Search results for: regional vector

Authors: Shirin Jalili, Ramin Ghasemi

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Lumbar microdiscectomy is the most commonly performed spinal surgery strategy; it is regularly performed to lighten the indications and signs of sciatica within the lower back and leg caused by a lumbar disc herniation. This surgery aims to progress leg pain, reestablish function, and enable a return to ordinary day-by-day exercises. Rates of lumbar disc surgery show critical geographic varieties recommending changing treatment criteria among working specialists. Few population-based considers have investigated the hazard of reoperation after disc surgery, and regional or inter specialty varieties within the reoperations are obscure. The conventional approach to recouping from lumbar microdiscectomy has been to restrain bending, lifting, or turning for a least 6 weeks in arrange to anticipate the disc from herniating once more. Traditionally, patients were exhorted to limit post-operative action, which was accepted to decrease the hazard of disc herniation and progressive insecurity. In modern hone, numerous specialists don't limit understanding of postoperative action due to the discernment this practice is pointless. There's a need of thinks about highlighting the result by distinctive scores or parameters after surgery for repetitive circle herniations of the lumbar spine at the starting herniation location. This study will evaluate the quality of life after surgical treatment of recurrent herniations with distinctive standardized approved result instruments.

Keywords: post-operative activity, disc, quality of life, treatment, movements

Procedia PDF Downloads 64

Authors: Tekle Olbamo, Tegegn Tesfaye, Dikaso Unbushe, Belete Jorga

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Bovine trypanosomosis is a haemoprotozoan parasitic disease, mostly transmitted by the tsetse fly (Glossina species) and poses significant losses to the livestock industry in pastoral and agro-pastoral areas. Therefore, the current study was aimed to determine the prevalence of bovine trypanosomosis and its vectorial density in some selected tsetse suppression and non-tsetse suppression areas of South Omo Zonefrom December 2018- November 2019. Dark phase contrast buffy coat, hematocrit techniques, and thin blood smear method were used for determination of prevalence and packed cell volume of trypanosomosis infection, respectively. For entomological investigation, 96 NGU traps were deployed (64 traps in tsetse suppression areas, 32 traps in tsetse non-suppression areas) in vector breeding areas. The overall prevalence of bovine trypanosomosis was 11.05% (142/1284), and overall seasonal prevalence of disease was 14.33% (92/642) and 7.78% (50/642) for dry and wet seasons, respectively. There was a statistically significant difference (P <0.05) in disease prevalence between the two seasons. Trypanosomacongolensewas the dominant parasite species; 80% and 71.64%, followed by Trypanosomavivax. Overall mean packed cell volume indicated parasitaemic animals (23.57±3.13) had significantly lower PCV than aparasitaemic animals (27.80±4.95), and animals examined during dry season (26.22±4.37) had lower mean PCV than animals examined during wet season with the significant association. Entomological study result revealed a total of 2.64 F/T/D and 2.03 F/T/D respectively from tsetse suppression areas and tsetse non-suppression areas during dry season and 0.42 F/T/D and 0.56 F/T/D during the wet season. Glossinapallidipes was the only cyclical vectors collected and identified from current study areas along with numerous mechanical vectors of genus Tabanus, Stomoxys, and Haematopota. Therefore integrated and safe control and prevention effort should be engaged to uphold cattle production and productivity in the area.

Keywords: bovine trypanosomiasis, South Omo, tsetse fly density, epidemiological study

Procedia PDF Downloads 144

Authors: Kai Zhang

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Transformation under the reconfiguration of urban-rural relation in Liuzhou city has never been as radical and visible as it has been since the tremendous turn of the last century in China. Huanjiang village is located in Linhuashan Scenic Area in the middle east of Liuzhou city, with spectacular landscape and traditional features. Nowadays Huanjiang village has become a so-called "village in city", which is considered full of great potential for development because of the economic value of regional advantages during the urban sprawl. Communities of village found it difficult to acclimatize with the dramatic changes, which later led to numerous problems including ecological damage, unemployment of landless farmers and loss of traditional culture. Government has started up a series of renewal planings to resolve the problems, which are based on advanced technology and conform to sustainable and integrated strategies of city planning considering the original context and historical culture, superseding the traditional arrangements based on the guide of extensive economic growth. This paper aims to elaborate the context of Liuzhou city and Huanjiang village offered to both the traditional and sustainable planning approaches, in order to understand challenges and solutions of the rebuilding process. Through the analysis of the place relevant to architecture, society and culture, it will establish the corresponding systematic strategies. Considering the local features, it concludes with a comprehensive perspective on organic renewal in the case of Huanjiang village.

