (WSI) and water absorption index (WAI)) characteristics. The expanded snacks produced from refined sorghum flour enriched with Viscozyme-treated bran had similar expansion ratios to refined sorghum flour extrudates, which were higher than those for untreated bran-sorghum extrudate. Sorghum extrudates without bran showed higher values of expansion ratio and low values of bulk density compared to the untreated bran extrudates. The enzyme-treated fibre increased the expansion ratio significantly with low bulk density values compared to untreated bran. Compared to untreated bran extrudates, WSI values in enzyme-treated samples increased, while WAI values decreased. Enzyme treatment of bran reduced particle size and increased soluble dietary fibre to increase expansion. Lower particle size suggests less interference with bubble formation at the die. Viscozyme-treated bran-sorghum composite flour could be used as raw material to produce high-fibre expanded snacks with improved physicochemical and functional properties.

Keywords: extrusion, sorghum bran, decortication, expanded snacks

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Authors: Gherbi Nabil

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Detection of ground targets in SAR radar images is an important area for radar information processing. In the literature, various algorithms have been discussed in this context. However, most of them are of low robustness and accuracy. To this end, we discuss target detection in SAR images based on compressive sensing. Firstly, traditional SAR image target detection algorithms are discussed, and their limitations are highlighted. Secondly, a compressive sensing method is proposed based on the sparsity of SAR images. Next, the detection problem is solved using Multiple Measurements Vector configuration. Furthermore, a robust Alternating Direction Method of Multipliers (ADMM) is developed to solve the optimization problem. Finally, the detection results obtained using raw complex data are presented. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

Keywords: compressive sensing, raw complex data, synthetic aperture radar, ADMM

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Authors: Wang Zhanquan, Yang Zeping, Gu Chunhua, Zhu Fazhi, Guo Weibin

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Recently, Massive Open Online Courses (MOOCs) are becoming the new trend of education. There are many problems under the environment of Database Principle curriculum teaching process in MOOCs, such as teaching ideas and theories which are out of touch with the reality, how to carry out the technical teaching and interactive practice in the MOOCs environment, thus the methods of database course under the environment of MOOCs are proposed. There are three processes to deal with problem solving in the research, which are problems proposed, problems solved, and inductive analysis. The present research includes the design of teaching contents, teaching methods in classroom, flipped classroom teaching mode under the environment of MOOCs, learning flow method and large practice homework. The database designing ability is systematically improved based on the researching methods.

Keywords: problem solving-driven, MOOCs, teaching art, learning flow;

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Authors: Majeda S. Hammoud

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Background and Aim: Gestational diabetes mellitus (GDM) is a common pregnancy complication affecting around 14% of pregnancies globally that carries short and long-term consequences to the mother and her child. Pre-pregnancy overweight or obesity is the most consistently and strongly associated modifiable risk factor with GDM development. This analysis aimed to determine whether vitamin D status during pregnancy modulates the effect of pre-pregnancy obesity/overweight on GDM risk while stratifying by maternal age. Methods: Data from the Kuwait Birth Cohort (KBC) study were analyzed, which enrolled pregnant women in the second or third trimester of gestation. Pre-pregnancy body mass index (BMI; kg/m2) was categorized as under/normal weight (<25.0), overweight (25.0 to <30.0), and obesity (≥30.0). 25 hydroxyvitamin D levels were measured in blood samples that were collected at recruitment and categorized as deficiency (<50 nmol/L) and insufficiency/sufficiency (≥50 nmol/L). GDM status was ascertained according to international guidelines. Logistic regression was used to evaluate associations, and adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated. Results: The analyzed study sample included a total of 982 pregnant women, with a mean (SD) age of 31.4 (5.2) years. The prevalence of GDM was estimated to be 17.3% (95% CI: 14.9-19.7), and the prevalence of pre-pregnancy overweight and obesity was 37.8% (95% CI: 34.8-40.8) and 28.8% (95% CI: 26.0-31.7), respectively. The prevalence of gestational vitamin D deficiency was estimated to be 55.3% (95% CI: 52.2-58.4). The association between pre-pregnancy overweight or obesity with GDM risk differed according to maternal age and gestational vitamin D status (Pinteraction[BMI × age × vitamin D = 0.047). Among pregnant women aged <35 years, prepregnancy obesity compared to under/normal weight was associated with increased GDM risk among women with gestational vitamin D deficiency (aOR: 3.65, 95% CI: 1.50-8.86, p = 0.004) and vitamin D insufficiency/sufficiency (aOR: 2.55, 95% CI: 1.16-5.61, p = 0.019). In contrast, among pregnant women aged ≥35 years, pre-pregnancy obesity compared to under/normal weight was associated with increased GDM risk among women with gestational vitamin D deficiency (aOR: 9.70, 95% CI: 2.01-46.69, p = 0.005), but not among women with vitamin D insufficiency/sufficiency (aOR: 1.46, 95% CI: 0.42-5.16, p = 0.553). Conclusion: The effect of pre-pregnancy obesity on GDM risk is modulated by maternal age and gestational vitamin D status, with the effect of pre-pregnancy obesity being more pronounced among older pregnant women (aged ≥35 years) with gestational vitamin D deficiency compared to those with vitamin D insufficiency/sufficiency. Whereas, among younger women (aged <35 years), the effect of pre-pregnancy obesity on GDM risk was not modulated by gestational vitamin D status. Therefore, vitamin D supplementation among pregnant women, specifically older women with pre-pregnancy obesity, may mitigate the effect of pre-pregnancy obesity on GDM risk.

