Search results for: adaptive filter

Authors: Oyebade K. Oyedotun

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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Renae McNair

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Subjective well-being levels were assessed in individuals diagnosed with an autoimmune disease. The current exploratory analysis sought to examine two factors that impact subjective well-being in individuals diagnosed with a chronic health condition. The two factors, resilience, and rumination, were assessed as possible moderators in self-reported levels of subjective well-being were measured. The importance of understanding the psychological state of perceived well-being in an individual diagnosed with an autoimmune disease is important given the impact of the level of subjective well-being on life longevity. In previous research, higher levels of subjective well-being are correlated with longer life longevity, including those individuals who have been diagnosed with an autoimmune disease. Conversely, individuals who report higher levels of negative affect have a shorter length of life longevity. According to the Center for Disease Control (CDC) and a report from the National Health Council, currently, 8-10% of individuals in the United States have been diagnosed with at least one autoimmune disease. Although treatment plans are in place to help manage the physical effects of disease, the psychological state of the person impacts life longevity. Resilience and rumination impact subjective well-being as an outcome in individuals diagnosed with an autoimmune disease. Resilience is the ability to adjust or adapt effectively and positively to unfavorable life conditions or events. Resilience acts as a protective factor in life, allowing those who face adversity to successfully adapt, regardless of the health diagnosis. Rumination is the worry or dwelling on the negative aspects of a given situation. Rumination interrupts the adaptive response, leading to a decrease in well-being. The relationship between resilience and subjective well-being were examined correlated with higher levels of resilience and higher levels of self-reported subjective well-being.

Keywords: subjective well-being, rumination, resilience, autoimmune disease

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Authors: Lamees Nasser, Yago Diez, Robert Martí, Joan Martí, Ibrahim Sadek

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Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively.

Keywords: Automated 3D Breast Ultrasound, Eigenvalues of Hessian matrix, Nipple detection, Rib detection

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Authors: Richard L. Summers

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The advanced computational analysis of biological systems is becoming increasingly dependent upon an understanding of the information-theoretic structure of the materials, energy and interactive processes that comprise those systems. The stability and survival of these living systems are fundamentally contingent upon their ability to acquire and process the meaning of information concerning the physical state of its biological continuum (biocontinuum). The drive for adaptive system reconciliation of a divergence from steady-state within this biocontinuum can be described by an information metric-based formulation of the process for actionable knowledge acquisition that incorporates the axiomatic inference of Kullback-Leibler information minimization driven by survival replicator dynamics. If the mathematical expression of this process is the Lagrangian integrand for any change within the biocontinuum then it can also be considered as an action functional for the living system. In the direct method of Lyapunov, such a summarizing mathematical formulation of global system behavior based on the driving forces of energy currents and constraints within the system can serve as a platform for the analysis of stability. As the system evolves in time in response to biocontinuum perturbations, the summarizing function then conveys information about its overall stability. This stability information portends survival and therefore has absolute existential meaning for the living system. The first derivative of the Lyapunov energy information function will have a negative trajectory toward a system's steady state if the driving force is dissipating. By contrast, system instability leading to system dissolution will have a positive trajectory. The direction and magnitude of the vector for the trajectory then serves as a quantifiable signature of the meaning associated with the living system’s stability information, homeostasis and survival potential.

Keywords: meaning, information, Lyapunov, living systems

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Authors: Kai-Jui Kou, Tzu-Ling Shen, Ying-Fang Wang