Keywords: China, Liuzhou, sustainable strategy, urban renewal, village in city

Procedia PDF Downloads 264

Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

Procedia PDF Downloads 73

Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 124

Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: Mitchell T. Grothaus, Cory Grigsby, Timothy S. Hare

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This study aims to determine if there is a relationship between elevated cancer risks in eastern Kentucky and the environmental effects of black shale. Previous research shows that black shale formations, such as those in eastern Kentucky contain high levels of toxic elements including arsenic and radon compared to average rocks and sediment. Similarly, the population of eastern Kentucky has higher rates of many health conditions, including lung cancer and cardiovascular disease, than surrounding regions. These poor health outcomes are typically explained in relation to social, economic, behavioral, and healthcare factors. The rates of many conditions, however, have not decreased as these factors improve with regional development. Black shale is known to affect environmental conditions such as by increasing radiation levels and heavy metal toxicity. We are mapping the effects of black shale through monitoring radiation, microbes, and chemical standards of water sources. In this presentation, we report on our measuring pH, dissolved oxygen, total dissolved solids, conductivity, temperature, and discharge and comparison with water quality standards from the Kentucky Department for Environmental Protection. The conditions of water sources combined with an environmental survey of the surrounding areas provide a greater understanding of why the people in eastern Kentucky face the current health issues.

Keywords: black shale, eastern Kentucky, environmental impact, water quality

Procedia PDF Downloads 147

Authors: Mohammed A. Alhoot, Rathika A/P Nagarajan

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Climate change and variability are affecting human health and diseases direct or indirectly through many mechanisms. Change in rain pattern, an increase of temperature and humidity are showing an increased trend in Malaysia. This will affect the biological, physical and chemical component of water through different pathways and will enhance the risk of waterborne diseases. Besides, the warm temperature and humid climate provide very suitable conditions for the growth of pathogenic bacteria. This study is intended to highlight the relationship between the climate changes and the incidence food and water associated infections. Incidences of food and water associated infection and climate data were collected from Malaysian Ministry of health and Malaysian Metrological Department respectively. Maximum and minimum temperature showed high correlation with incidence of typhoid, hepatitis A, dysentery, food poisoning (P value <0.05 significant with 2 tailed / 0.5<[r]). Heavy rainfall does not associated with any outbreaks. Climate change brings out new challenges in controlling food and water associated infections. Adaptation strategies should involve all key stakeholders with a strong regional cooperation to prevent and deal with cross-boundary health crises. Moreover, the role of health care personnel at local, state and national levels is important to ensure the success of these programmes. As has been shown herein, climate variability is an important element influencing the food and water associated epidemiology in Malaysia. The results of this study are crucial to implementing climate changes as a factor to reduce any future outbreaks.

Keywords: climate change, typhoid, hepatitis A, dysentery, food poisoning

Procedia PDF Downloads 286

Authors: Zaide Montes Ortiz, Jorge Alberto Molina, Alejandro Reyes

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Insects are extremely successful organisms. A sophisticated olfactory system is in part responsible for their survival and reproduction. The detection of volatile organic compounds can positively or negatively affect many behaviors in insects. Compounds such as carbon dioxide (CO2), ammonium, indol, and lactic acid are essential for many species of mosquitoes like Anopheles gambiae in order to locate vertebrate hosts. For instance, in A. gambiae, the olfactory receptor AgOR2 is strongly activated by indol, which accounts for almost 30% of human sweat. On the other hand, in some insects of agricultural importance, the detection and identification of pheromone receptors (PRs) in lepidopteran species has become a promising field for integrated pest management. For example, with the disruption of the pheromone receptor, BmOR1, mediated by transcription activator-like effector nucleases (TALENs), the sensitivity to bombykol was completely removed affecting the pheromone-source searching behavior in male moths. Then, the detection and identification of olfactory receptors in the genomes of insects is fundamental to improve our understanding of the ecological interactions, and to provide alternatives in the integrated pests and vectors management. Hence, the objective of this study is to propose a bioinformatic workflow to enhance the detection and identification of potential olfactory receptors in genomes of relevant insects. Applying Hidden Markov models (Hmms) and different computational tools, potential candidates for pheromone receptors in Tuta absoluta were obtained, as well as potential carbon dioxide receptors in Rhodnius prolixus, the main vector of Chagas disease. This study showed the validity of a bioinformatic workflow with a potential to improve the identification of certain olfactory receptors in different orders of insects.