Keywords: gestational diabetes mellitus, vitamin D, obesity, body mass index

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Authors: Wanatchapong Kongkaew

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This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness

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Authors: Taylin L. Peoples, Lauren Lewis, Sebastian G. Risco, Devin Mills

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The impact of problematic drinking (PD) on the health of U.S. adults continues to be a concerning issue. Additionally, the U.S. Surgeon General recently highlighted the isolation epidemic, bringing attention to the significant and detrimental impact of loneliness. Research has found PD to be associated with deficits in feeling connection towards others. This suggests that one consequence of the isolation epidemic is the greater severity of PD. Further, PD has long been associated with three dark personality traits (i.e., narcissism, Machiavellianism, psychopathy), which may be explained by interpersonal factors but has yet to be examined. Therefore, the present study assessed the extent to which thwarted belongingness (TB) and perceived burdensomeness (PB) explain the relationship between dark personality traits and PD. Data was collected from 606 US adults reporting alcohol consumption. The participants completed the Interpersonal Needs Questionnaire, the Short Dark Triad scale, and the Alcohol Use Disorders Identification Test. Results from a path analysis supported the hypothesis that dark traits are associated with more severe PD through both PB and TB. The present results underscore the role of connection to others, as defined by TB and PB, in facilitating the relationship between dark personality traits and PD. Future research is needed in this area to develop preventative strategies and policies as well as clinical interventions. In sum, the findings offer a novel perspective on the intersection of personality traits, PB and TB, and PD.

Keywords: problem drinking, personality, dark traits, dark traid, thwarted belonginess, perceived burdensomeness

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Authors: Alluru Gopala Krishna, Thella Babu Rao

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In machining, there is always a problem with heat generation and friction produced during the process as they consequently affect tool wear and surface finish. An instant heat transfer mechanism could protect the cutting tool edge and enhance the tool life by cooling the cutting edge of the tool. In the present work, carbon nanotube (CNT) based nano-cutting fluid is proposed for machining a hard-to-cut material. Tool wear and surface roughness are considered for the evaluation of the nano-cutting fluid in turning process. The performance of nanocoolant is assessed against the conventional coolant and dry machining conditions and it is observed that the proposed nanocoolant has produced better performance than the conventional coolant.