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The objectives of the present study are to characterize nanoparticles generated from the different working type in 3D printing room and the stack flue during 3D printing process. The studied laboratory (10.5 m× 7.2 m × 3.2 m) with a ventilation rate of 500 m³/H is installed a 3D metal printing machine. Direct-reading instrument of a scanning mobility particle sizer (SMPS, Model 3082, TSI Inc., St. Paul, MN, USA) was used to conduct static sampling for nanoparticle number concentration and particle size distribution measurements. The SMPS obtained particle number concentration at every 3 minutes, the diameter of the SMPS ranged from 11~372 nm when the aerosol and sheath flow rates were set at 0.6 and 6 L/min, respectively. The concentrations of background, printing process, clearing operation, and screening operation were performed in the laboratory. On the other hand, we also conducted nanoparticle measurement on the 3D printing machine's stack flue to understand its emission characteristics. Results show that the nanoparticles emitted from the different operation process were the same distribution in the form of the uni-modal with number median diameter (NMD) as approximately 28.3 nm to 29.6 nm. The number concentrations of nanoparticles were 2.55×10³ count/cm³ in laboratory background, 2.19×10³ count/cm³ during printing process, 2.29×10³ count/cm³ during clearing process, 3.05×10³ count/cm³ during screening process, 2.69×10³ count/cm³ in laboratory background after printing process, and 6.75×10³ outside laboratory, respectively. We found that there are no emission nanoparticles during the printing process. However, the number concentration of stack flue nanoparticles in the ongoing print is 1.13×10⁶ count/cm³, and that of the non-printing is 1.63×10⁴ count/cm³, with a NMD of 458 nm and 29.4 nm, respectively. It can be confirmed that the measured particle size belongs to easily penetrate the filter in theory during the printing process, even though the 3D printer has a high-efficiency filtration device. Therefore, it is recommended that the stack flue of the 3D printer would be equipped with an appropriate dust collection device to prevent the operators from exposing these hazardous particles.

Keywords: nanoparticle, particle emission, 3D printing, number concentration

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Authors: G. Hosseini, A. Sadighzadeh, M. Rahimnejad, N. Hosseini, Z. Jamalzadeh

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Metal pollutants, especially heavy metals from anthropogenic sources such as metallurgical industries’ waste including mining, smelting, casting or production of nuclear fuel, including mining, concentrate production and uranium processing ends in the environment contamination (water and soil) and risk to human health around the facilities of this type of industrial activity. There are different methods that can be used to remove these contaminants from water and soil. These are very expensive and time-consuming. In this case, the people have been forced to leave the area and the decontamination is not done. For example, in the case of Chernobyl accident, an area of 30 km around the plant was emptied of human life. A very efficient and cost-effective method for decontamination of the soil and the water is phytoremediation. In this method, the plants preferentially native plants which are more adaptive to the regional climate are well used. In this study, three types of plants including Alfalfa, Sunflower and wheat were used to Barium decontamination. Alfalfa and Sunflower were not grown good enough in Saghand mine’s soil sample. This can be due to non-native origin of these plants. But, Wheat rise in Saghand Uranium Mine soil sample was satisfactory. In this study, we have investigated the effect of 4 types of acids inclusive nitric acid, oxalic acid, acetic acid and citric acid on the removal efficiency of Barium by Wheat. Our results indicate the increase of Barium absorption in the presence of citric acid in the soil. In this paper, we will present our research and laboratory results.

Keywords: phytoremediation, heavy metal, wheat, soil

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Authors: Budiman Ibnu

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This study investigates about enabling conditions to facilitate social innovation in the energy sector. This is important to support the energy transition in Indonesia. This research provides appropriate project direction, including research (and action) gaps for the energy actors in Indonesia. The actors are allowed to work further with the result of this study to stimulate the energy transition in Indonesia. This report uses systemic change framework which recognizes four drivers of systemic change in a region: 1. transforming political ecologies; 2. configuring green economies; 3. building of adaptive communities; 4. social innovation. These drivers are interconnected, and this report particularly focuses on how social innovation can be supported by other drivers. This study used methods of interview and literature review as the main sources for data collection in this report. There were interviews with eight experts in the related topic which come from different countries which have experienced social innovation in the energy sector. Afterwards, this research reviewed related journal papers from last five years, to check the latest development within the topic, to support the interview result. The result found that the enabling condition can focus on one of the drivers of systemic change, which is building communities by increasing their participation, through several integrated actions. This can be implemented in two types of citizen energy initiatives which are energy cooperatives and sustainable consumption initiatives. This implementation requires study about its related policy and governance support, in order to create complete enabling conditions to facilitate social innovation in the energy transition.

