Search results for: cognitive radio network

Authors: Ricardo A. Martins, Matheus S. da Silva, Gabriel H. F. Iarossi, Helen C. M. Senefonte, Cinthyan R. S. C. de Barbosa

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This paper is the development of a concept of the man/robot interaction. More accurately in developing of an autistic child that have more troubles with interaction, here offers an efficient solution, even though simple; however, less studied for this public. This concept is based on code applied thought out the Lego NXT kit, built for the interpretation of the robot, thereby can create this interaction in a constructive way for children suffering with Autism.

Keywords: lego NXT, interaction, BricX, autismo, ANN (Artificial Neural Network), MLP back propagation, hidden layers

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Authors: Danjuma Bawa

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This paper aimed at exploring the capabilities of Location-Allocation model in complementing the strides of the existing physical planning models in the location and distribution of facilities for regional consumption. The paper was designed to provide a blueprint to the Nigerian government and other donor agencies especially the Fertilizer Distribution Initiative (FDI) by the federal government for the revitalization of the terrorism ravaged regions. Theoretical underpinnings of central place theory related to spatial distribution, interrelationships, and threshold prerequisites were reviewed. The study showcased how Location-Allocation Model (L-AM) alongside Central Place Theory (CPT) was applied in Geographic Information System (GIS) environment to; map and analyze the spatial distribution of settlements; exploit their physical and economic interrelationships, and to explore their hierarchical and opportunistic influences. The study was purely spatial qualitative research which largely used secondary data such as; spatial location and distribution of settlements, population figures of settlements, network of roads linking them and other landform features. These were sourced from government ministries and open source consortium. GIS was used as a tool for processing and analyzing such spatial features within the dictum of CPT and L-AM to produce a comprehensive spatial digital plan for equitable and judicious location and distribution of fertilizer deports in the study area in an optimal way. Population threshold was used as yardstick for selecting suitable settlements that could stand as service centers to other hinterlands; this was accomplished using the query syntax in ArcMapTM. ArcGISTM’ network analyst was used in conducting location-allocation analysis for apportioning of groups of settlements around such service centers within a given threshold distance. Most of the techniques and models ever used by utility planners have been centered on straight distance to settlements using Euclidean distances. Such models neglect impedance cutoffs and the routing capabilities of networks. CPT and L-AM take into consideration both the influential characteristics of settlements and their routing connectivity. The study was undertaken in two terrorism ravaged Local Government Areas of Adamawa state. Four (4) existing depots in the study area were identified. 20 more depots in 20 villages were proposed using suitability analysis. Out of the 300 settlements mapped in the study area about 280 of such settlements where optimally grouped and allocated to the selected service centers respectfully within 2km impedance cutoff. This study complements the giant strides by the federal government of Nigeria by providing a blueprint for ensuring proper distribution of these public goods in the spirit of bringing succor to these terrorism ravaged populace. This will ardently at the same time help in boosting agricultural activities thereby lowering food shortage and raising per capita income as espoused by the government.

Keywords: central place theory, GIS, location-allocation, network analysis, urban and regional planning, welfare economics

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Authors: Arezoo Fallahi, Siamak Derakhshan, Parvaneh Taymoori, Babak Nematshahrbabaki

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Background: Osteoporosis, the most common metabolic bone disease, is an important health care issue. Not only the cost of disease is high but also is one of the causes of disability and mortality and effect on quality of life. Although self-care is effective on disease, s control and treatment but still effective factors on self-care of patient, s viewpoint have not been survey. The aim of this study was to explore effective factors on self-care in women with osteoporosis. Materials and methods: This study was done by conventional content analysis approach in year 2014. Through purposeful sampling 15 women referred to bone mass densitometry centers participated in this study. Inclusion criteria were: Women older than 50 years old with osteoporosis, final diagnosis of osteoporosis for over six –month period, T-score index below -2.5 (lower back or hip), drug use by patients with a physician’s prescription, ability in speaking and attending to participate in the study. Data was collected by face to face and group semi-structure deep interviews and analyzed via content analysis method. To support of rigor of data, criteria credibility, confirmability and transferability were used. Results: during data analysis five categories developed: “hope and disability in the face of illness”, “mutual roles of physician”, “role of family” and “administrative centers and organizations”. To perform self-care behaviors, the participations of this study emphasized on pay attention to their own healthy, regarding patients' rights by physician, pay attention to women's health by men, and the role of media especially radio and television. Conclusion: the finding of the study showed that women’s responsibility with osteoporosis for their health is not a factor but it is multifactorial. Increasing life expectancy in patients, attention to patients needs by physician, increasing health promotion programs in the media and enhancing role of family may provide conditions and infrastructure to empowerment women in doing self-care behavior.