Keywords: bioinformatic workflow, insects, olfactory receptors, protein prediction

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Authors: Stephane Laussac, Jacques Piazzola, Gilles Tedeschi

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Estimation of the impact of atmospheric aerosols on the climate evolution is an important scientific challenge. One of a major source of particles is constituted by the oceans through the generation of sea-spray aerosols. In coastal areas, marine aerosols can affect air quality through their ability to interact chemically and physically with other aerosol species and gases. The integration of accurate sea-spray emission terms in modeling studies is then required. However, it was found that sea-spray concentrations are not represented with the necessary accuracy in some situations, more particularly at short fetch. In this study, the WRF-Chem model was implemented on a North-Western Mediterranean coastal region. WRF-Chem is the Weather Research and Forecasting (WRF) model online-coupled with chemistry for investigation of regional-scale air quality which simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. One of the objectives was to test the ability of the WRF-Chem model to represent the fine details of the coastal geography to provide accurate predictions of sea spray evolution for different fetches and the anthropogenic aerosols. To assess the performance of the model, a comparison between the model predictions using a local emission inventory and the physicochemical analysis of aerosol concentrations measured for different wind direction on the island of Porquerolles located 10 km south of the French Riviera is proposed.

Keywords: sea-spray aerosols, coastal areas, sea-spray concentrations, short fetch, WRF-Chem model

Procedia PDF Downloads 173

Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Shahed, Tala, Ahlam, Marah, Sara, Shaden

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Research on fear of crime has shown that there are many influences on it, including gender, age, and geographic location. For example, women are more afraid of crime than men. The campus has a high crime rate and fear of crime due to many hiding places and blind spots; women are more likely than men to be victims of certain types of crime, such as rape and verbal and sexual harassment. And it became clear that older female students have a different perception of the place over time and other knowledge and fear of it, another study at Hashemite University. This study aims to understand better how the environment affects the negative experiences of female students and how their age and familiarity environment affects their sense of safety. This study also examines whether CPTED can be used to help prevent crime. The Broken Windows Theory also states that crime occurs in areas with overt indications of criminal activity, antisocial behavior, and civil unrest. This is related to the principle of CPTED maintenance and monitoring, activity support, regional development, and access control. Given their increased vulnerability to harassment, “sexual harassment” can refer to different behaviors. On campuses, harassment was happening everywhere, but it was most prevalent in "blind spots" that were out of sight and deserted. This study uses a methodology based on quantitative data that depends on putting a number on the amount of a particular phenomenon that exists in the world. The main finding shows how CPTED works in an academic context and what adjustments need to be made.

Keywords: Hashmite University, CPTED, crime prevention, university campus, fear of crime, female faer, broken window theory

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Authors: Leizhang Wang, Baocang Wang

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General Sieve Kernel (G6K) is considered as currently the fastest algorithm for the shortest vector problem (SVP) and record holder of open SVP challenge. We study the lattice basis quality improvement effects of the Workout proposed in G6K, which is composed of a series of pumps to solve SVP. Firstly, we use a low-dimensional pump output basis to propose a predictor to predict the quality of high-dimensional Pumps output basis. Both theoretical analysis and experimental tests are performed to illustrate that it is more computationally expensive to solve the LWE problems by using a G6K default SVP solving strategy (Workout) than these lattice reduction algorithms (e.g. BKZ 2.0, Progressive BKZ, Pump, and Jump BKZ) with sieving as their SVP oracle. Secondly, the default Workout in G6K is optimized to achieve a stronger reduction and lower computational cost. Thirdly, we combine the optimized Workout and the Pump output basis quality predictor to further reduce the computational cost by optimizing LWE instances selection strategy. In fact, we can solve the TU LWE challenge (n = 65, q = 4225, = 0:005) 13.6 times faster than the G6K default Workout. Fourthly, we consider a combined two-stage (Preprocessing by BKZ- and a big Pump) LWE solving strategy. Both stages use dimension for free technology to give new theoretical security estimations of several LWE-based cryptographic schemes. The security estimations show that the securities of these schemes with the conservative Newhope’s core-SVP model are somewhat overestimated. In addition, in the case of LAC scheme, LWE instances selection strategy can be optimized to further improve the LWE-solving efficiency even by 15% and 57%. Finally, some experiments are implemented to examine the effects of our strategies on the Normal Form LWE problems, and the results demonstrate that the combined strategy is four times faster than that of Newhope.