Keywords: enabling condition, social innovation, citizen initiatives, community participation

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Authors: Asma Alhuqail, Nagwa Aref

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Programmed cell death resembles a real nature active defense in Datura metel against TMV after three days of virus infection. Physiological plant response was assessed for asymptomatic healthy and symptomatic infected detached leaves. The results indicated H2O2 and Chlorophyll-a as the most potential parameters. Chlorophyll-a was considered the only significant predictor variant for the H2O2 dependent variant with a P value of 0.001 and R-square of 0.900. The plant immune response was measured within three days of virus infection using the cutoff value of H2O2 (61.095 lmol/100 mg) and (63.201 units) for the tail moment in the Comet Assay. Their percentage changes were 255.12% and 522.40% respectively which reflects the stress of virus infection in the plant. Moreover, H2O2 showed 100% specificity and sensitivity in the symptomatic infected group using the receiver-operating characteristic (ROC). All tested parameters in the symptomatic infected group had significant correlations with twenty-five positive and thirty-one negative correlations where the P value was <0.05 and 0.01. Chlorophyll-a parameter had a crucial role of highly significant correlation between total protein and salicylic acid. Contrarily, this correlation with tail moment unit was (r = _0.930, P <0.01) where the P value was < 0.01. The strongest significant negative correlation was between Chlorophyll-a and H2O2 at P < 0.01, while moderate negative significant correlation was seen for Chlorophyll-b where the P value < 0.05. The present study discloses the secret of the three days of rapid transient production of activated oxygen species (AOS) that was enough for having potential quantitative physiological parameters for defensive plant response toward the virus.

Keywords: programmed cell death, plant–adaptive immune response, hydrogen peroxide (H2O2), physiological parameters

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Authors: Enyia James Diwa, Dodeye Ina Igbong, Archibong Archibong Eso

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Gas turbine operators have been faced with the dramatic financial setback resulting from compressor fouling. In a highly deregulated power industry where there is stiffness in the market competition, has made it imperative to improvise means of reducing maintenance cost in other to yield maximum profit. Compressor fouling results from the deposition of contaminants in the presence of oil and moisture on the compressor blade or annulus surfaces, which leads to a loss in flow capacity and compressor efficiency. These combined effects reduce power output, increase heat rate and cause creep life reduction. This paper also contains a model of two gas turbine engines via Cranfield University software known as TURBOMATCH, which is simulation software for detecting engine fouling rate. The model engines are of different configurations and capacities, and are operating in two different modes of constant output power and turbine inlet temperature for a two and three stage filter system. The idea is to investigate the more economically viable filtration systems by gas turbine users based on performance only. It has been demonstrated in the results that the two spool engine is a little more beneficial compared to the single spool. This is as a result of a higher pressure ratio of the two spools as well as the deceleration of the high-pressure compressor and high-pressure turbine speed in a constant TET. Meanwhile, the inlet filtration system was properly designed and balanced with a well-timed and economical compressor washing regime/scheme to control compressor fouling. The different technologies of inlet air filtration and compressor washing are considered and an attempt at optimization with respect to the cost of a combination of both control measures are made.

Keywords: inlet filtration, pressure loss, single spool, two spool

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Authors: Dora Bianchi, Anthony Schinelli, Laura Maria Fatta, Antonia Lonigro, Fabio Lucidi, Fiorenzo Laghi

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Purpose: The role of body image in adolescent binge eating is widely confirmed, albeit the various facets of this relationship are still mostly unexplored. Within the multidimensional body image framework, this study hypothesized the indirect effects of three body image coping strategies (positive rational acceptance, appearance fixing, avoidance) in the expected relationship between the perceived impact of body image on individuals’ quality of life and binge eating symptoms. Methods: Participants were 715 adolescents aged 15-21 years (49.1% girls) recruited in Italian schools. An anonymous self-report online survey was administered. A multiple mediation model was tested. Results: A more positive perceived impact of body image on quality of life was a negative predictor of adolescents’ binge eating, controlling for individual levels of body satisfaction. Three indirect effects were found in this relationship: on one hand, the positive body image impact reduced binge eating via increasing positive rational acceptance (M1), and via reducing avoidance (M2); on the contrary, the positive body image impact also enhanced binge eating via increasing appearance fixing (M3). Conclusions: The body image impact on quality of life can be alternatively protective—when adaptive coping is solicited, and maladaptive strategies are reduced—or a risk factor, which may increase binge eating by soliciting appearance fixing.