Keywords: women, osteoporosis, self-care, content analysis

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Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

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Authors: M. Sirhan, S. Bekhor, A. Sidess

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In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

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Authors: Tomás Sobrino, Lara García-Varela, Marta Aramburu-Núñez, Mónica Castro, Noemí Gómez-Lado, Mariña Rodríguez-Arrizabalaga, Antía Custodia, Juan Manuel Pías-Peleteiro, José Manuel Aldrey, Daniel Romaus-Sanjurjo, Ángeles Almeida, Pablo Aguiar, Alberto Ouro

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Introduction: Alzheimer’s disease (AD) and other tauopathies are primary causes of dementia, causing progressive cognitive deterioration that entails serious repercussions for the patients' performance of daily tasks. Currently, there is no effective approach for the early diagnosis and treatment of AD and tauopathies. This study suggests a theragnostic approach based on the importance of phosphorylated tau protein (p-Tau) in the early pathophysiological processes of AD. We have developed a novel theragnostic monoclonal antibody (mAb) to provide both diagnostic and therapeutic effects. Methods/Results: We have developed a p-Tau mAb, which was doped with deferoxamine for radiolabeling with Zirconium-89 (89Zr) for PET imaging, as well as fluorescence dies for immunofluorescence assays. The p-Tau mAb was evaluated in vitro for toxicity by MTT assay, LDH activity, propidium iodide/Annexin V assay, caspase-3, and mitochondrial membrane potential (MMP) assay in both mouse endothelial cell line (bEnd.3) and cortical primary neurons cell cultures. Importantly, non-toxic effects (up to concentrations of p-Tau mAb greater than 100 ug/mL) were detected. In vivo experiments in the tauopathy model mice (PS19) show that the 89Zr-pTau-mAb and 89Zr-Fragments-pTau-mAb are stable in circulation for up to 10 days without toxic effects. However, only less than 0.2% reached the brain, so further strategies have to be designed for crossing the Brain-Blood-Barrier (BBB). Moreover, an intraparenchymal treatment strategy was carried out. The PS19 mice were operated to implement osmotic pumps (Alzet 1004) at two different times, at 4 and 7 months, to stimulate the controlled release for one month each of the B6 antibody or the IgG1 control antibody. We demonstrated that B6-treated mice maintained their motor and memory abilities significantly compared with IgG1 treatment. In addition, we observed a significant reduction in p-Tau deposits in the brain. Conclusions /Discussion: A theragnostic pTau-mAb was developed. Moreover, we demonstrated that our p-Tau mAb recognizes very-early pathology forms of p-Tau by non-invasive techniques, such as PET. In addition, p-Tau mAb has non-toxic effects, both in vitro and in vivo. Although the p-Tau mAb is stable in circulation, only 0.2% achieve the brain. However, direct intraventricular treatment significantly reduces cognitive impairment in Alzheimer's animal models, as well as the accumulation of toxic p-Tau species.

Keywords: alzheimer's disease, theragnosis, tau, PET, immunotherapy, tauopathies

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Authors: Angelo Lerro, Manuela Battipede, Piero Gili, Alberto Brandl

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Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility.

Keywords: aerodynamic angles, air data system, flight test, neural network, unmanned aerial vehicle, virtual sensor

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Authors: Jiří Barta, Tomáš Ludík, Jiří Urbánek

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The topics of disaster and emergency management are highly debated among experts. Fast communication will help to deal with emergencies. Problem is with the network connection and data exchange. The paper suggests a solution, which allows possibilities and perspectives of new flexible communication platform to the protection of communication systems for crisis management. This platform is used for everyday communication and communication in crisis situations too.