Keywords: CNT based nano cutting fluid, tool wear, turning, surface roughness

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Authors: Mohamed Elkholy, Ahmed Elfatatry

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Cloud computing has emerged as a flexible computing paradigm that reshaped the Information Technology map. However, cloud computing brought about a number of security challenges as a result of the physical distribution of computational resources and the limited control that users have over the physical storage. This situation raises many security challenges for data integrity and confidentiality as well as authentication and access control. This work proposes a security mechanism for data integrity that allows a data owner to be aware of any modification that takes place to his data. The data integrity mechanism is integrated with an extended Kerberos authentication that ensures authorized access control. The proposed mechanism protects data confidentiality even if data are stored on an untrusted storage. The proposed mechanism has been evaluated against different types of attacks and proved its efficiency to protect cloud data storage from different malicious attacks.

Keywords: access control, data integrity, data confidentiality, Kerberos authentication, cloud security

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Authors: Behnam Shakerian, Negin Razavi

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The pathogenesis of coronary artery disease is multifactorial. The epicardial fat pad is a localized fat depot lying between the myocardium and the visceral layer of the pericardium. The mechanisms through which epicardial fat pad can cause atherosclerosis are complex. The epicardial fat pad can surround the coronary arteries and contributes to the development and progression of coronary artery disease. Methods: we selected 50 patients who underwent coronary artery angiography for the evaluation of coronary artery disease that results were positive for coronary artery disease. All patients underwent an echocardiographic examination after coronary angiography to measure epicardial fat pad thickness. The epicardial fat pad was defined as an echo-free space between the myocardium's outer wall and the pericardium's visceral layer. Results: The epicardial fat pad was measured on the right ventricle apex in 46 patients. Sixty- five percent of the studied patients were male. The most common vessel with stenosis was the left anterior descending artery. A significant correlation was observed between epicardial fat pad thickness and the severity of coronary artery disease. Discussions: The epicardial fat pad provides a horizon on the pathophysiology of cardiovascular diseases. It directly contributes to the development and progression of coronary artery disease by causing inflammation and endothelial damage. Further investigations are needed to determine whether medical treatment can reduce the mass of epicardial fat pad and can help to improve atherosclerosis. Conclusion: The epicardial fat pad measurement could be used as an indicator of coronary arteries’ atherosclerosis. Therefore, thickness measurement of the epicardial fat pad in the clinical practice could be of assistance in identifying patients at risk and if required, undergoing supplementary diagnosis with coronary angiography.

Keywords: epicardial, fat pad, coronary artery disease, echocardiography

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Authors: A: Annis Fathima, V. Vaidehi, S. Ajitha

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Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach.

Keywords: face recognition, Gabor wavelet, LDA, k-NN classifier

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Authors: M. Yoneda

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In this paper, a lateral walking design force per person is proposed and compared with Imperial College test results. Numerical simulations considering the proposed walking design force which is incorporated into the neural-oscillator model are carried out placing much emphasis on the synchronization (the lock-in phenomenon) for a pedestrian bridge model with the span length of 50 m. Numerical analyses are also conducted for an existing pedestrian suspension bridge. As compared with full scale measurements for this suspension bridge, it is confirmed that the analytical method based on the neural-oscillator model might be one of the useful ways to explain the synchronization (the lock-in phenomenon) of pedestrians being on the bridge.

Keywords: pedestrian bridge, human-induced lateral vibration, neural-oscillator, full scale measurement, dynamic response analysis

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Authors: Saba Riaz, Syed A. Hussain

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This paper is addressing the estimation method of the unknown population variance of the variable of interest. A new generalized class of estimators of the finite population variance has been suggested using the auxiliary information. To improve the precision of the proposed class, known population variance of the auxiliary variable has been used. Mathematical expressions for the biases and the asymptotic variances of the suggested class are derived under large sample approximation. Theoretical and numerical comparisons are made to investigate the performances of the proposed class of estimators. The empirical study reveals that the suggested class of estimators performs better than the usual estimator, classical ratio estimator, classical product estimator and classical linear regression estimator. It has also been found that the suggested class of estimators is also more efficient than some recently published estimators.