Keywords: binge eating, body image satisfaction, quality of life, coping strategies, adolescents

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Authors: Evan W. Krieger, Vijayan K. Asari, Saibabu Arigela

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Video imagery captured for real-time security and surveillance applications is typically captured in complex lighting conditions. These less than ideal conditions can result in imagery that can have underexposed or overexposed regions. It is also typical that the video is too low in resolution for certain applications. The purpose of security and surveillance video is that we should be able to make accurate conclusions based on the images seen in the video. Therefore, if poor lighting and low resolution conditions occur in the captured video, the ability to make accurate conclusions based on the received information will be reduced. We propose a solution to this problem by using image preprocessing to improve these images before use in a particular application. The proposed algorithm will integrate an intensity enhancement algorithm with a super resolution technique. The intensity enhancement portion consists of a nonlinear inverse sign transformation and an adaptive contrast enhancement. The super resolution section is a single image super resolution technique is a Fourier phase feature based method that uses a machine learning approach with kernel regression. The proposed technique intelligently integrates these algorithms to be able to produce a high quality output while also being more efficient than the sequential use of these algorithms. This integration is accomplished by performing the proposed algorithm on the intensity image produced from the original color image. After enhancement and super resolution, a color restoration technique is employed to obtain an improved visibility color image.

Keywords: dynamic range compression, multi-level Fourier features, nonlinear enhancement, super resolution

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Authors: Unity Chipfupa, Aluwani Tagwi, Edilegnaw Wale

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Climate change adaptation studies are challenged by a limited understanding of how non-cognitive factors such as psychological capital affect adaptation decisions of smallholder farmers. The concept of psychological capital has not been fully applied in the empirical literature on climate change adaptation strategies. Hence, the study was meant to assess how psychological capital endowment affects climate change adaptation among smallholder farmers. A multivariate probit regression model was estimated using data collected from 328 smallholder farmers in KwaZulu-Natal, South Africa. The findings indicate that, among other factors, self-confidence and hope or aspirations in farming influence climate change adaptation decisions of smallholders. The psychological capital theory proved to be comprehensive in identifying specific psychological dimensions associated with adaptation decisions. However, the non-alignment of approaches for measuring non-cognitive factors made it difficult to compare results among different studies. In conclusion, the study recommends the need for practical ways for enhancing smallholders’ endowment with key non-cognitive abilities. Researchers should develop and agree on a comprehensive framework for assessing non-cognitive factors critical for climate change adaptation. This will improve the use of positive psychology theories to advance the literature on climate change adaptation. Other key recommendations include targeted support for communities facing higher risks of climate change, improving smallholders’ ability to adapt, promotion of social networks and the inclusion of farming objectives as an important indicator in climate change adaptation research.

Keywords: adaptive capacity, climate change adaptation, psychological capital, multivariate probit, non-cognitive factors.

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Authors: Jinan Fiaidhi, Sabah Mohammed, Petros Zezos

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Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn's disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours to reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning, including deep learning, has failed to report robust results because of the lack of large samples to train its neural nets. In this paper, we are describing a thick data approach that learns from a few anchor images. We are using sound datasets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies in any CE video. We are identifying candidate frames based on feature extraction to provide representative measures of the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach reported accuracy of detecting Crohn's Disease based on the availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine-tune our approach by adding more thick data methods for enhancing diagnosis accuracy.

Keywords: thick data analytics, capsule endoscopy, Crohn’s disease, siamese neural network, decision tree

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Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

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Authors: Zohreh Mohammadi, Jan Kaspar, Peter Lohmander, Robert Marusak, Harald Vacik, Ljusk Ola Eriksson

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Storms are important damaging agents in European forest ecosystems. In the latest decades, significant economic losses in European forestry occurred due to storms. This study investigates the problem of optimal harvest planning when forest stands risk to be felled by storms. One of the most applicable mathematical methods which are being used to optimize forest management is stochastic dynamic programming (SDP). This method belongs to the adaptive optimization class. Sequential decisions, such as harvest decisions, can be optimized based on sequential information about events that cannot be perfectly predicted, such as the future storms and the future states of wind protection from other forest stands. In this paper, stochastic dynamic programming is used to maximize the expected present value of the profits from an area consisting of several forest stands. The region of analysis is the Czech Republic. The harvest decisions, in a particular time period, should be simultaneously taken in all neighbor stands. The reason is that different stands protect each other from possible winds. The optimal harvest age of a particular stand is a function of wind speed and different wind protection effects. The optimal harvest age often decreases with wind speed, but it cannot be determined for one stand at a time. When we consider a particular stand, this stand also protects other stands. Furthermore, the particular stand is protected by neighbor stands. In some forest stands, it may even be rational to increase the harvest age under the influence of stronger winds, in order to protect more valuable stands in the neighborhood. It is important to integrate wind risk in forestry decision-making.

