Search results for: collision probability

Authors: Kunal Tongaria, Abhishek Mangal, S. Mandal, Devendra Mohan

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The carbonation of concrete is a phenomenon which is a function of various interdependent parameters. Therefore, in spite of numerous literature and database, the useful generalization is not an easy task. These interdependent parameters can be grouped under the category of internal and external factors. This paper focuses on the internal parameters which govern and increase the probability of the ingress of deleterious substances into concrete. The mechanism of effects of internal parameters such as microstructure for with and without supplementary cementing materials (SCM), water/binder ratio, the age of concrete etc. has been discussed. This is followed by the comparison of various proposed mathematical models for the deterioration of concrete. Based on existing laboratory experiments as well as field results, this paper concludes the present understanding of mechanism, modeling and future research needs in this field.

Keywords: carbonation, diffusion coefficient, microstructure of concrete, reinforced concrete

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Authors: Armana Huq

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Railway networks have a significant role in the economy of any country. Similar to other transportation modes, many lives suffer from fatalities or injuries caused by accidents related to the railway. Railway accidents are not as common as roadway accidents yet they are more devastating and damaging than other roadway accidents. Despite that, issues related to railway accidents are not taken into consideration with significant attention as a major threat because of their less frequency compared to other accident categories perhaps. However, the Federal Railroad Administration reported nearly twelve thousand train accidents related to the railroad in the year 2014, resulting in more than eight hundred fatalities and thousands of injuries in the United States alone of which nearly one third fatalities resulted from railway crossing accidents. From an analysis of railway accident data of six years (2005-2010), it has been revealed that 344 numbers of the collision were occurred resulting 200 people dead and 443 people injured in Bangladesh. This paper includes a comprehensive overview of the railway safety situation in Bangladesh from 1998 to 2015. Each year on average, eight fatalities are reported in at-grade level crossings due to railway accidents in Bangladesh. In this paper, the number of railway accidents that occurred in Bangladesh has been presented and a fatality rate of 58.62% has been estimated as the percentage of total at-grade railway level crossing accidents. For this study, analysis of railway accidents in Bangladesh for the period 1998 to 2015 was obtained from the police reported accident database using MAAP (Microcomputer Accident Analysis Package). Investigation of the major contributing factors to the railway accidents has been performed using the Multinomial Logit model. Furthermore, hotspot analysis has been conducted using ArcGIS. Eventually, some suggestions have been provided to mitigate those accidents.

Keywords: safety, human factors, multinomial logit model, railway

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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Authors: A. R. Manu, V. K. Agrawal, K. N. Balasubramanya Murthy

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Authentication is required in stored database systems so that only authorized users can access the data and related cloud infrastructures. This paper proposes an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. The proposed technique is likely to be more robust as the probability of breaking the password is extremely low. This framework uses a multi-modal biometric approach and SMS to enforce additional security measures with the conventional Login/password system. The robustness of the technique is demonstrated mathematically using a statistical analysis. This work presents the authentication system along with the user authentication architecture diagram, activity diagrams, data flow diagrams, sequence diagrams, and algorithms.

Keywords: design, implementation algorithms, performance, biometric approach

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Authors: Huimin Liu, Li Xiang, Yue Liu, Jing Wang