Keywords: LWE, G6K, pump estimator, LWE instances selection strategy, dimension for free

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Authors: Murad Ghanim

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The citrus greening disease, also known as Huanglongbing, caused by the phloem-limited bacterium Candidatus Liberibacter asiaticus (CLas) has resulted in tremendous losses and the death of millions of citrus trees worldwide. CLas is transmitted by the Asian citrus psyllid (ACP) Diaphorina citri. The closely-related bacterium Candidatus Liberibacter solanacearum (CLso), which is associated with vegetative disorders in carrots and the zebra chips disease in potatoes, is transmitted by other psyllid species including Bactericera trigonica in carrots and B. ckockerelli in potatoes. Chemical sprays are currently the prevailing method for managing these diseases for limiting psyllid populations; however, they are limited in their effectiveness. A promising approach to prevent the transmission of these pathogens is to interfere with the vector-pathogen interactions, but our understanding of these processes is very limited. CLas induces changes in the nuclear architecture in the midgut of ACP and activates programmed cell death (apoptosis) in this organ. Strikingly, CLso displayed an opposite effect in the gut of B. trigonica, showing limited apoptosis, but widespread necrosis. Electron and fluorescent microscopy further showed that CLas induced the formation of Endoplasmic reticulum (ER) inclusion- and replication-like bodies, in which it increases and multiplies. ER involvement in bacterial replication is hypothesized to be the first stage of an immune response leading to the apoptotic and necrotic responses. ER exploitation and the subsequent events that lead to these cellular and stress responses might activate a cascade of molecular responses ending up with apoptosis and necrosis. Understanding the molecular interactions that underlay the necrotic/apoptotic responses to the bacteria will increase our knowledge of ACP-CLas, and BT-CLso interactions, and will set the foundation for developing novel, and efficient strategies to disturb these interactions and inhibit the transmission.

Keywords: Liberibacter, psyllid, transmission, apoptosis, necrosis

Procedia PDF Downloads 132

Authors: Pattra Pukdeekiat, Siriluk Ruangrungrote

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This work was originated to precisely determine direct solar radiation by using atmospheric physics models since the accurate prediction of solar radiation is necessary and useful for solar energy applications including atmospheric research. The possible models and techniques for a calculation of regional direct solar radiation were challenging and compulsory for the case of unavailable instrumental measurement. The investigation was mathematically governed by six astronomical parameters i.e. declination (δ), hour angle (ω), solar time, solar zenith angle (θz), extraterrestrial radiation (Iso) and eccentricity (E0) along with two atmospheric parameters i.e. air mass (mr) and dew point temperature at Bangna meteorological station (13.67° N, 100.61° E) in Bangkok, Thailand. Analyses of five models of solar radiation determination with the assumption of clear sky were applied accompanied by three statistical tests: Mean Bias Difference (MBD), Root Mean Square Difference (RMSD) and Coefficient of determination (R2) in order to validate the accuracy of obtainable results. The calculated direct solar radiation was in a range of 491-505 Watt/m2 with relative percentage error 8.41% for winter and 532-540 Watt/m2 with relative percentage error 4.89% for summer 2014. Additionally, dataset of seven continuous days, representing both seasons were considered with the MBD, RMSD and R2 of -0.08, 0.25, 0.86 and -0.14, 0.35, 3.29, respectively, which belong to Kumar model for winter and CSR model for summer. In summary, the determination of direct solar radiation based on atmospheric models and empirical equations could advantageously provide immediate and reliable values of the solar components for any site in the region without a constraint of actual measurement.

Keywords: atmospheric physics models, astronomical parameters, atmospheric parameters, clear sky condition

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Authors: Kahlil Fredrick Cui, Marissa Pastor

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Earthquakes are complex phenomena, exhibiting complex correlations in space, time, and magnitude. Recently, the concept of complex networks has been used to shed light on the statistical and dynamical characteristics of regional seismicity. In this work, we study the relationships and interactions of seismic regions in Chile, Japan, and the Philippines through weighted and directed complex network analysis. Geographical areas are digitized into cells of fixed dimensions which in turn become the nodes of the network when an earthquake has occurred therein. Nodes are linked if a correlation exists between them as determined and measured by a correlation metric. The networks are found to be scale-free, exhibiting power-law behavior in the distributions of their different centrality measures: the in- and out-degree and the in- and out-strength. The evidence is also found of preferential interaction between seismically active regions through their degree-degree correlations suggesting that seismicity is dictated by the activity of a few active regions. The importance of a seismic region to the overall seismicity is measured using a generalized centrality metric taken to be an indicator of its activity or passivity. The spatial distribution of earthquake activity indicates the areas where strong earthquakes have occurred in the past while the passivity distribution points toward the likely locations an earthquake would occur whenever another one happens elsewhere. Finally, we propose a method that would project the location of the next possible earthquake using the generalized centralities coupled with correlations calculated between the latest earthquakes and a geographical point in the future.