Keywords: study variable, auxiliary variable, finite population variance, bias, asymptotic variance, percent relative efficiency

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Authors: Qiankun Zhong

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In a global cultural context, film festival awards become authorities to define the aesthetic value of films. To study which genres and producing countries are valued by different film festivals and how those evaluations interact with each other, this research explored the interactions between the film festivals through their selection of movies and the factors that lead to the tendency of film festivals to nominate the same movies. To do this, the author employed a two-mode network on the movies that won the highest awards at five international film festivals with the highest attendance in the past ten years (the Venice Film Festival, the Cannes Film Festival, the Toronto International Film Festival, Sundance Film Festival, and the Berlin International Film Festival) and the film festivals that nominated those movies. The title, genre, producing country and language of 50 movies, and the range (regional, national or international) and organizing country or area of 129 film festivals were collected. These created networks connected by nominating the same films and awarding the same movies. The author then assessed the density and centrality of these networks to answer the question: What are the film festivals that tend to have more shared values with other festivals? Based on the Eigenvector centrality of the two-mode network, Palm Springs, Robert Festival, Toronto, Chicago, and San Sebastian are the festivals that tend to nominate commonly appreciated movies. In contrast, Black Movie Film Festival has the unique value of generally not sharing nominations with other film festivals. A homophily test was applied to access the clustering effects of film and film festivals. The result showed that movie genres (E-I index=0.55) and geographic location (E-I index=0.35) are possible indicators of film festival clustering. A blockmodel was also created to examine the structural roles of the film festivals and their meaning in real-world context. By analyzing the same blocks with film festival attributes, it was identified that film festivals either organized in the same area, with the same history, or with the same attitude on independent films would occupy the same structural roles in the network. Through the interpretation of the blocks, language was identified as an indicator that contributes to the role position of a film festival. Comparing the result of blockmodeling in the different periods, it is seen that international film festivals contrast with the Hollywood industry’s dominant value. The structural role dynamics provide evidence for a multi-value film festival network.

Keywords: film festivals, film studies, media industry studies, network analysis

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Authors: Yao-Hong Tsai

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This paper presents a weighted approach to unconstrained iris recognition. Nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition.

Keywords: authentication, iris recognition, adaboost, local binary pattern

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Authors: Biao Hu, Mark E. J. Cutler, Alexandra C. Morel

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Global climate change introduces both opportunities and challenges for crop productivity, with differences in temperature stress across latitudes and crop types, one of the most important meteorological factors impacting crop productivity. The development and productivity of crops are particularly impacted when temperatures occur outwith their preferred ranges, which has implications for global agri-food sector. This study investigated the spatiotemporal dynamics of heat stress and cold stress on UK arable lands for rapeseed cropping between 1961 and 2020, using a 1 km spatial resolution temperature dataset. Stress indices, including heat stress index (fHS) defined as the ratio of “Tmax - Tcrit_h” to “Tlimit_h - Tcrit_h” where Tmax, Tcrit_h and Tlimit_h represent the daily maximum temperature (°C), critical high temperature threshold (°C) and limiting high temperature threshold (°C) of rapeseed crop respectively; cold degree days (CDD) as the difference between daily Tmin (minimum temperature) and Tcrit_l (critical low temperature threshold); and a normalized rapeseed production loss index (fRPL) as the product of fHS and attainable rapeseed yield in the same land pixel were established. The values of fHS and CDD, percentages of days experiencing each stress and fRPL were investigated. Results found increasing fHS and the areas impacted by heat stress during flowering (from April to May) and reproductive (from April to July) stages over time, with the mean fHS being negatively correlated with latitude. This pattern of increased heat stress agrees with previous research on rapeseed cropping, which have been noted at global scale in response to changes in climate. The decreasing number of CDD and frequency of cold stress suggest cold stress decreased during flowering, vegetative (from September to March next year) and reproductive stages, and the magnitude of cold stress in the south of the UK was smaller to that compared to northern regions over the studied periods. The decreasing CDD matches observed declining cold stress of global rapeseed and of other crops such as rice in the northern hemisphere. Notably, compared with previous studies which mainly tracked the trends of heat stress and cold stress individually, this study conducted a comparative analysis of the rate of their changes and found heat stress of rapeseed crops in the UK was increasing at a faster rate than cold stress, which was seen to decrease during flowering. The increasing values of fRPL, with statistically significant differences (p < 0.05) between regions of the UK, suggested an increasing loss in rapeseed due to heat stress in the studied period. The largest increasing trend in heat stress was observed in South-eastern England, where a decreasing cold stress was taking place. While the present study observed a relatively slowly increasing heat stress, there is a worrying trend of increasing heat stress for rapeseed cropping into the future, as the cases of other main rapeseed cropping systems in the northern hemisphere including China, European counties, the US, and Canada. This study demonstrates the negative impact of global warming on rapeseed cropping, highlighting the adaptation and mitigations strategies for sustainable rapeseed cultivation across the globe.