Keywords: crisis management, information systems, interoperability, crisis communication, security environment, communication platform

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Authors: Hong Yu, Dirk Heider, Suresh Advani

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Increasing demands for high strength and lightweight materials in aircraft industry prompted the wide use of carbon composites in recent decades. Carbon composite laminates used on aircraft structures are subject to lightning strikes. Unlike its metal/alloy counterparts, carbon fiber reinforced composites demonstrate smaller electrical conductivity, yielding more severe damages due to Joule heating. The anisotropic nature of composite laminates makes the electrical and thermal conduction within carbon composite laminates even more complicated. Good understanding of the electrical conduction behavior of carbon composites is the key to effective lightning protection design. The goal of this study is to numerically and experimentally investigate the impact of ultra-high temperature induced by simulated lightning strike on the electrical conduction of carbon composites. A lightning simulator is designed to apply standard lightning current waveform to composite laminates. Multiple carbon composite laminates made from IM7 and AS4 carbon fiber are tested and the transient resistance data is recorded. A microstructure based resistor network model is developed to describe the electrical and thermal conduction behavior, with consideration of temperature dependent material properties. Material degradations such as thermal and electrical breakdown are also modeled to include the effect of high current and high temperature induced by lightning strikes. Good match between the simulation results and experimental data indicates that the developed model captures the major conduction mechanisms. A parametric study is then conducted using the validated model to investigate the effect of system parameters such as fiber volume fraction, inter-ply interface quality, and lightning current waveforms.

Keywords: carbon composite, joule heating, lightning strike, resistor network

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Authors: Manali Chakraborty

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In the developing countries the most prevalent pathological state is malnutrition and under nutrition due to deficiency of essential nutrients. This condition is more seen between the woman population, especially in the adolescent girls. It is causing childhood deaths along with others cognitive, degenerative diseases. Born of low weight babies and stillbirth are also major problems associated with the malnutrition. Along with the increased level of population, people not only should concern about their quantity of food but also for the quality of the food to be healthy. Lethargy, depression caused due to iron deficiency often quoted as normal or unimportant issues. Children of malnourished women are more likely associated with cognitive impairment, immune dysfunction leading to a higher risk of being attacked by diseases. Malnourishment also affects the productivity of women. Low social status, lack of proper nutritional education is an important cause of this nutrient deficiency among women. Iron deficiency and anemia are mostly famous nutritional deficiencies among women worldwide. Mostly women from below poverty lined area are anemic due to less consumption of iron-rich foods or having foods that might inhibit the iron absorption. Growing females like adolescents or lactating females need more iron supply. Less supplement causes iron deficiency. Though malaria also might cause anemia, it is more likely endemic one in some specific areas. Folate deficiency in females also may cause neurological defects in the infants. Other Vitamin B, A deficiencies along with low iodine level is also noted in malnourished women. According to tradition, still in some areas in developing countries females have their food at the end. According to some survey and collected data, these females are often into the risk zone of being malnourished. Regularly they have the very lesser amount, or food sometimes may start to lose its nutrients. Women are the one who maintains the responsibility to cook foods in the home. Lack of proper nutritional education and proper food preparation not only make those foods lose the nutrients leading to impairment in proper nutrient intake of the family members but also impairing own nutritional status. Formulation and development of food products from iron or other nutrient affluent sources viz., traditional herbs can be helpful in the prevention of such malnutrition condition in females. Keeping low cost and smooth maintenance of the food product development from the natural sources like pulses, cereals or other vegetation also can be beneficial to sustain socio-economic condition. Consumption of such kind of foodstuff is much healthier rather than taking continuous supplements like capsules. Utilization of proper scientific and cost-effective techniques for this food product development and their distribution among rural women population might be an enormous initiative.

Keywords: anemia, food supplements, malnutrition, rural places, women population

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Authors: Mou Juliet Rebeiro, S. M. Abul Kalam Azad

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This study was carried out to see the anxiety and depression in caregivers of autistic children. The objectives of the research were to assess depression and anxiety among caregivers of autistic children and to find out the experience of caregivers. For this purpose, the research was conducted on a sample of 39 caregivers of autistic children. Participants were taken from a special school. To collect data for this study each of the caregivers were administered questionnaire comprising scales to measure anxiety and depression and some responses of the participants were taken through interview based on a topic guide. Obtained quantitative data were analyzed by using statistical analysis and qualitative data were analyzed according to themes. Mean of the anxiety score (55.85) and depression score (108.33) is above the cutoff point. Results showed that anxiety and depression is clinically present in caregivers of autistic children. Most of the caregivers experienced behavior, emotional, cognitive and social problems of their child that is linked with anxiety and depression.