Keywords: Czech republic, forest stands, stochastic dynamic programming, wind risk

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Authors: Andrea Tonoli, Nicola Amati, Maria Cavatorta, Reza Mirsanei, Behzad Mozaffari, Hamed Ahani, Akbar Karamihafshejani, Mohammad Ghazivakili, Mohammad Abuabiah

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The vibrations transmitted to the drivers and passengers through vehicle seat seriously effect on the level of their attention, fatigue and physical health and reduce the comfort and efficiency of the occupants. Recently, some researchers have focused on vibrations at low excitation frequency(0.5-5 Hz) which are considered to be the main risk factors for lumbar part of the backbone but they were not applicable to A and B-segment cars regarding to the size and weight. A semi-active system with two symmetric negative stiffness structures (NSS) in parallel to a positive stiffness structure and actuators has been proposed to attenuate low frequency excitation and makes system flexible regarding to different weight of passengers which is applicable for A and B-Segment cars. Here, the 3 degree of freedom system is considered, dynamic equation clearly is presented, then simulated in MATLAB in order to analysis of performance of the system. The design procedure is derived so that the resonance peak of frequency–response curve shift to the left, the isolating range is increased and especially, the peak of the frequency–response curve is minimized. According to ISO standard different class of road profile as an input is applied to the system to evaluate the performance of the system. To evaluate comfort issues, we extract the RMS value of the vertical acceleration acting on the passenger's body. Then apply the band-pass filter, which takes into account the human sensitivity to acceleration. According to ISO, this weighted acceleration is lower than 0.315 m/s^2, so the ride is considered as comfortable.

Keywords: low frequency excitation, negative stiffness, seat vehicle, vibration isolation

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

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

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

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Authors: Liang Weichien

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Without adequate information and mitigation plan for natural disaster, the risk to urban populated areas will increase in the future as populations grow, especially in Taiwan. Taiwan is recognized as the world's high-risk areas, where an average of 5.7 times of floods occur per year should seek to strengthen coherence and consensus in how cities can plan for flood and climate change. Therefore, this study aims at understanding the vulnerability to flooding in Taipei city, Taiwan, by creating indicators and calculating the vulnerability of each study units. The indicators were grouped into sensitivity and adaptive capacity based on the definition of vulnerability of Intergovernmental Panel on Climate Change. The indicators were weighted by using Principal Component Analysis. However, current researches were based on the assumption that the composition and influence of the indicators were the same in different areas. This disregarded spatial correlation that might result in inaccurate explanation on local vulnerability. The study used Geographically Weighted Principal Component Analysis by adding geographic weighting matrix as weighting to get the different main flood impact characteristic in different areas. Cross Validation Method and Akaike Information Criterion were used to decide bandwidth and Gaussian Pattern as the bandwidth weight scheme. The ultimate outcome can be used for the reduction of damage potential by integrating the outputs into local mitigation plan and urban planning.

Keywords: flood vulnerability, geographically weighted principal components analysis, GWPCA, highly urbanized area, spatial correlation

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Authors: Yanping Liao, Congcong He, Ruigang Zhao

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The application of magnetocardiography signals to detect cardiac electrical function is a new technology developed in recent years. The magnetocardiography signal is detected with Superconducting Quantum Interference Devices (SQUID) and has considerable advantages over electrocardiography (ECG). It is difficult to extract Magnetocardiography (MCG) signal which is buried in the noise, which is a critical issue to be resolved in cardiac monitoring system and MCG applications. In order to remove the severe background noise, the Total Variation (TV) regularization method is proposed to denoise MCG signal. The approach transforms the denoising problem into a minimization optimization problem and the Majorization-minimization algorithm is applied to iteratively solve the minimization problem. However, traditional TV regularization method tends to cause step effect and lacks constraint adaptability. In this paper, an improved TV regularization method for denoising MCG signal is proposed to improve the denoising precision. The improvement of this method is mainly divided into three parts. First, high-order TV is applied to reduce the step effect, and the corresponding second derivative matrix is used to substitute the first order. Then, the positions of the non-zero elements in the second order derivative matrix are determined based on the peak positions that are detected by the detection window. Finally, adaptive constraint parameters are defined to eliminate noises and preserve signal peak characteristics. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance.