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Background: As one of typical case for the developing countries who are stepping into the aging times globally, more and more older people in China might face the problem of which they could not maintain normal life due to the functional disability. While the government take efforts to build long-term care system and further carry out related policies for the core concept, there is still lack of strong evidence to evaluating the profile of disability states in the elderly population and its transition rate. It has been proved that disability is a dynamic condition of the person rather than irreversible so it means possible to intervene timely on them who might be in a risk of severe disability. Objective: The aim of this study was to depict the picture of the disability transferring status of the older people in China, and then find out individual characteristics that change the state of disability to provide theory basis for disability prevention and early intervention among elderly people. Methods: Data for this study came from the 2011 baseline survey and the 2013 follow-up survey of the China Health and Retirement Longitudinal Study (CHARLS). Normal ADL function, 1~2 ADLs disability,3 or above ADLs disability and death were defined from state 1 to state 4. Multi-state Markov model was applied and the four-state homogeneous model with discrete states and discrete times from two visits follow-up data was constructed to explore factors for various progressive stages. We modeled the effect of explanatory variables on the rates of transition by using a proportional intensities model with covariate, such as gender. Result: In the total sample, state 2 constituent ratio is nearly about 17.0%, while state 3 proportion is blow the former, accounting for 8.5%. Moreover, ADL disability statistics difference is not obvious between two years. About half of the state 2 in 2011 improved to become normal in 2013 even though they get elder. However, state 3 transferred into the proportion of death increased obviously, closed to the proportion back to state 2 or normal functions. From the estimated intensities, we see the older people are eleven times as likely to develop at 1~2 ADLs disability than dying. After disability onset (state 2), progression to state 3 is 30% more likely than recovery. Once in state 3, a mean of 0.76 years is spent before death or recovery. In this model, a typical person in state 2 has a probability of 0.5 of disability-free one year from now while the moderate disabled or above has a probability of 0.14 being dead. Conclusion: On the long-term care cost considerations, preventive programs for delay the disability progression of the elderly could be adopted based on the current disabled state and main factors of each stage. And in general terms, those focusing elderly individuals who are moderate or above disabled should go first.

Keywords: Markov model, elderly people, disability, transition intensity

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Authors: Suk-Hyun Yu, Heeyong Kwon

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Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.

Keywords: artificial intelligence, air quality prediction, neural networks, pattern recognition, PM-10

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Authors: Lila A. Alkhattaby, Norah A. Alsayegh, Mohammad S. Ansari, Mohammad O. Ansari

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In this work, we doped zinc oxide ZnO with potassium K we have synthesized using the sol-gel method. Structural properties were depicted by X-ray diffractometer (XRD) and energy distribution spectroscopy, X-ray diffraction studies confirm the nanosized of the particles and favored orientations along the (100), (002), (101), (102), (110), (103), (200), and (112) planes confirm the hexagonal wurtzite structure of ZnO NPs. The optical properties study using the UV-Vis spectroscopy. The band gap decreases from 4.05 eV to 3.88 eV, the lowest band gap at 10% doped concentration. The photoluminescence (PL) spectroscopy results show two main peaks, a sharp peak at ≈ 384 nm in the UV region and a broad peak around 479 nm in the visible region. The highest intensity of the band-edge luminescence was for 2% doped concentration because of the combined effect of the decreased probability of nonradiative recombination and has better crystallinity.

Keywords: K doped ZnO, photoluminescence spectroscopy, UV-Vis spectroscopy, x-ray spectroscopy

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Authors: Zhiyong Zheng, Xu Li, Liang Huang, Zhengliang Sun, Jianhua Xu

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Yaw angle plays a significant role in many vehicle safety applications, such as collision avoidance and lane-keeping system. Although the estimation of the yaw angle has been extensively studied in existing literature, it is still the main challenge to simultaneously achieve a reliable and cost-effective solution in complex urban environments. This paper proposes a multi-stage learning framework to estimate the yaw angle with a monocular camera, which can deal with the challenge in a more reliable manner. In the first stage, an efficient road detection network is designed to extract the road region, providing a highly reliable reference for the estimation. In the second stage, a variational auto-encoder (VAE) is proposed to learn the distribution patterns of road regions, which is particularly suitable for modeling the changing patterns of yaw angle under different driving maneuvers, and it can inherently enhance the generalization ability. In the last stage, a gated recurrent unit (GRU) network is used to capture the temporal correlations of the learned patterns, which is capable to further improve the estimation accuracy due to the fact that the changes of deflection angle are relatively easier to recognize among continuous frames. Afterward, the yaw angle can be obtained by combining the estimated deflection angle and the road direction stored in a roadway map. Through effective multi-stage learning, the proposed framework presents high reliability while it maintains better accuracy. Road-test experiments with different driving maneuvers were performed in complex urban environments, and the results validate the effectiveness of the proposed framework.