Keywords: complex networks, correlations, earthquake, hazard assessment

Procedia PDF Downloads 190

Authors: Muhammad Sohail Sajid, Mahvish Maqbool, Hafiz Muhammad Rizwan, Muhammad Saqib, Haroon Ahmad

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Ticks are blood-sucking ectoparasite that causes a decrease in production and economic losses and affects mammals, reptiles, and birds. Hyalomma anatolicum is the main vector for CCHF transmission and Pakistan has faced several outbreaks of CCHF in the recent past. Ferritin (fer)is a highly conserved molecule that is ubiquitous in most tick tissues and responsible for iron metabolism and storage. It was hypothesized that the development of acaricidal resistance and residual effects of commercially used acaricides could be controlled by using alternative control methods, including RNA interference. The current study aimed to evaluate the fer silencing effects on tick feeding, average body weight, egg mass index, and mortality. Ticks, collected through the standard collection protocols were further subjected to RNA isolation using the Trizol method. Commercially available kit procedures were followed for cDNA and dsRNA synthesis. The soaking/Immersion method was used for dsRNA delivery. Our findings have shown a 27% reduction in body weight of fer silenced group and showed a significant association of fer and body weight. Silencing of fer had a significant effect on the engorgement percentage (P= 0.0007), oviposition (P=0.008), egg mass (P= 0.004) and hatching (P= 0.001). The soaking method was used for dsRNA delivery and 15°C was found to be an optimum temperature for inducing gene silencing in ticks as at this temperature, maximum survivability after immersion was attained. This study along with previous studies, described that iron toxicity due to the silencing of fer could play an important role in the control of ticks and fer can be used as a potent candidate for vaccine development.

Keywords: ticks, iron, ferritin, engorgement, oviposition, immersion, RNA interference

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Authors: Hirbaye Mokona, Abebaw Gebeyehu, Aemro Zerihun

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Introduction: Attention deficit hyperactivity disorder is serious public health problem affecting millions of children throughout the world. Method: A cross-sectional study conducted from May to June 2015 among children age 6 to 17 years living in rural area of Girja district. Multi-stage cluster sampling technique was used to select 1302 study participants. Disruptive Behavior Disorder rating scale was used to collect the data. Data were coded, entered and cleaned by Epi-Data version 3.1 and analyzed by SPSS version 20. Logistic regression analysis was used and Variables that have P-values less than 0.05 on multivariable logistic regression was considered as statistically significant. Results: Prevalence of Attention deficit hyperactivity disorder (ADHD) among children age 6 to 17 years was 7.3%. Being male [AOR=1.81, 95%CI: (1.13, 2.91)]; living with single parent [AOR=5.0, 95%CI: (2.35, 10.65)]; child birth order/rank [AOR=2.35, 95%CI: (1.30, 4.25)]; low family socio-economic status [AOR= 2.43, 95%CI: (1.29, 4.59)]; maternal alcohol/khat use during pregnancy [AOR=3.14, 95%CI: (1.37, 7.37)] and complication at delivery [AOR=3.56, 95%CI: (1.19, 10.64)] were more likely to develop Attention deficit hyperactivity disorder. Conclusion: In this study, the prevalence of Attention deficit hyperactivity disorder was similar with worldwide prevalence. Prevention and early management of its modifiable risk factors should be carryout alongside increasing community awareness.