Keywords: anxiety, autism, caregiver, depression

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Authors: Swee Yang Low, Dong Eon Kim, Canh Tien Trinh Nguyen, Yixiong Cai, Shie-Yui Liong

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Nee Soon swamp forest is one of the last vestiges of tropical wetland in Singapore. Understanding the hydrological regime of the swamp forest and implications for water quality is critical to guide stakeholders in implementing effective measures to preserve the wetland against anthropogenic impacts. In particular, although current field measurement data do not indicate a concern with organic pollution, reviewing the ways in which the wetland responds to elevated organic waste influx (and the corresponding impact on dissolved oxygen, DO) can help identify potential hotspots, and the impact on the outflow from the catchment which drains into downstream controlled watercourses. An integrated water quality model is therefore developed in this study to investigate spatial and temporal concentrations of DO levels and organic pollution (as quantified by biochemical oxygen demand, BOD) within the catchment’s river network under hypothetical, projected scenarios of spiked upstream inflow. The model was developed using MIKE HYDRO for modelling the study domain, as well as the MIKE ECO Lab numerical laboratory for characterising water quality processes. Model parameters are calibrated against time series of observed discharges at three measurement stations along the river network. Over a simulation period of April 2014 to December 2015, the calibrated model predicted that a continuous spiked inflow of 400 mg/l BOD will elevate downstream concentrations at the catchment outlet to an average of 12 mg/l, from an assumed nominal baseline BOD of 1 mg/l. Levels of DO were decreased from an initial 5 mg/l to 0.4 mg/l. Though a scenario of spiked organic influx at the swamp forest’s undeveloped upstream sub-catchments is currently unlikely to occur, the outcomes nevertheless will be beneficial for future planning studies in understanding how the water quality of the catchment will be impacted should urban redevelopment works be considered around the swamp forest.

Keywords: hydrology, modeling, water quality, wetland

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Authors: Martina Kaufmann, Thomas Goetz, Anastasiya A. Lipnevich, Reinhard Pekrun

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Positive emotions have been shown to benefit from optimistic perceptions, even if these perceptions are illusory. The current research investigated the impact of illusions of control on positive emotions. There is empirical evidence showing that people are more emotionally attentive to losses than to gains. Hence, we expected that, compared to gains, losses in illusory control would have a stronger impact on positive emotions. The results of two experimental studies support this assumption: Participants who experienced gains in illusory control showed no substantial change in positive emotions. However, positive emotions decreased when they perceived a loss in illusory control. These results suggest that a loss of illusory control (but not a gain thereof) mediates the impact of the situation on individuals’ positive emotions. Implications for emotion theory and practice are discussed.

Keywords: cognitive appraisal, control, illusions, optimism, positive emotions

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Khachen Kongpakwattana, Nathorn Chaiyakunapruk

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Background: Decision-analytic models for Alzheimer’s disease (AD) have been advanced to discrete-event simulation (DES), in which individual-level modelling of disease progression across continuous severity spectra and incorporation of key parameters such as treatment persistence into the model become feasible. This study aimed to apply the DES to perform a cost-effectiveness analysis of treatment for AD in Thailand. Methods: A dataset of Thai patients with AD, representing unique demographic and clinical characteristics, was bootstrapped to generate a baseline cohort of patients. Each patient was cloned and assigned to donepezil, galantamine, rivastigmine, memantine or no treatment. Throughout the simulation period, the model randomly assigned each patient to discrete events including hospital visits, treatment discontinuation and death. Correlated changes in cognitive and behavioral status over time were developed using patient-level data. Treatment effects were obtained from the most recent network meta-analysis. Treatment persistence, mortality and predictive equations for functional status, costs (Thai baht (THB) in 2017) and quality-adjusted life year (QALY) were derived from country-specific real-world data. The time horizon was 10 years, with a discount rate of 3% per annum. Cost-effectiveness was evaluated based on the willingness-to-pay (WTP) threshold of 160,000 THB/QALY gained (4,994 US$/QALY gained) in Thailand. Results: Under a societal perspective, only was the prescription of donepezil to AD patients with all disease-severity levels found to be cost-effective. Compared to untreated patients, although the patients receiving donepezil incurred a discounted additional costs of 2,161 THB, they experienced a discounted gain in QALY of 0.021, resulting in an incremental cost-effectiveness ratio (ICER) of 138,524 THB/QALY (4,062 US$/QALY). Besides, providing early treatment with donepezil to mild AD patients further reduced the ICER to 61,652 THB/QALY (1,808 US$/QALY). However, the dominance of donepezil appeared to wane when delayed treatment was given to a subgroup of moderate and severe AD patients [ICER: 284,388 THB/QALY (8,340 US$/QALY)]. Introduction of a treatment stopping rule when the Mini-Mental State Exam (MMSE) score goes below 10 to a mild AD cohort did not deteriorate the cost-effectiveness of donepezil at the current treatment persistence level. On the other hand, none of the AD medications was cost-effective when being considered under a healthcare perspective. Conclusions: The DES greatly enhances real-world representativeness of decision-analytic models for AD. Under a societal perspective, treatment with donepezil improves patient’s quality of life and is considered cost-effective when used to treat AD patients with all disease-severity levels in Thailand. The optimal treatment benefits are observed when donepezil is prescribed since the early course of AD. With healthcare budget constraints in Thailand, the implementation of donepezil coverage may be most likely possible when being considered starting with mild AD patients, along with the stopping rule introduced.