Keywords: constraint parameters, derivative matrix, magnetocardiography, regular term, total variation

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: K. C. Ching

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There is growing public concern over the increasing prevalence of student suicide in Hong Kong. Some identify the problem with insufficient social support, while some attribute it to the vast fluctuations in emotional experience and the hindrances to emotion-regulation, both typical of adolescence and emerging adulthood. This study is thus designed to explore the respective effect of perceived social support and emotion self-confidence, on positive emotions and negative emotions. Fifty-seven Hong Kong higher-education students (17 males, 40 females) aged between 18 and 25 (M = 21.78) responded to an online questionnaire consisted of self-reported measures of perceived social support, emotional self-confidence, positive emotions, and negative emotions. Hierarchical regression analysis revealed that emotional self-confidence positively associated with positive emotions and negatively with negative emotions, while perceived social support positively associated with positive emotions but was not related to negative emotions. Perceived social support and emotional self-confidence both predicted positive emotions, but did not interact to predict any emotional outcome. It is concluded that students’ positive and negative emotional experiences are closely related to their emotion-regulation process. But for social support, its effect is merely protective, meaning that although perceived social support generally promotes positive emotions, it alone does not suffice to alleviate students’ negative emotions. These conclusions carry profound implications to suicide prevention practices, including that most existing suicide prevention campaigns should advance from merely fostering mutual support to directly promoting adaptive coping of emotional negativity.

Keywords: emerging adulthood, emotional self-confidence, hong kong, perceived social support, suicide prevention

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Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

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Authors: Kyosuke Kamada, Christoph Kapeller, Michael Jordan, Mostafa Mohammadpour, Christy Li, Christoph Guger

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Cortico-cortical evoked potentials (CCEP) refer to responses generated by cortical electrical stimulation at distant brain sites. These responses provide insights into the functional networks associated with language or motor functions, and in the context of epilepsy, they can reveal pathological networks. Locating the origin and spread of seizures within the cortex is crucial for pre-surgical planning. This process can be enhanced by employing cortical stimulation at the seizure onset zone (SOZ), leading to the generation of CCEPs in remote brain regions that may be targeted for disconnection. In the case of a 24-year-old male patient suffering from intractable epilepsy, corpus callosotomy was performed as part of the treatment. DTI-MRI imaging, conducted using a 3T MRI scanner for fiber tracking, along with CCEP, is used as part of an assessment for surgical planning. Stimulation of the SOZ, with alternating monophasic pulses of 300µs duration and 15mA current intensity, resulted in CCEPs on the contralateral frontal cortex, reaching a peak amplitude of 206µV with a latency of 31ms, specifically in the left pars triangularis. The related fiber tracts were identified with a two-tensor unscented Kalman filter (UKF) technique, showing transversal fibers through the corpus callosum. The CCEPs were monitored through the progress of the surgery. Notably, the SOZ-associated CCEPs exhibited a reduction following the resection of the anterior portion of the corpus callosum, reaching the identified connecting fibers. This intervention demonstrated a potential strategy for mitigating the impact of intractable epilepsy through targeted disconnection of identified cortical regions.

Keywords: CCEP, SOZ, Corpus callosotomy, DTI

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Authors: T. Obateru, Samuel Olaniran

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In an era when the new media is affording easy access to packaging and dissemination of information, the social media have become a popular avenue for sharing information for good or ill. It is evident that the traditional role of journalists as ‘news breakers’ is fast being eroded. People now share information on happenings via the social media like Facebook, Twitter and the rest, such that journalists themselves now get leads on happenings from such sources. Beyond the access to information provided by the new media is the erosion of the gatekeeping role of journalists who by their training and calling, are supposed to handle information with responsibility. Thus, sensitive information that journalists would normally filter is randomly shared by social media activists. This was the experience of journalists in Jos, Plateau State in January 2010 when another of the recurring ethnoreligious crisis that engulfed the state resulted in another widespread killing, vandalism, looting, and displacements. Considered as one of the high points of crises in the state, journalists who had the duty of covering the crisis also relied on some of these sources to get their bearing on the violence. This paper examined the role of Facebook in the work of journalists who covered the 2010 crisis. Taking the gatekeeping perspective, it interrogated the extent to which Facebook impacted their professional duty positively or negatively vis-à-vis the peace journalism model. It employed survey to elicit information from 50 journalists who covered the crisis using questionnaire as instrument. The paper revealed that the dissemination of hate information via mobile phones and social media, especially Facebook, aggravated the crisis situation. Journalists became news followers rather than news breakers because a lot of them were put on their toes by information (many of which were inaccurate or false) circulated on Facebook. It recommended that journalists must remain true to their calling by upholding their ‘gatekeeping’ role of disseminating only accurate and responsible information if they would remain the main source of credible information on which their audience rely.