Keywords: attention deficit hyperactivity disorder, ADHD, associated factors, children, prevalence

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

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The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Angesom Gebrezgabiher Tesfay, Afafaw Hailesilasie Tesfay, Muyiwa Samuel Adaramola

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The energy demand boost in Ethiopia urges sustainable fuel options while it is mainly supplemented by traditional biomass and imported conventional fuels. To satisfy the deficiency it has to be sourced from all renewables. Thus identifying resources and estimating potential is vital to the sector. This study aims at an in-depth assessment to quantify, prioritize, and analyze agricultural residue biomass energy and related characteristic forms. Biomass use management and modernization seeks successive information and a clue about the resource quantity and characteristic. Five years of crop yield data for thirteen crops were collected. Conversion factors for their 20 residues are surveyed from the literature. Then residues amount potentially available for energy and their energy is estimated regional, crop-wise, residue-wise, and shares compared. Their potential value for energy is analyzed from two perspectives and prioritized. The gross potential is estimated to be 495PJ, equivalent to 12/17 million tons of oil/coal. At 30% collection efficiency, it is the same as conventional fuel import in 2018. Maize and sorghum potential and spatial availability are preeminent. Cotton and maize presented the highest potential values for energy from application and resource perspectives. Oromia and Amhara regions' contributions are the highest. The resource collection and application trends are required for future management that implicates a prospective study.

Keywords: crop residue, biomass potential, biomass resource, Ethiopian energy

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: T. Garba, Y. Y. Babanyara, K. G. Ilellah, M. A. Modibbo, T. O. Quddus, M. J. Sani

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After the United Nation Convention on Desertification (UNCD) in 1977 which was preceded by extensive, regional, and local studies, and consultations with numerous scientists, decision-makers, and relevant institutions. Global Plan of Action to Combat Desertification (PACD) was formulated, endorsed by member Countries. The role of implementing PACD was vested with Governments of countries affected by desertification. The Federal Government of Nigeria as a signatory and World Bank funded and implement afforestation project aimed at combating desertification between 1988 and 1999. This research, therefore, applied remote sensing techniques to assess the effectiveness of the project. To achieve that a small portion of about 143,609 hectares was curved out from the project area. Normalized Difference of the Vegetative Index (NDVI) and Land Use Land Cover were derived from Landsat TM 1986, Landsat ETM 1999 and Nigeria Sat 1, 2007 of the project area. The findings show that there was an increase in cultivated area due to the project from 1986 through 1999 and 2007. This is further buttressed by the three NDVI imageries due to their high positive pixel value from 0.04 in 1986 to 0.22 in 1999 and to 0.32 in 2007 These signifies the gradual physical development of Afforestation project in the area. In addition, it was also verified by histograms of changes in vegetation which indicated an increased vegetative cover from 60,192 in 1986, to 102,476 in 1999 and then to 88,343 in 2007. The study concluded that Remote Sensing approach has actually confirmed that the project was indeed successful and effective.

Keywords: afforestation, desertification, landsat, vegetative index, remote sensing

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Authors: Riccardo Pasquali, Keith Harlin, Mark Muller

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The ShallowTherm project is focussed on developing and applying a methodology for extrapolating relatively sparsely sampled thermal conductivity measurements across Ireland using mapped Litho-Electrical (LE) units. The primary data used consist of electrical resistivities derived from the Geological Survey Ireland Tellus airborne electromagnetic dataset, GIS-based maps of Irish geology, and rock thermal conductivities derived from both the current Irish Ground Thermal Properties (IGTP) database and a new programme of sampling and laboratory measurement. The workflow has been developed across three case-study areas that sample a range of different calcareous, arenaceous, argillaceous, and volcanic lithologies. Statistical analysis of resistivity data from individual geological formations has been assessed and integrated with detailed lithological descriptions to define distinct LE units. Thermal conductivity measurements from core and hand samples have been acquired for every geological formation within each study area. The variability and consistency of thermal conductivity measurements within each LE unit is examined with the aim of defining a characteristic thermal conductivity (or range of thermal conductivities) for each LE unit. Mapping of LE units, coupled with characteristic thermal conductivities, provides a method of defining thermal conductivity properties at a regional scale and facilitating the design of ground source heat pump closed-loop collectors.

Keywords: thermal conductivity, ground source heat pumps, resistivity, heat exchange, shallow geothermal, Ireland

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Authors: Jake Tempo, Jack Crozier, Huay Ann Chia, Philip Tan