Keywords: Alzheimer's disease, cost-effectiveness analysis, discrete event simulation, health technology assessment

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Catarina Cruz, Ana Breda

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In the early years, until the children’s entrance at the elementary school, they are stimulated by their educators, through rich and attractive contexts, to explore and develop skills in different domains, from the socio-emotional to the cognitive. Many of these contexts trigger real or imaginary situations, familiar or not, through resources or pedagogical practices that incite children's curiosity, questioning, expression of ideas or emotions, social interaction, among others. Later, when children enter at the elementary school, their activity at school becomes more focused on developing skills in the cognitive domain, namely acquiring learning from different subject areas, such as Mathematics, Natural Sciences, History, among others. That is, to ensure that children develop the standardized learning recommended in the guiding curriculum documents, they spend part of their time applying formulas, memorizing information, following instructions, and so on, and in this way not much time is left to listen children, to learn about their interests and likes, as well as their perspective and questions about the surround world. In Elementary School, especially in the 1st Cycle, children are naturally curious, however, sometimes this skill is subtly conditioned by adults. Curious children learn more, since they have an intrinsic desire to know more, especially about what is unknown. When children think on subjects or themes that they are interested in or curious about, they attribute more meaning to this learning and retain it for longer. Therefore, it is important to approach subjects in the classroom that seduce or captivate children's attention, trigger them curiosity, and allow to hear their ideas. There are several resources, strategies and pedagogical practices to awaken children's curiosity, to explore their knowledge, to understand their perspectives and their way of thinking, to know a little more about their personality and to provide space for dialogue. The storytelling, its narrative’s exploration and interpretation is one of those pedagogical practices. Children’s literature, about real or imaginary subjects, stimulate children’s insights supported into their experiences, emotions, learnings and personality, and promote opportunities for children express freely their feelings and thoughts. This work focuses on a session developed with children in the 3rd year of schooling, from a Portuguese 1st Cycle Basic School, in which the story "From the Outside In and From the Inside Out" was presented. The story’s presentation was mainly centred on children’s activity, who read excerpts and interpreted/explored them through a dialogue led by one of the authors. The study presented here intends to show an example of how an exploration of a children's story can trigger ideas, thoughts, emotions or attitudes in children in the 3rd year of elementary school. To answer the research question, this work aimed to: identify ideas, thoughts, emotions or attitudes that emerged from the exploration of story; analyse aspects of the story and the orchestration/conduction of dialogue with/between children that facilitated or inhibited the emergence of ideas, thoughts, emotions or attitudes by children,

Keywords: storytelling, children’s perspectives, soft skills, non-formal learning contexts, orchestration

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Authors: Elmahdi Mohammadine, Ahouzi Esmail, Najid Abdellah

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This contribution aims to present a new protected hybrid WDM/TDM PON architecture using Wavelength Selective Switches and Optical Line Protection devices. The objective from using these technologies is to improve flexibility and enhance the protection of GPON networks.

Keywords: Wavlenght Division Multiplexed Passive Optical Network (WDM-PON), Time Division Multiplexed PON (TDM-PON), architecture, Protection, Wavelength Selective Switches (WSS), Optical Line Protection (OLP)

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Alejandro Tellez-Jurado, Juan C. Seck-Tuoh-Mora, Eva S. Hernandez-Gress, Norberto Hernandez-Romero, Iaina P. Medina-Serna