Keywords: gated recurrent unit, multi-stage learning, reliable estimation, variational auto-encoder, yaw angle

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: segmentation, color-texture, neural networks, fractal, watershed

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Authors: J. S. Saini, P. P. K. Sandhu

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The multi-hop nature of Wireless Mesh Networks and the hasty progression of throughput demands results in multi- channels and multi-radios structures in mesh networks, but the main problem of co-channels interference reduces the total throughput, specifically in multi-hop networks. Quality of Service mentions a vast collection of networking technologies and techniques that guarantee the ability of a network to make available desired services with predictable results. Quality of Service (QoS) can be directed at a network interface, towards a specific server or router's performance, or in specific applications. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may interfere with each other. This paper has considered the Destination Sequenced Distance Vector (DSDV) routing protocol to locate the secure and optimised path. The proposed technique also utilizes the Lempel–Ziv–Welch (LZW) based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregate the multiple packets and locates a single route using the clusters to improve the intra cluster data aggregation. The use of the LZW based lossless data compression has ability to reduce the data packet size and hence it will consume less energy, thus increasing the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the projected technique. The comparative analysis has shown that the proposed technique outperforms over the existing techniques.

Keywords: WMNS, QOS, flooding, collision avoidance, LZW, congestion control

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Authors: Dogaru-Ulieru Valentin, Sălișteanu Ioan Corneliu, Ardeleanu Mihăiță Nicolae, Broscăreanu Ștefan, Sălișteanu Bogdan, Mihai Mihail

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The SiaMOTO system is a communications and data processing platform for vehicle traffic. The human factor is the most important factor in the generation of this data, as the driver is the one who dictates the trajectory of the vehicle. Like any trajectory, specific parameters refer to position, speed and acceleration. Constant knowledge of these parameters allows complex analyses. Roadways allow many vehicles to travel through their confined space, and the overlapping trajectories of several vehicles increase the likelihood of collision events, known as road accidents. Any such event has causes that lead to its occurrence, so the conditions for its occurrence are known. The human factor is predominant in deciding the trajectory parameters of the vehicle on the road, so monitoring it by knowing the events reported by the DiaMOTO device over time, will generate a guide to target any potentially high-risk driving behavior and reward those who control the driving phenomenon well. In this paper, we have focused on detailing the communication infrastructure of the DiaMOTO device with the traffic data collection server, the infrastructure through which the database that will be used for complex AI/DLM analysis is built. The central element of this description is the data string in CODEC-8 format sent by the DiaMOTO device to the SiaMOTO collection server database. The data presented are specific to a functional infrastructure implemented in an experimental model stage, by installing on a number of 50 vehicles DiaMOTO unique code devices, integrating ADAS and GPS functions, through which vehicle trajectories can be monitored 24 hours a day.

Keywords: DiaMOTO, Codec-8, ADAS, GPS, driver monitoring

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Authors: Preeda Sansakorn, Min An

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In order to be capable of dealing with uncertainties, subjectivities, including vagueness arising in building construction projects, the application of fuzzy reasoning technique based on fuzzy set theory is proposed. This study contributes significantly to the development of a fuzzy reasoning safety risk assessment model for building construction projects that could be employed to assess the risk magnitude of each hazardous event identified during construction, and a third parameter of probability of consequence is incorporated in the model. By using the proposed safety risk analysis methodology, more reliable and less ambiguities, which provide the safety risk management project team for decision-making purposes.

Keywords: safety risk assessment, building construction safety, fuzzy reasoning, construction risk assessment model, building construction projects

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Authors: Li Zhou, Yanmei Fang

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With current technology, images and videos can be obtained more easily than ever. It is so easy to manipulate these digital multimedia information when obtained, and that the content or source of the image and video could be easily tampered. In this paper, we propose to identify the image and video frame by the texture-based approach, e.g. Markov Transition Probability (MTP), which is in space domain, DCT domain and DWT domain, respectively. In the experiment, image and video frame database is constructed, and is used to train and test the classifier Support Vector Machine (SVM). Experiment results show that the texture-based approach has good performance. In order to verify the experiment result, and testify the universality and robustness of algorithm, we build a random testing dataset, the random testing result is in keeping with above experiment.

Keywords: multimedia forensics, video frame, LBP, MTP, SVM

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Su-Ping Liu, Yue Tian, Yifan Shen

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This study investigates the effect of corporate governance on earnings management when firms have reported a long stream of earnings increases (hereafter referred to as earnings beaters). We expect that good quality of corporate governance decreases the probability of income-increasing earnings management. We employ transparent tools to capture firms’ opportunistic management behavior, specifically, the repurchase of stock. In addition, we use corporate governance proxies to measure the degree of corporate governance, including board size, board independence, CEO duality, and the frequency of meeting. The results hold after the controlling of variables that suggested in prior literature. We expect that the simple technique, that is, firms’ degree of corporate governance, to be used as an inexpensive first step in detecting earnings management.