Keywords: rapeseed, UK, heat stress, cold stress, global climate change, spatiotemporal analysis, production loss index

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Authors: Sanjay Agrawal, Trapti Varshney, G. N. Tiwari

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In this paper, thermal modeling has been developed for photovoltaic PV modules, namely; Case A: semitransparent PV module without duct, Case B: semitransparent PV module with duct, Case C: opaque PV module without duct, Case D: opaque PV module with duct for Delhi, India climatic condition. MATLAB 7.0 software has been used to solve mathematical models of the proposed system. For validation of proposed system, the experimental study has also been carried out for all above four cases, and then comparative analysis of all different type of PV module has been presented. The hybrid PVT module air collectors presented in this study are self sustaining the system and can be used for the electricity generation in remote areas where access of electricity is not economical due to high transmission and distribution losses. It has been found that overall annual thermal energy and exergy gain of semitransparent PV module is higher by 11.6% and7.32% in summer condition and 16.39% and 18% in winter condition respectively as compared to opaque PV module considering same area (0.61 m2) of PV module.

Keywords: semitransparent PV module, overall exergy, overall thermal energy, opaque

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Authors: Zeinab Papi

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This study was aimed at analyzing the copyright laws of Iran, Australia, the U.S., and library portals, thereby providing a proposed framework for the copyright of library resources for the NLAI and other Iranian libraries while considering the current situation and the internal Iranian laws. This is an applied study falling in the category of qualitative approach research. Documentary analysis method and comparative method were used to resolve the problem and answer the questions of the research. The two National Library of Australia (NLA) and Library of Congress (LC), together with the NLAI formed the research community. In addition, the Iranian Law for the Protection of Authors, Composers and Artists Rights (1970); the Australian Copyright Act (1968), and the U.S. Copyright Law (1976) were purposefully selected as three main resources among other documents and resources. Findings revealed that the dimensions of fair and non-profit use, duration of copyright, license, and agreement, copyright policy, moral rights, economic rights, and infringement of copyright were the main dimensions that, along with 49 main components, formed the proposed framework for the copyright of information resources for the NLAI and other Iranian libraries. It should be acknowledged that there are some differences in different copyright fields between countries' laws, and each country takes into account its internal conditions to compile and revise the laws. By following the laws of other countries, it is possible to effectively improve and develop copyright laws. The researcher hopes that this research can have its effects in creating awareness and ability among librarians, formulating a copyright policy in Iranian libraries, and helping legislators in revising copyright laws regarding library exceptions and exemptions.

Keywords: copyright, library resources, National Library and Archives of the I.R. of Iran, National Library of Australia, Library of Congress, copyright law

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Authors: Yasaman Ashrafi, Stephen Kajewski, Annastiina Silvennoinen, Madhav Nepal