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Trichoderma harzianum is a fungus that has been utilized as a low-cost fungicide for biological control of pests, and it is important to determine the optimal conditions to produce the highest amount of conidiospores of Trichoderma harzianum. In this work, the conidiospore production of Trichoderma harzianum is modeled and optimized by using Artificial Neural Networks (AANs). In order to gather data of this process, 30 experiments were carried out taking into account the number of hours of culture (10 distributed values from 48 to 136 hours) and the culture humidity (70, 75 and 80 percent), obtained as a response the number of conidiospores per gram of dry mass. The experimental results were used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers, and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The ANN with the best performance was chosen in order to simulate the process and be able to maximize the conidiospores production. The obtained ANN with the highest performance has 2 inputs and 1 output, three hidden layers with 3, 10 and 10 neurons in each layer, respectively. The ANN performance shows an R2 value of 0.9900, and the Root Mean Squared Error is 1.2020. This ANN predicted that 644175467 conidiospores per gram of dry mass are the maximum amount obtained in 117 hours of culture and 77% of culture humidity. In summary, the ANN approach is suitable to represent the conidiospores production of Trichoderma harzianum because the R2 value denotes a good fitting of experimental results, and the obtained ANN model was used to find the parameters to produce the biggest amount of conidiospores per gram of dry mass.

Keywords: Trichoderma harzianum, modeling, optimization, artificial neural network

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Shofiayuningtyas Luftiani

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Some private schools in Indonesia started integrating the online program Gizmos in the teaching-learning process. Gizmos was developed to supplement the existing curriculum by integrating it into the instructional programs. The program has some features using an inquiry-based simulation, in which students conduct exploration by using a worksheet while teachers use the teacher guidelines to direct and assess students’ performance In this study, the discussion about Gizmos highlights its features as the assessment media of mathematics learning for secondary school students. The discussion is based on the case study and literature review from the Indonesian context. The purpose of applying Gizmos as an assessment media refers to the diagnostic assessment. As a part of the diagnostic assessment, the teachers review the student exploration sheet, analyze particularly in the students’ difficulties and consider findings in planning future learning process. This assessment becomes important since the teacher needs the data about students’ persistent weaknesses. Additionally, this program also helps to build student’ understanding by its interactive simulation. Currently, the assessment over-emphasizes the students’ answers in the worksheet based on the provided answer keys while students perform their skill in translating the question, doing the simulation and answering the question. Whereas, the assessment should involve the multiple perspectives and sources of students’ performance since teacher should adjust the instructional programs with the complexity of students’ learning needs and styles. Consequently, the approach to improving the assessment components is selected to challenge the current assessment. The purpose of this challenge is to involve not only the cognitive diagnosis but also the analysis of skills and error. Concerning the selected setting for this diagnostic assessment that develops the combination of cognitive diagnosis, skills analysis and error analysis, the teachers should create an assessment rubric. The rubric plays the important role as the guide to provide a set of criteria for the assessment. Without the precise rubric, the teacher potentially ineffectively documents and follows up the data about students at risk of failure. Furthermore, the teachers who employ the program of Gizmos as the diagnostic assessment might encounter some obstacles. Based on the condition of assessment in the selected setting, the obstacles involve the time constrain, the reluctance of higher teaching burden and the students’ behavior. Consequently, the teacher who chooses the Gizmos with those approaches has to plan, implement and evaluate the assessment. The main point of this assessment is not in the result of students’ worksheet. However, the diagnostic assessment has the two-stage process; the process to prompt and effectively follow-up both individual weaknesses and those of the learning process. Ultimately, the discussion of Gizmos as the media of the diagnostic assessment refers to the effort to improve the mathematical learning process.

Keywords: diagnostic assessment, error analysis, Gizmos online program, skills analysis

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Authors: M. Ahmadi, Sh. Damanabi, F. Sadoughi

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National Health Information System plays an important role in ensuring timely and reliable access to Health information which is essential for strategic and operational decisions that improve health, quality and effectiveness of health care. In other words, by using the National Health information system you can improve the quality of health data, information and knowledge used to support decision making at all levels and areas of the health sector. Since full identification of the components of this system for better planning and management influential factors of performance seems necessary, therefore, in this study, different attitudes towards components of this system are explored comparatively. Methods: This is a descriptive and comparative kind of study. The society includes printed and electronic documents containing components of the national health information system in three parts: input, process, and output. In this context, search for information using library resources and internet search were conducted and data analysis was expressed using comparative tables and qualitative data. Results: The findings showed that there are three different perspectives presenting the components of national health information system, Lippeveld, Sauerborn, and Bodart Model in 2000, Health Metrics Network (HMN) model from World Health Organization in 2008 and Gattini’s 2009 model. All three models outlined above in the input (resources and structure) require components of management and leadership, planning and design programs, supply of staff, software and hardware facilities, and equipment. In addition, in the ‘process’ section from three models, we pointed up the actions ensuring the quality of health information system and in output section, except Lippeveld Model, two other models consider information products, usage and distribution of information as components of the national health information system. Conclusion: The results showed that all the three models have had a brief discussion about the components of health information in input section. However, Lippeveld model has overlooked the components of national health information in process and output sections. Therefore, it seems that the health measurement model of network has a comprehensive presentation for the components of health system in all three sections-input, process, and output.