Keywords: corporate governance, earnings management, earnings patterns, stock repurchase

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Authors: Nulu Nanono

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Background: The Ugandan national health policy and the national population policy all recognize the need to increase access to quality, affordable, acceptable and sustainable contraceptive services for all people but provision and utilization of quality services remains low. Two contraceptive methods are categorized as long-acting temporary methods: intrauterine contraceptive devices (IUCDs) and implants. Copper-containing IUCDs, generally available in Ministry of Health (MoH) family planning programs and is effective for at least 12 years while Implants, depending on the type, last for up to three to seven years. Uganda’s current policy and political environment are favorable towards achieving national access to quality and safe contraceptives for all people as evidenced by increasing government commitments and innovative family planning programs. Despite the increase of modern contraception use from 14% to 26%, long acting reversible contraceptive (LARC) utilization has relatively remained low with less than 5% using IUDs & Implants which in a way explains Uganda’s persistent high fertility rates. Main question/hypothesis: The purpose of the study was to examine relationship between the demographic, socio-economic characteristics of women, health facility factors and long acting reversible contraception utilization. Methodology: LARC utilization was investigated comprising of the two questions namely are you or your partner currently doing something or using any method to delay or avoid getting pregnant? And which method or methods are you using? Data for the study was sourced from the 2016 Uganda Performance Monitoring and Accountability 2020 Survey comprising of 3816 female respondents aged 15 to 49 years. The analysis was done using the Chi-squared tests and the probit regression at bivariate and multivariate levels respectively. The model was further tested for validity and normality of the residuals using the Sharipo wilks test and test for kurtosis and skewness. Results: The results showed the model the age, parity, marital status, region, knowledge of LARCs, availability of LARCs to be significantly associated with long acting contraceptive utilization with p value of less than 0.05. At the multivariate analysis level, women who had higher parities (0.000) tertiary education (0.013), no knowledge about LARCs (0.006) increases their probability of using LARCs. Furthermore while women age 45-49, those who live in the eastern region reduces their probability of using LARCs. Knowledge contribution: The findings of this study join the debate of prior research in this field and add to the body of knowledge related to long acting reversible contraception. An outstanding and queer finding from the study is the non-utilization of LARCs by women who are aware and have knowledge about them, this may be an opportunity for further research to investigate the attribution to this.

Keywords: contraception, long acting, utilization, women (15-49)

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Authors: Minghui Zhang, Sirine Fkaier, Sabri Fernana, Svetlana Kiseleva, Denis Kiselev

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The real-time identification of bacteria and fungi is difficult because they emit much weaker signals than pollen. In 2020, Plair developed Rapid-E+, which extends abilities of Rapid-E to detect smaller bioaerosols such as bacteria and fungal spores with diameters down to 0.3 µm, while keeping the similar or even better capability for measurements of large bioaerosols like pollen. Rapid-E+ enables simultaneous measurements of (1) time-resolved, polarization and angle dependent Mie scattering patterns, (2) fluorescence spectra resolved in 16 channels, and (3) fluorescence lifetime of individual particles. Moreover, (4) it provides 2D Mie scattering images which give the full information on particle morphology. The parameters of every single bioaerosol aspired into the instrument are subsequently analysed by machine learning. Firstly, pure species of microbes, e.g., Bacillus subtilis (a species of bacteria), and Penicillium chrysogenum (a species of fungal spores), were aerosolized in a bioaerosol chamber for Rapid-E+ training. Afterwards, we tested microbes under different concentrations. We used several steps of data analysis to classify and identify microbes. All single particles were analysed by the parameters of light scattering and fluorescence in the following steps. (1) They were treated with a smart filter block to get rid of non-microbes. (2) By classification algorithm, we verified the filtered particles were microbes based on the calibration data. (3) The probability threshold (defined by the user) step provides the probability of being microbes ranging from 0 to 100%. We demonstrate how Rapid-E+ identified simultaneously microbes based on the results of Bacillus subtilis (bacteria) and Penicillium chrysogenum (fungal spores). By using machine learning, Rapid-E+ achieved identification precision of 99% against the background. The further classification suggests the precision of 87% and 89% for Bacillus subtilis and Penicillium chrysogenum, respectively. The developed algorithm was subsequently used to evaluate the performance of microbe classification and quantification in real-time. The bacteria and fungi were aerosolized again in the chamber with different concentrations. Rapid-E+ can classify different types of microbes and then quantify them in real-time. Rapid-E+ enables classifying different types of microbes and quantifying them in real-time. Rapid-E+ can identify pollen down to species with similar or even better performance than the previous version (Rapid-E). Therefore, Rapid-E+ is an all-in-one instrument which classifies and quantifies not only pollen, but also bacteria and fungi. Based on the machine learning platform, the user can further develop proprietary algorithms for specific microbes (e.g., virus aerosols) and other aerosols (e.g., combustion-related particles that contain polycyclic aromatic hydrocarbons).