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Mega projects either in construction, urban development or energy sectors are one of the key drivers that build the foundation of wealth and modern civilizations in regions and nations. Such projects require economic justification and substantial capital investment, often derived from individual and corporate investors as well as governments. Cost overruns and time delays in these mega projects demands a new approach to more accurately predict project costs and establish realistic financial plans. The significance of this paper is that the cost efficiency of megaprojects will improve and decrease cost overruns. This research will assist Project Managers (PMs) to make timely and appropriate decisions about both cost and outcomes of ongoing projects. This research, therefore, examines the oil and gas industry where most mega projects apply the classic methods of Cost Performance Index (CPI) and Schedule Performance Index (SPI) and rely on project data to forecast cost and time. Because these projects are always overrun in cost and time even at the early phase of the project, the probabilistic methods of Monte Carlo Simulation (MCS) and Bayesian Adaptive Forecasting method were used to predict project cost at completion of projects. The current theoretical and mathematical models which forecast the total expected cost and project completion date, during the execution phase of an ongoing project will be evaluated. Earned Value Management (EVM) method is unable to predict cost at completion of a project accurately due to the lack of enough detailed project information especially in the early phase of the project. During the project execution phase, the Bayesian adaptive forecasting method incorporates predictions into the actual performance data from earned value management and revises pre-project cost estimates, making full use of the available information. The outcome of this research is to improve the accuracy of both cost prediction and final duration. This research will provide a warning method to identify when current project performance deviates from planned performance and crates an unacceptable gap between preliminary planning and actual performance. This warning method will support project managers to take corrective actions on time.

Keywords: cost forecasting, earned value management, project control, project management, risk analysis, simulation

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Authors: Mohammad A. Alia

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With the rapid evolution of the internet applications, cloud computing becomes one of today’s hottest research areas due to its ability to reduce costs associated with computing. Cloud is, therefore, increasing flexibility and scalability for computing services in the internet. Cloud computing is Internet based computing due to shared resources and information which are dynamically delivered to consumers. As cloud computing share resources via the open network, hence cloud outsourcing is vulnerable to attack. Therefore, this paper will explore data security of cloud computing by implementing chaotic cryptography. The proposal scenario develops a problem transformation technique that enables customers to secretly transform their information. This work proposes the chaotic cryptographic algorithms have been applied to enhance the security of the cloud computing accessibility. However, the proposed scenario is secure, easy and straightforward process. The chaotic encryption and digital signature systems ensure the security of the proposed scenario. Though, the choice of the key size becomes crucial to prevent a brute force attack.

Keywords: chaos, cloud computing, security, cryptography

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Authors: Jianhong Xiang, Hao Xiang, Linyu Wang

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Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB.

Keywords: smoothed L0, compressed sensing, image processing, sparse reconstruction

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Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Ibtissem Talbi

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During the last decade, a variety of chaos-based cryptosystems have been investigated. Most of them are based on the structure of Fridrich, which is based on the traditional confusion-diffusion architecture proposed by Shannon. Compared with traditional cryptosystems (DES, 3DES, AES, etc.), the chaos-based cryptosystems are more flexible, more modular and easier to be implemented, which make them suitable for large scale-data encyption, such as images and videos. The heart of any chaos-based cryptosystem is the chaotic generator and so, a part of the efficiency (robustness, speed) of the system depends greatly on it. In this talk, we give an overview of the state of the art of chaos-based block ciphers and we describe some of our schemes already proposed. Also we will focus on the essential characteristics of the digital chaotic generator, The needed performance of a chaos-based block cipher in terms of security level and speed of calculus depends on the considered application. There is a compromise between the security and the speed of the calculation. The security of these block block ciphers will be analyzed.

Keywords: chaos-based cryptosystems, chaotic generator, security analysis, structure of Fridrich

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Authors: Naji Ali Albakay, Abdulrahman Alothaim, Isa Barshushi

Abstract:

The most common quadrature amplitude modulator (QAM) applies two Mach-Zehnder Modulators (MZM) and one phase shifter to generate high order modulation format. The bias of MZM changes over time due to temperature, vibration, and aging factors. The change in the biasing causes distortion to the generated QAM signal which leads to deterioration of bit error rate (BER) performance. Therefore, it is critical to be able to lock MZM’s Q point to the required operating point for good performance. We propose a technique for automatic bias control (ABC) of QAM transmitter using BER measurements and gradient descent optimization algorithm. The proposed technique is attractive because it uses the pertinent metric, BER, which compensates for bias drifting independently from other system variations such as laser source output power. The proposed scheme performance and its operating principles are simulated using OptiSystem simulation software for 4-QAM and 16-QAM transmitters.