Keywords: National Health Information System, components of the NHIS, Lippeveld Model

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: Ioanna Taouki, Marie Lallier, David Soto

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Metacognition refers to the capacity to reflect upon our own cognitive processes. Although there is an ongoing discussion in the literature on the role of metacognition in learning and academic achievement, little is known about its neurodevelopmental trajectories in early childhood, when children begin to receive formal education in reading. Here, we evaluate the metacognitive ability, estimated under a recently developed Signal Detection Theory model, of a cohort of children aged between 6 and 7 (N=60), who performed three two-alternative-forced-choice tasks (two linguistic: lexical decision task, visual attention span task, and one non-linguistic: emotion recognition task) including trial-by-trial confidence judgements. Our study has three aims. First, we investigated how metacognitive ability (i.e., how confidence ratings track accuracy in the task) relates to performance in general standardized tasks related to students' reading and general cognitive abilities using Spearman's and Bayesian correlation analysis. Second, we assessed whether or not young children recruit common mechanisms supporting metacognition across the different task domains or whether there is evidence for domain-specific metacognition at this early stage of development. This was done by examining correlations in metacognitive measures across different task domains and evaluating cross-task covariance by applying a hierarchical Bayesian model. Third, using robust linear regression and Bayesian regression models, we assessed whether metacognitive ability in this early stage is related to the longitudinal learning of children in a linguistic and a non-linguistic task. Notably, we did not observe any association between students’ reading skills and metacognitive processing in this early stage of reading acquisition. Some evidence consistent with domain-general metacognition was found, with significant positive correlations between metacognitive efficiency between lexical and emotion recognition tasks and substantial covariance indicated by the Bayesian model. However, no reliable correlations were found between metacognitive performance in the visual attention span and the remaining tasks. Remarkably, metacognitive ability significantly predicted children's learning in linguistic and non-linguistic domains a year later. These results suggest that metacognitive skill may be dissociated to some extent from general (i.e., language and attention) abilities and further stress the importance of creating educational programs that foster students’ metacognitive ability as a tool for long term learning. More research is crucial to understand whether these programs can enhance metacognitive ability as a transferable skill across distinct domains or whether unique domains should be targeted separately.

Keywords: confidence ratings, development, metacognitive efficiency, reading acquisition

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Authors: Lana Burmistrova

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Introduction: The influence of visual impairments on several cognitive processes used in the music learning process is an increasingly important area in special education and cognitive musicology. Many children have several visual impairments due to the refractive errors and irreversible inhibitors. However, based on the compensatory neuroplasticity and functional reorganization, congenitally blind (CB) and early blind (EB) individuals use several areas of the occipital lobe to perceive and process auditory and tactile information. CB individuals have greater memory capacity, memory reliability, and less false memory mechanisms are used while executing several tasks, they have better working memory (WM) and short-term memory (STM). Blind individuals use several strategies while executing tactile and working memory n-back tasks: verbalization strategy (mental recall), tactile strategy (tactile recall) and combined strategies. Methods and design: The aim of the pilot study was to substantiate similar tendencies while executing attention, memory and combined auditory tasks in blind and sighted individuals constructed for this study, and to investigate attention, memory and combined mechanisms used in the music learning process. For this study eight (n=8) blind and eight (n=8) sighted individuals aged 13-20 were chosen. All respondents had more than five years music performance and music learning experience. In the attention task, all respondents had to identify pitch changes in tonal and randomized melodic pairs. The memory task was based on the mismatch negativity (MMN) proportion theory: 80 percent standard (not changed) and 20 percent deviant (changed) stimuli (sequences). Every sequence was named (na-na, ra-ra, za-za) and several items (pencil, spoon, tealight) were assigned for each sequence. Respondents had to recall the sequences, to associate them with the item and to detect possible changes. While executing the combined task, all respondents had to focus attention on the pitch changes and had to detect and describe these during the recall. Results and conclusion: The results support specific features in CB and EB, and similarities between late blind (LB) and sighted individuals. While executing attention and memory tasks, it was possible to observe the tendency in CB and EB by using more precise execution tactics and usage of more advanced periodic memory, while focusing on auditory and tactile stimuli. While executing memory and combined tasks, CB and EB individuals used passive working memory to recall standard sequences, active working memory to recall deviant sequences and combined strategies. Based on the observation results, assessment of blind respondents and recording specifics, following attention and memory correlations were identified: reflective attention and STM, reflective attention and periodic memory, auditory attention and WM, tactile attention and WM, auditory tactile attention and STM. The results and the summary of findings highlight the attention and memory features used in the music learning process in the context of blindness, and the tendency of the several attention and memory types correlated based on the task, strategy and individual features.