Keywords: bioaerosols, laser-induced fluorescence, Mie-scattering, microorganisms

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Authors: Alexander K. Guo

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PM2.5 poses a large threat to people’s health and the environment and is an issue of large concern in Beijing, brought to the attention of the government by the media. In addition, both the United States Embassy in Beijing and the government of China have increased monitoring of PM2.5 in recent years, and have made real-time data available to the public. This report utilizes hourly historical data (2008-2016) from the U.S. Embassy in Beijing for the first time. The first objective was to attempt to fit probability distributions to the data to better predict a number of days exceeding the standard, and the second was to uncover any yearly, seasonal, monthly, daily, and hourly patterns and trends that may arise to better understand of air control policy. In these data, 66,650 hours and 2687 days provided valid data. Lognormal, gamma, and Weibull distributions were fit to the data through an estimation of parameters. The Chi-squared test was employed to compare the actual data with the fitted distributions. The data were used to uncover trends, patterns, and improvements in PM2.5 concentration over the period of time with valid data in addition to specific periods of time that received large amounts of media attention, analyzed to gain a better understanding of causes of air pollution. The data show a clear indication that Beijing’s air quality is unhealthy, with an average of 94.07µg/m3 across all 66,650 hours with valid data. It was found that no distribution fit the entire dataset of all 2687 days well, but each of the three above distribution types was optimal in at least one of the yearly data sets, with the lognormal distribution found to fit recent years better. An improvement in air quality beginning in 2014 was discovered, with the first five months of 2016 reporting an average PM2.5 concentration that is 23.8% lower than the average of the same period in all years, perhaps the result of various new pollution-control policies. It was also found that the winter and fall months contained more days in both good and extremely polluted categories, leading to a higher average but a comparable median in these months. Additionally, the evening hours, especially in the winter, reported much higher PM2.5 concentrations than the afternoon hours, possibly due to the prohibition of trucks in the city in the daytime and the increased use of coal for heating in the colder months when residents are home in the evening. Lastly, through analysis of special intervals that attracted media attention for either unnaturally good or bad air quality, the government’s temporary pollution control measures, such as more intensive road-space rationing and factory closures, are shown to be effective. In summary, air quality in Beijing is improving steadily and do follow standard probability distributions to an extent, but still needs improvement. Analysis will be updated when new data become available.

Keywords: Beijing, distribution, patterns, pm2.5, trends

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Authors: HoYoung Son, Ki Young Kim, Woo Young Jung

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This study investigated the change of weir structure performances when durability of concrete, which is the main material of weir structure, decreased due to their aging by mean of seismic fragility analysis. In the analysis, it was assumed that the elastic modulus of concrete was reduced by 10% in order to account for their aged deterioration. Additionally, the analysis of seismic fragility was based on Monte Carlo Simulation method combined with a 2D nonlinear finite element in ABAQUS platform with the consideration of deterioration of concrete. Finally, the comparison of seismic fragility of model pre- and post-deterioration was made to study the performance of weir. Results show that the probability of failure in moderate damage for deteriorated model was found to be larger than pre-deterioration model when peak ground acceleration (PGA) passed 0.4 g.