Keywords: automatic bias control, optical fiber communication, optical modulation, optical devices

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Authors: Antoniette Belle R. Bontuyan

Abstract:

The aim of the study was to determine the levels of performance in Composition Writing of English 122: Writing in the Discipline of the Associate in Hospitality Management Freshman Students in relation to their reading and writing experiences at the Cebu Technological University Tuburan Campus, Academic Year 2009-2010 as basis for a proposed skill exercises. Specifically, this research answers the following questions: Firstly, based on the students’ written compositions, what the students’ levels of performance in the following are: Composition Topic with subcomponents of Topic Development, Organizational or Logical Conclusions, Accurate, Relevant Evidence or Detail, Voice/Tone/Style, and the Composition Conventions with subcomponents of Structure, Grammar and Usage, Spelling, Capitalization, Punctuation. Secondly, what the students’ extents of experiences in view of Writing and Reading Experiences are.

Keywords: COMPOSITION WRITING

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Authors: Chung-Chun Hsiao, Yu-Kai, Ting, Kai-Yuan Liu, Pang-Wei Yen, Jia-Ying Tu

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In this paper, a new design of spherical robotic system based on the concepts of gimbal structure and gyro dynamics is presented. Robots equipped with multiple wheels and complex steering mechanics may increase the weight and degrade the energy transmission efficiency. In addition, the wheeled and legged robots are relatively vulnerable to lateral impact and lack of lateral mobility. Therefore, the proposed robotic design uses a spherical shell as the main body for ground locomotion, instead of using wheel devices. Three spherical shells are structured in a similar way to a gimbal device and rotate like a gyro system. The design and mechanism of the proposed robotic system is introduced. In addition, preliminary results of the dynamic model based on the principles of planar rigid body kinematics and Lagrangian equation are included. Simulation results and rig construction are presented to verify the concepts.

Keywords: gyro, gimbal, lagrange equation, spherical robots

Procedia PDF Downloads 316

Authors: Adriana Diaz Triana, Wolfgang Wimmer, Sebastian Glaser, Rainer Pamminger

Abstract:

SUSTAINEXT is a pioneer initiative in Extremadura, Spain under the EU Biobased industries. SUSTANEXT will scale-up and validate an industrial facility to produce botanical extracts, based on three key pillars. First, the whole valorization of bio-based feedstocks with a zero-waste and zero-emissions ambition. SUSTAINEXT will be deployed with six feedstocks. Three medicinal and aromatic plants (Rosemary, Chamomile, and Lemon verbena) will be locally sourced from disused tobacco fields with installed agri-voltaics; and three underexploited agro-industrial side streams will be further valorized (Olive, artichoke-cardoon, and pomegranate). Second, a dynamic, analytical biorefinery (DYANA) will isolate polyphenol and tri-terpenes from feedstocks in a disruptive and circular way. SUSTAINEXT explores 12 valorization routes (VRs) to extract and purify 46 functional ingredients, of which 13 are new in the market and 12 are newly produced in Europe. Third, the integrated and versatile value chain engages all actors, from feedstocks suppliers to extract users in the industries of food, animal feed, nutraceuticals, cosmetics, chemical performance, soil enhancers and fertilizers. This paper addresses SUTAINEXT activities towards zero impacts and full regulatory compliance. A comprehensive Life Cycle Thinking approach is proposed, with four complementary assessments running iteratively along the project duration (4,5 years). These are the Life Cycle Cost (LCCA), Life Cycle (LCA), Social Life Cycle (S-LCA) and Circularity (CA) assessments. The LCA will help evaluate the feedstock suitability parameters and intrinsic characteristics that quantify the feedstock´s grade for a determined use, and the feedstock´s suitability index for a specific VR. The LCA will also study the emissions, land use change, energy generation and consumption, and other environmental aspects and impacts of the VRs, to identify the most resource efficient and less impactful distribution of products from the circular biorefinery model used in SUSTAINEXT. Challenges to complete the LCA include the definition of the system boundaries, carrying out a robust inventory, and the proper allocation of impacts to the different VRs.

Keywords: biorefinery, botanical extracts, life cycle assessment, valorization routes.