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Purpose: A prospective study was performed to determine whether temporary ureteric catheterization should be eliminated as a prophylactic method for preventing ureteric obstruction after uncomplicated ureteropyeloscopic lithotripsy. Material and Methods: From 2010 to 2014, 227 patients underwent uncomplicated ureteroscopic and/or pyeloscopic lithotripsy. Three patient-groups based on postoperative drainage method were analysed: temporary uretericcatheter (TUC), -ureteric JJ stent, and no-stent groups. Exclusion criteria included urosepsis, ureteric injury, and non-surgical complications delaying hospital-discharge. Outcome measures included parenteral analgesic requirements, prolonged hospitalization ≥2 days due to postoperative-pain, and readmissions rate. Results: Delayed discharge was reported in 14.5%(9 of 62) patients in the TUC group compared to 3.4%(4 of 119) in theureteric JJ stent group and 8.7%(4 of 46) in the no-drainage-group (p=0.02). Odds ratio for delayed-discharge between catheter- versus-ureteric JJ stent is 4.9 (95% CI = 1.6-15.0; p < 0.01). Parenteral analgesic requirements in the TUC group (12.9%) was also significantly higher than theureteric JJ stent group (1.7%; p=0.003). Readmissions were negligible between groups. Conclusions: Patients with ureteric catheters after uncomplicated ureteroscopy have a prolonged hospital stay with increased pain and parenteral analgesic requirements. There is a 7.6-fold increased requirement for parenteral analgesia and a 4.2-fold increased risk of delayed-discharge compared to a patient with a ureteric JJ stent.

Keywords: ureteric catheter, ureteric stent, ureteroscopy, pyeloscopy

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Authors: Vera Marcantonio, Marcello De Falco, Mauro Capocelli, Álvaro Amado-Fierro, Teresa A. Centeno, Enrico Bocci

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Concerns about energy security, energy prices, and climate change led scientific research towards sustainable solutions to fossil fuel as renewable energy sources coupled with hydrogen as an energy vector and carbon capture and conversion technologies. Among the technologies investigated in the last decades, biomass gasification acquired great interest owing to the possibility of obtaining low-cost and CO₂ negative emission hydrogen production from a large variety of everywhere available organic wastes. Upstream and downstream treatment were then studied in order to maximize hydrogen yield, reduce the content of organic and inorganic contaminants under the admissible levels for the technologies which are coupled with, capture, and convert carbon dioxide. However, studies which analyse a whole process made of all those technologies are still missing. In order to fill this lack, the present paper investigated the coexistence of hydrothermal carbonization (HTC), sorption enhance gasification (SEG), hot gas cleaning (HGC), and CO₂ conversion by dielectric barrier discharge (DBD) plasma reactor for H₂ production from biomass waste by means of Aspen Plus software. The proposed model aimed to identify and optimise the performance of the plant by varying operating parameters (such as temperature, CaO/biomass ratio, separation efficiency, etc.). The carbon footprint of the global plant is 2.3 kg CO₂/kg H₂, lower than the latest limit value imposed by the European Commission to consider hydrogen as “clean”, that was set to 3 kg CO₂/kg H₂. The hydrogen yield referred to the whole plant is 250 gH₂/kgBIOMASS.

Keywords: biomass gasification, hydrogen, aspen plus, sorption enhance gasification

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Authors: An-Na Li, Ying-Yu Chen, Chang-Kuang Chiou

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Food tourism is one of the growing industries and areas of interest in the tourism industry today. The Destination Marketing Organizations (DMOs) are aware of the importance of gastronomy in order to stimulate local and regional economic development. From the heritage and cultural aspects, gastronomy is becoming a more important part of the cultural heritage of region and countries. Heritage destinations provide culinary heritage, which fits the current interest in traditional food, and cuisine is part of a general desire for authentic experiences. However, few studies have empirically examining food tourist’s behavior. This study examined the effects of cuisine experience, emotions and tourists’ revisit intentions. A total of 402 individuals responded to the on-site survey in the historic town of Lu-Kang in Taiwan. The results indicated that tourists’ cuisine experience include place flavor, media recommended local learning, life transfer and interpersonal share. In addition, cuisine experience had significant impacts on emotions, which in turn cuisine experience and emotions had significant effects on tourists’ revisit intentions. The findings suggested that the cuisine experience is a multi- dimensions construct. On the other hands, the good quality of cuisine experience could evoke tourists’ positive emotions and it plays a significant role in promote tourist revisit intentions and word of mouth. Implications for theory and practice are discussed.