Keywords: weir, FEM, concrete, fragility, aging

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Authors: Kimaya Subhash Gaikwad, S. B. Waykar

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The mobile ad-hoc network (MANET) is collection of various types of nodes. In MANET various protocols are used for communication. In OLSR protocol, a node is selected as multipoint relay (MPR) node which broadcast the messages. As the MANET is open kind of network any malicious node can easily enter into the network and affect the performance of the network. The performance of network mainly depends on the components which are taking part into the communication. If the proper nodes are not selected for the communication then the probability of network being attacked is more. Therefore, it is important to select the more reliable and secure components in the network. MANET does not have any filtering so that only selected nodes can be used for communication. The openness of the MANET makes it easier to attack the communication. The most of the attack are on the Quality of service (QoS) of the network. This paper gives the overview of the various attacks that are possible on OLSR protocol and some solutions. The papers focus mainly on the OLSR protocol.

Keywords: communication, MANET, OLSR, QoS

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Authors: Mohsen Khaledian, Razali Ismail, Mehdi Saeidmanesh, Mahdiar Hosseinghadiry

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A semi-analytical model for impact ionization coefficient of graphene nanoribbon (GNR) is presented. The model is derived by calculating probability of electrons reaching ionization threshold energy Et and the distance traveled by electron gaining Et. In addition, ionization threshold energy is semi-analytically modeled for GNR. We justify our assumptions using analytic modeling and comparison with simulation results. Gaussian simulator together with analytical modeling is used in order to calculate ionization threshold energy and Kinetic Monte Carlo is employed to calculate ionization coefficient and verify the analytical results. Finally, the profile of ionization is presented using the proposed models and simulation and the results are compared with that of silicon.

Keywords: nanostructures, electronic transport, semiconductor modeling, systems engineering

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Authors: Bander Alzahrani, Mohammed Alreshoodi

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Information-centric networking (ICN) using architectures such as the Publish-Subscribe Internet Technology (PURSUIT) has been proposed as a new networking model that aims at replacing the current used end-centric networking model of the Internet. This emerged model focuses on what is being exchanged rather than which network entities are exchanging information, which gives the control plane functions such as routing and host location the ability to be specified according to the content items. The forwarding plane of the PURSUIT ICN architecture uses a simple and light mechanism based on Bloom filter technologies to forward the packets. Although this forwarding scheme solve many problems of the today’s Internet such as the growth of the routing table and the scalability issues, it is vulnerable to brute force attacks which are starting point to distributed- denial-of-service (DDoS) attacks. In this work, we design and analyze a novel source-routing and information delivery technique that keeps the simplicity of using Bloom filter-based forwarding while being able to deter different attacks such as denial of service attacks at the ingress of the network. To achieve this, special forwarding nodes called Edge-FW are directly attached to end user nodes and used to perform a security test for malicious injected random packets at the ingress of the path to prevent any possible attack brute force attacks at early stage. In this technique, a core entity of the PURSUIT ICN architecture called topology manager, that is responsible for finding shortest path and creating a forwarding identifiers (FId), uses a cryptographically secure hash function to create a 64-bit hash, h, over the formed FId for authentication purpose to be included in the packet. Our proposal restricts the attacker from injecting packets carrying random FIds with a high amount of filling factor ρ, by optimizing and reducing the maximum allowed filling factor ρm in the network. We optimize the FId to the minimum possible filling factor where ρ ≤ ρm, while it supports longer delivery trees, so the network scalability is not affected by the chosen ρm. With this scheme, the filling factor of any legitimate FId never exceeds the ρm while the filling factor of illegitimate FIds cannot exceed the chosen small value of ρm. Therefore, injecting a packet containing an FId with a large value of filling factor, to achieve higher attack probability, is not possible anymore. The preliminary analysis of this proposal indicates that with the designed scheme, the forwarding function can detect and prevent malicious activities such DDoS attacks at early stage and with very high probability.