Keywords: crisis, ethnoreligious, Facebook, journalists

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Authors: Deyu Zhou, Xiao Xue, Lizhen Cui

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With the increasing collaboration among various services and the growing complexity of user demands, there are more and more factors affecting the stable development of the cloud manufacturing service economic system (CMSE). This poses new challenges to the evolution analysis of the CMSE. Many researchers have modeled and analyzed the evolution process of CMSE from the perspectives of individual learning and internal factors influencing the system, but without considering other important characteristics of the system's individuals (such as heterogeneity, bounded rationality, etc.) and the impact of external environmental factors. Therefore, this paper proposes an integrated artificial social model for the cloud manufacturing service economic system, which considers both the characteristics of the system's individuals and the internal and external influencing factors of the system. The model consists of three parts: the Agent model, environment model, and rules model (Agent-Environment-Rules, AER): (1) the Agent model considers important features of the individuals, such as heterogeneity and bounded rationality, based on the adaptive behavior mechanisms of perception, action, and decision-making; (2) the environment model describes the activity space of the individuals (real or virtual environment); (3) the rules model, as the driving force of system evolution, describes the mechanism of the entire system's operation and evolution. Finally, this paper verifies the effectiveness of the AER model through computational and experimental results.

Keywords: cloud manufacturing service economic system (CMSE), AER model, artificial social modeling, integrated framework, computing experiment, agent-based modeling, social networks

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Shabadini Sampath, Jinglang Feng

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With huge kinetic energy, space debris poses a major threat to astronauts’ space activities and spacecraft in orbit if a collision happens. The active removal of space debris is required in order to avoid frequent collisions that would occur. In addition, the amount of space debris will increase uncontrollably, posing a threat to the safety of the entire space system. But the safe and reliable removal of large-scale space debris has been a huge challenge to date. While capturing and deorbiting space debris, the space manipulator has to achieve high control precision. However, due to uncertainties and unknown disturbances, there is difficulty in coordinating the control of the space manipulator. To address this challenge, this paper focuses on developing a robust and intelligent control algorithm that controls joint movement and restricts it on the sliding manifold by reducing uncertainties. A neural network adaptive sliding mode controller (NNASMC) is applied with the objective of finding the control law such that the joint motions of the space manipulator follow the given trajectory. A computed torque control (CTC) is an effective motion control strategy that is used in this paper for computing space manipulator arm torque to generate the required motion. Based on the Lyapunov stability theorem, the proposed intelligent controller NNASMC and CTC guarantees the robustness and global asymptotic stability of the closed-loop control system. Finally, the controllers used in the paper are modeled and simulated using MATLAB Simulink. The results are presented to prove the effectiveness of the proposed controller approach.

Keywords: GNC, active removal of space debris, AI controllers, MatLabSimulink

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Authors: Nasiru Medugu Idris

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This research is aimed at assessing the vulnerability of climate change to rural communities (farmers, herdsmen and fishermen) in Nigeria with the view to study the underlying causes and degree of vulnerability to climate change and examine the conflict between farmers and herdsmen as a result of climate change. This research employed the use of quantitative and qualitative means of data gathering techniques as well as physical observations. Six states (Kebbi, Adamawa, Nasarawa, Osun, Ebonyi, and Akwa Ibom) have been selected on the ground that they are key food production areas in the country and are therefore essential to continual food security in the country. So also, they also double as fishing communities in order to aid the comprehensive study of all the effects on climate on farmers and fishermen alike. Community focus group discussions were carried out in the various states for an interactive session and also to have firsthand information on their level of awareness on climate change. Climate data from the Nigerian Meteorological Agency over the past decade were collected for the purpose of analyzing trends in climate. The study observed that the level of vulnerability of rural dwellers most especially farmers, herdsmen and fishermen to climate change is very high due to their socioeconomic, ethnic and historical perspective of their trend. The study, therefore, recommends that urgent step needs to be put in place to help control natural hazards and man-made disasters and serious measures are also needed in order to minimize severe societal, economic and political crises; some of which may either escalate to violent conflicts or could be avoided by efforts of conflict resolution and prevention by the initiation of a process of de-escalation. So this study has recommended the best-fit adaptive and mitigation measures to climate change vulnerability in rural communities of Nigeria.