Keywords: culinary tourism, cuisine experience, emotions, revisit intentions

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Authors: Yanling Li, Linying Ji, Zita Oravecz, Timothy R. Brick, Michael D. Hunter, Sy-Miin Chow

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Assessing several individuals intensively over time yields intensive longitudinal data (ILD). Even though ILD provide rich information, they also bring other data analytic challenges. One of these is the increased occurrence of missingness with increased study length, possibly under non-ignorable missingness scenarios. Multiple imputation (MI) handles missing data by creating several imputed data sets, and pooling the estimation results across imputed data sets to yield final estimates for inferential purposes. In this article, we introduce dynr.mi(), a function in the R package, Dynamic Modeling in R (dynr). The package dynr provides a suite of fast and accessible functions for estimating and visualizing the results from fitting linear and nonlinear dynamic systems models in discrete as well as continuous time. By integrating the estimation functions in dynr and the MI procedures available from the R package, Multivariate Imputation by Chained Equations (MICE), the dynr.mi() routine is designed to handle possibly non-ignorable missingness in the dependent variables and/or covariates in a user-specified dynamic systems model via MI, with convergence diagnostic check. We utilized dynr.mi() to examine, in the context of a vector autoregressive model, the relationships among individuals’ ambulatory physiological measures, and self-report affect valence and arousal. The results from MI were compared to those from listwise deletion of entries with missingness in the covariates. When we determined the number of iterations based on the convergence diagnostics available from dynr.mi(), differences in the statistical significance of the covariate parameters were observed between the listwise deletion and MI approaches. These results underscore the importance of considering diagnostic information in the implementation of MI procedures.

Keywords: dynamic modeling, missing data, mobility, multiple imputation

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Authors: Yun Wang, Ruoyu Wang, Tuo Chen, Guangxiu Liu, Guodong Chen, Wei Zhang

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Tuberculosis (TB) is a major public health problem in China. China has the world's second largest tuberculosis epidemic (after India). Xinjiang almost has the highest annual attendance rate of TB in China, and the province is also famous because of its severe dust storms. The epidemic timing starts in February and ends in July, and the dust storm mainly distribute throughout the spring and early summer, which strongly indicate a close linkage between causative agent of TB and dust storm events. However, mechanisms responsible for the observed patterns are still not clearly indentified. By comparing the information on cases of TB from Centers for Disease Control of China annual reports with dust storm atmosphere datasets, we constructed the relationship between the large scale annual occurrence of TB in Xinjiang, a Northwest province of China, and dust storm occurrence. Regional atmospheric indexes of dust storm based on surface wind speed show a clear link between population dynamics of the disease and the climate disaster: the onset of epidemics and the dust storm defined by the atmospheric index share the same mean year. This study is the first that provides a clear demonstration of connections that exist between TB epidemics and dust storm events in China. The development of this study will undoubtedly help early warning for tuberculosis epidemic onset in China and help nationwide and international public health institutions and policy makers to better control TB disease in Norwest China.

Keywords: dust storm, tuberculosis, Xinjiang province, epidemic

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Authors: Lilla Anjani, Ervina Ahyudanari

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Tourism is one of the contributors to regional economic income. Banyuwangi has made rapid developments related to the tourism sector, especially since 2010. There are 25 tourist visit locations that can become tourist destinations. Banyuwangi has tourism transportation to support the ease of reaching tourist places. This transportation operates with six routes, namely the final destination of Ijen Crater, Glenmore, Bajangan, Bangsring, Red Island, and Pine Forest. Despite having tourism transportation, tourists tend to choose to use a private car or rent a car because there is no access to tourist places using public transportation. Tourism transportation is also one form of sustainable tourism development in the future, such as the Sustainable Development Goals. The Banyuwangi government has a special program for tourism development that is supported by all sectors in Banyuwangi. To support the development of tourism in Banyuwangi, it is necessary to analyze existing tourism transportation as well as suggestions regarding new routes to reach all tourism locations in Banyuwangi Regency. The analysis reviewed in this study is an analysis of accessibility, distance, and time to the tourism location. There are 30 tourism destination points from 39 ODTW references from the transportation service, and the tourism office of Banyuwangi Regency Banyuwangi tourism objects can be divided into six zones based on travel time and distance. The highest accessibility value for Zone A is 51.96, and the lowest is 11.989. The highest accessibility value for Zone B is 33.4269, and the lowest is 21.737. The highest accessibility value for Zone C is 33,407, and the lowest is 14,848. The highest accessibility value for Zone D is 58,967, and the lowest is 14,742. The highest accessibility value for Zone E is 56,401, and the lowest is 14.1. The highest accessibility value for Zone F is 176.14, and the lowest is 44.1. There are two tourist transportation routes with six sessions every day. The resulting new route is in the form of grouping based on locations that can be reached in one particular area.

Keywords: accessibility, tourism clustering, Banyuwangi tourism, sustainable development goals

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