Keywords: forwarding identifier, filling factor, information centric network, topology manager

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Authors: Gianluca Del Rossi

Abstract:

Sub-concussive impacts occur frequently in collision sports such as American tackle football. Sub-concussive level impacts are defined as hits to the head that do not result in the clinical manifestation of concussion injury. Presently, there is limited information known about the short-term effects of repeated sub-concussive blows to the head. Therefore, the purpose of this investigation was to determine if standard clinical measures could detect acute impairments in neurologic function resulting from the accumulation of sub-concussive impacts throughout a season of high school American tackle football. Simple reaction time using the ruler-drop test, and oculomotor performance using the King-Devick (KD) test, were assessed in 15 athletes prior to the start of the athletic season, then repeated each week of the season, and once following its completion. The mean reaction times and fastest KD scores that were recorded or calculated from each study participant and from each test session were analyzed to assess for change in reaction time and oculomotor performance over the course of the American tackle football season. Analyses of KD data revealed improvements in oculomotor performance from baseline measurements (i.e., decreased time), with most weekly comparisons to baseline being significantly different. Statistical tests performed on the mean reaction times obtained via the ruler-drop test throughout the season revealed statistically significant declines (i.e., increased time) between baseline and weeks 3, 4, 10, and 12 of the athletic season. The inconsistent and contrasting findings between KD data and reaction time demonstrate the need to identify more robust clinical measures to definitively assess if repeated sub-concussive impacts to the head are acutely detrimental to patients.

Keywords: head injury, mTBI and sport, subclinical head trauma, sub-concussive impacts

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Authors: Asowata Osamede, Trudy Sutherland

Abstract:

The purpose of this paper is to assess the available power from a Renewable Energy Source (off-grid photovoltaic (PV) panel) in the Southern Hemisphere using anisotropic model. Direct solar radiation is the driving force in photovoltaics. In a basic PV panels in the Southern Hemisphere, Power conversion is eminent, and this is achieved by the PV cells converting solar energy into electrical energy. In this research, the results was determined for a 6 month period from September 2022 through February 2023. Preliminary results, which include Normal Probability plot, data analysis - R2 value, effective conversion-time per week and work-time per day, indicate a favorably comparison between the empirical results and the simulation results.

Keywords: power-conversion, mathematical model, PV panels, DC-DC converters, direct solar radiation

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez, Mehdi Mrad

Abstract:

We investigate the large scale of networks in the context of network survivability under attack. We use appropriate techniques to evaluate and the attacker-based- and the defender-based-network survivability. The attacker is unaware of the operated links by the defender. Each attacked link has some pre-specified probability to be disconnected. The defender choice is so that to maximize the chance of successfully sending the flow to the destination node. The attacker however will select the cut-set with the highest chance to be disabled in order to partition the network. Moreover, we extend the problem to the case of selecting the best p paths to operate by the defender and the best k cut-sets to target by the attacker, for arbitrary integers p,k > 1. We investigate some variations of the problem and suggest polynomial-time solutions.

Keywords: defense/attack strategies, large scale, networks, partitioning a network

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Authors: Aouina Nabila Yasmina, Zine El Abidine Chaoui

Abstract:

Recently, understanding the interactions of electrons with the DNA molecule and its components has attracted considerable interest because DNA is the main site damaged by ionizing radiation. The interactions of radiation with DNA induce a variety of molecular damage such as single-strand breaks, double-strand breaks, basic damage, cross-links between proteins and DNA, and others, or the formation of free radicals, which, by chemical reactions with DNA, can also lead to breakage of the strand. One factor that can contribute significantly to these processes is the effect of water hydration on the formation and reaction of radiation induced by these radicals in and / or around DNA. B-DNA requires about 30% by weight of water to maintain its native conformation in the crystalline state. The transformation depends on various factors such as sequence, ion composition, concentration and water activity. Partial dehydration converts it to DNA-A. The present study shows the results of theoretical calculations for positrons and electrons elastic scattering with DNA medium and water over a broad energy range from 10 eV to 100 keV. Indeed, electron elastic cross sections and elastic mean free paths are calculated using a corrected form of the independent atom method, taking into account the geometry of the biomolecule (SCAR macro). Moreover, the elastic scattering of electrons and positrons by atoms of the biomolecule was evaluated by means of relativistic (Dirac) partial wave analysis. Our calculated results are compared with theoretical data available in the literature in the absence of experimental data, in particular for positron. As a central result, our electron elastic cross sections are in good agreement with existing theoretical data in the range of 10 eV to 1 keV.