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Authors: Seyed Mohsen Jameii

Abstract:

Wireless underwater sensor networks are a type of sensor networks that are located in underwater environments and linked together by acoustic waves. The application of these kinds of network includes monitoring of pollutants (chemical, biological, and nuclear), oil fields detection, prediction of the likelihood of a tsunami in coastal areas, the use of wireless sensor nodes to monitor the passing submarines, and determination of appropriate locations for anchoring ships. This paper proposes a boundary coverage algorithm for intrusion detection in underwater sensor networks. In the first phase of the proposed algorithm, optimal deployment of nodes is done in the water. In the second phase, after the employment of nodes at the proper depth, clustering is executed to reduce the exchanges of messages between the sensors. In the third phase, the algorithm of "divide and conquer" is used to save energy and increase network efficiency. The simulation results demonstrate the efficiency of the proposed algorithm.

Keywords: boundary coverage, clustering, divide and ‎conquer, underwater sensor nodes

Procedia PDF Downloads 341

Authors: Yang Xinyu, Gong Cong, Hu Changjuan

Abstract:

Stairway spaces are a crucial component of the pathway systems and vertical transportation networks in mountain city parks. These spaces are closely integrated with the undulating terrain of mountain environments, resulting in continuously changing spatial conditions that can significantly influence participants' behavioral characteristics, thereby affecting their perception. EEG signals, which have been proven to reflect various non-attentive physiological activities in the brain, are widely used in studies related to stress recovery effects and emotional perception. Existing research predominantly examines the impact of spatial characteristics and landscape elements of trails and greenways in plain cities on participants' perception, utilizing EEG signals in laboratory-simulated environments. These studies have preliminarily revealed the relationship between spatial environments and perception preferences. However, on-site ergonomics research in mountain environments remains relatively underdeveloped. To address this gap, the Stairway spaces in Pipashan Park, Chongqing, were selected as the research object. Wearable hydrogel EEG devices were employed to monitor participants' EEG data in real environments, and a Generalized Linear Mixed Model (GLMM) was constructed to explore differences in participants' perception under different paths and modes of movement, as well as the impact of visual and auditory environmental elements within each path on their perception. The model analysis results indicate significant differences in EEG data across different paths and movement modes. Additionally, typical mountainous spatial characteristics, such as openness, green view index, and elevation difference, are identified as key factors influencing participants' EEG data. Higher levels of natural sound and green view index were shown to effectively alleviate participants' stress perception in mountain stairway spaces. The findings reveal the intrinsic connections between environment, behavior, and perception in stairway spaces of mountain city parks, providing a theoretical basis for optimizing the design of stairway spaces in mountain cities.

Keywords: audio-visual perception, EEG monitoring, mountain city park, real environment, stairway space

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Authors: Salam Saudagar, Ankit Kamboj, Niraj Mohan, Satgounda Patil, Nilesh Powar

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Task Assignment and Scheduling is a challenging Operations Research problem when there is a limited number of resources and comparatively higher number of tasks. The Cost Management team at Cummins needs to assign tasks based on a deadline and must prioritize some of the tasks as per business requirements. Moreover, there is a constraint on the resources that assignment of tasks should be done based on an individual skill level, that may vary for different tasks. Another constraint is for scheduling the tasks that should be evenly distributed in terms of number of working hours, which adds further complexity to this problem. The proposed greedy approach to solve assignment and scheduling problem first assigns the task based on management priority and then by the closest deadline. This is followed by an iterative selection of an available resource with the least allocated total working hours for a task, i.e. finding the local optimal choice for each task with the goal of determining the global optimum. The greedy approach task allocation is compared with a variant of Hungarian Algorithm, and it is observed that the proposed approach gives an equal allocation of working hours among the resources. The comparative study of the proposed approach is also done with manual task allocation and it is noted that the visibility of the task timeline has increased from 2 months to 6 months. An interactive dashboard app is created for the greedy assignment and scheduling approach and the tasks with more than 2 months horizon that were waiting in a queue without a delivery date initially are now analyzed effectively by the business with expected timelines for completion.

Keywords: assignment, deadline, greedy approach, Hungarian algorithm, operations research, scheduling

Procedia PDF Downloads 147