Keywords: adaptation, farmers, fishermen, herdsmen

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Authors: Lidija Trandafilovic, Kirsten Leistner, Marie Stenfeldt, Louise Olsson

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Ammonia Selective Catalytic Reduction (NH3 SCR), is one of the most efficient post combustion abatement technologies for removing NOx from diesel engines. In order to remove soot, diesel particulate filters (DPF) are used. Recently, SCR coated filters have been introduced, which captures soot and simultaneously is active for ammonia SCR. There are large advantages with using SCR coated filters, such as decreased volume and also better light off characteristics, since both the SCR function as well as filter function is close to the engine. The objective of this work was to examine the effect of soot, produced using an engine bench, on Cu/SSZ-13 catalysts. The impact of soot on Cu/SSZ-13 in standard SCR, NH3 oxidation, NH3 temperature programmed desorption (TPD), as well as soot oxidation (with and without water) was examined using flow reactor measurements. In all experiments, prior to the soot loading, the fresh activity of Cu/SSZ-13 was recorded with stepwise increasing the temperature from 100°C till 600°C. Thereafter, the sample was loaded with soot and the experiment was repeated in the temperature range from 100°C till 700°C. The amount of CO and CO2 produced in each experiment is used to calculate the soot oxidized at each steady state temperature. The soot oxidized during the heating to next temperature step is included, e.g. the CO+CO2 produced when increasing the temperature to 600°C is added to the 600°C step. The influence of the two factors seem to be of the most importance to soot oxidation: ammonia and water. The influence of water on soot oxidation shift the maximum of CO2 and CO production towards lower temperatures, thus water increases the soot oxidation. Moreover, when adding ammonia to the system it is clear that the soot oxidation is lowered in the presence of ammonia, resulting in larger integrated COx at 500°C for O2+H2O, while opposite results at 600 °C was received where more was oxidised for O2+H2O+NH3 case. To conclude the presence of ammonia reduces the soot oxidation, which is in line with the ammonia TPD results where we found ammonia storage on the soot. Interestingly, during ammonia SCR conditions the activity for soot oxidation is regained at 500°C. At this high temperature the SCR zone is very short, thus the majority of the catalyst is not exposed to ammonia and therefore the inhibition effect of ammonia is not observed.

Keywords: NH3-SCR, Cu/SSZ-13, soot, zeolite

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Authors: Othman Maklouf, Abdunnaser Tresh

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Land vehicle navigation is subject of great interest today. Global Positioning System (GPS) is the main navigation system for positioning in such systems. GPS alone is incapable of providing continuous and reliable positioning, because of its inherent dependency on external electromagnetic signals. Inertial Navigation (INS) is the implementation of inertial sensors to determine the position and orientation of a vehicle. The availability of low-cost Micro-Electro-Mechanical-System (MEMS) inertial sensors is now making it feasible to develop INS using an inertial measurement unit (IMU). INS has unbounded error growth since the error accumulates at each step. Usually, GPS and INS are integrated with a loosely coupled scheme. With the development of low-cost, MEMS inertial sensors and GPS technology, integrated INS/GPS systems are beginning to meet the growing demands of lower cost, smaller size, and seamless navigation solutions for land vehicles. Although MEMS inertial sensors are very inexpensive compared to conventional sensors, their cost (especially MEMS gyros) is still not acceptable for many low-end civilian applications (for example, commercial car navigation or personal location systems). An efficient way to reduce the expense of these systems is to reduce the number of gyros and accelerometers, therefore, to use a partial IMU (ParIMU) configuration. For land vehicular use, the most important gyroscope is the vertical gyro that senses the heading of the vehicle and two horizontal accelerometers for determining the velocity of the vehicle. This paper presents a field experiment for a low-cost strap down (ParIMU)\GPS combination, with data post processing for the determination of 2-D components of position (trajectory), velocity and heading. In the present approach, we have neglected earth rotation and gravity variations, because of the poor gyroscope sensitivities of our low-cost IMU (Inertial Measurement Unit) and because of the relatively small area of the trajectory.

Keywords: GPS, IMU, Kalman filter, materials engineering

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