Keywords: elastic cross scrion, elastic mean free path, scar macro method, electron collision

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Authors: Seyed Ali Mahdipour, Maryam Shafeei Sarvestani

Abstract:

Radiation pressure acceleration (RPA) and target normal sheath acceleration (TNSA) are the most important methods of Laser-accelerated proton beams (LAP) planning systems.LAP has inspired novel applications that can benefit from proton bunch properties different from conventionally accelerated proton beams. The secondary neutron and photon produced in the collision of protons with beamline components are of the important concern in proton therapy. Various published Monte Carlo researches evaluated the beamline and shielding considerations for TNSA method, but there is no studies directly address secondary neutron and photon production from RPA method in LAP. The purpose of this study is to calculate the flux distribution of neutron and photon secondary radiations on the first area ofLAP and to determine the optimize thickness and radius of the energyselector in a LAP planning system based on RPA method. Also, we present the Monte Carlo calculations to determine the appropriate beam pipe for shielding a LAP planning system. The GEANT4 Monte Carlo toolkit has been used to simulate a secondary radiation production in LAP. A section of new multifunctional LAP beamlinehas been proposed, based on the pulsed power solenoid scheme as a GEANT4 toolkit. The results show that the energy selector is the most important source of neutron and photon secondary particles in LAP beamline. According to the calculations, the pure Tungsten energy selector not be the proper case, and using of Tungsten+Polyethylene or Tungsten+Graphitecomposite selectors will reduce the production of neutron and photon intensities by approximately ~10% and ~25%, respectively. Also the optimal radiuses of energy selectors were found to be ~4 cm and ~6 cm for a 3 degree and 5 degree proton deviation angles, respectively.

Keywords: neutron, photon, flux distribution, energy selector, GEANT4 toolkit

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Authors: Arunee Wongkhao, Suparat Niwitpong, Sa-aat Niwitpong

Abstract:

In this paper, we propose two new confidence intervals for the difference between the inverse of normal means with known coefficients of variation. One of these two confidence intervals for this problem is constructed based on the generalized confidence interval and the other confidence interval is constructed based on the closed form method of variance estimation. We examine the performance of these confidence intervals in terms of coverage probabilities and expected lengths via Monte Carlo simulation.

Keywords: coverage probability, expected length, inverse of normal mean, coefficient of variation, generalized confidence interval, closed form method of variance estimation

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Authors: Eva L. Suarez, Daniel E. Meeroff, Yan Yong

Abstract:

The U.S. Federal Emergency Management Agency (FEMA) developed flood maps based on experts’ experience and estimates of the probability of flooding. Current flood-risk models evaluate flood risk with regional and subjective measures without impact from torrential rain and nuisance flooding at the neighborhood level. Nuisance flooding occurs in small areas in the community, where a few streets or blocks are routinely impacted. This type of flooding event occurs when torrential rainstorm combined with high tide and sea level rise temporarily exceeds a given threshold. In South Florida, this threshold is 1.7 ft above Mean Higher High Water (MHHW). The National Weather Service defines torrential rain as rain deposition at a rate greater than 0.3-inches per hour or three inches in a single day. Data from the Florida Climate Center, 1970 to 2020, shows 371 events with more than 3-inches of rain in a day in 612 months. The purpose of this research is to develop a data-driven method to determine comprehensive analytical damage-avoidance criteria that account for nuisance flood events at the single-family home level. The method developed uses the Failure Mode and Effect Analysis (FMEA) method from the American Society of Quality (ASQ) to estimate the Damage Avoidance (DA) preparation for a 1-day 100-year storm. The Consequence of Nuisance Flooding (CoNF) is estimated from community mitigation efforts to prevent nuisance flooding damage. The Probability of Nuisance Flooding (PoNF) is derived from the frequency and duration of torrential rainfall causing delays and community disruptions to daily transportation, human illnesses, and property damage. Urbanization and population changes are related to the U.S. Census Bureau's annual population estimates. Data collected by the United States Department of Agriculture (USDA) Natural Resources Conservation Service’s National Resources Inventory (NRI) and locally by the South Florida Water Management District (SFWMD) track the development and land use/land cover changes with time. The intent is to include temporal trends in population density growth and the impact on land development. Results from this investigation provide the risk of nuisance flooding as a function of CoNF and PoNF for coastal areas of South Florida. The data-based criterion provides awareness to local municipalities on their flood-risk assessment and gives insight into flood management actions and watershed development.

Keywords: flood risk, nuisance flooding, urban flooding, FMEA

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