Keywords: attention, blindness, memory, music learning, strategy

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Authors: Eiji Nakamachi, Ryota Sakiyama, Koji Yamamoto, Yusuke Morita, Hidetoshi Sakamoto

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The regeneration of injured central nerve network caused by the cerebrovascular accidents is difficult, because of poor regeneration capability of central nerve system composed of the brain and the spinal cord. Recently, new regeneration methods such as transplant of nerve cells and supply of nerve nutritional factor were proposed and examined. However, there still remain many problems with the canceration of engrafted cells and so on and it is strongly required to establish an efficacious treating method of a central nerve system. Blackman proposed the electromagnetic stimulation method to enhance the axonal nerve extension. In this study, we try to design and fabricate a new three-dimensional (3D) bio-reactor, which can load a uniform AC magnetic field stimulation on PC12 cells in the extracellular environment for enhancement of an axonal nerve extension and 3D nerve network generation. Simultaneously, we measure the morphology of PC12 cell bodies, axons, and dendrites by the multiphoton excitation fluorescence microscope (MPM) and evaluate the effectiveness of the uniform AC magnetic stimulation to enhance the axonal nerve extension. Firstly, we designed and fabricated the uniform AC magnetic field stimulation bio-reactor. For the AC magnetic stimulation system, we used the laminated silicon steel sheets for a yoke structure of 3D chamber, which had a high magnetic permeability. Next, we adopted the pole piece structure and installed similar specification coils on both sides of the yoke. We searched an optimum pole piece structure using the magnetic field finite element (FE) analyses and the response surface methodology. We confirmed that the optimum 3D chamber structure showed a uniform magnetic flux density in the PC12 cell culture area by using FE analysis. Then, we fabricated the uniform AC magnetic field stimulation bio-reactor by adopting analytically determined specifications, such as the size of chamber and electromagnetic conditions. We confirmed that measurement results of magnetic field in the chamber showed a good agreement with FE results. Secondly, we fabricated a dish, which set inside the uniform AC magnetic field stimulation of bio-reactor. PC12 cells were disseminated with collagen gel and could be 3D cultured in the dish. The collagen gel were poured in the dish. The collagen gel, which had a disk shape of 6 mm diameter and 3mm height, was set on the membrane filter, which was located at 4 mm height from the bottom of dish. The disk was full filled with the culture medium inside the dish. Finally, we evaluated the effectiveness of the uniform AC magnetic field stimulation to enhance the nurve axonal extension. We confirmed that a 6.8 increase in the average axonal extension length of PC12 under the uniform AC magnetic field stimulation at 7 days culture in our bio-reactor, and a 24.7 increase in the maximum axonal extension length. Further, we confirmed that a 60 increase in the number of dendrites of PC12 under the uniform AC magnetic field stimulation. Finally, we confirm the availability of our uniform AC magnetic stimulation bio-reactor for the nerve axonal extension and the nerve network generation.

Keywords: nerve regeneration, axonal extension , PC12 cell, magnetic field, three-dimensional bio-reactor

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Authors: Joanna Skopinska-Wisniewska, Anna Bajek, Marta Ziegler-Borowska, Alina Sionkowska

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Hydrogels are a class of materials widely used in medicine for many years. Proteins, such as collagen, due to the presence of a large number of functional groups are easily wettable by polar solvents and can create hydrogels. The supramolecular network capable to swelling is created by cross-linking of the biopolymers using various reagents. Many cross-linking agents has been tested for last years, however, researchers still are looking for a new, more secure reactants. Squaric acid, 3,4-dihydroxy 3-cyclobutene 1,2- dione, is a very strong acid, which possess flat and rigid structure. Due to the presence of two carboxyl groups the squaric acid willingly reacts with amino groups of collagen. The main purpose of this study was to investigate the influence of addition of squaric acid on the chemical, physical and biological properties of collagen materials. The collagen type I was extracted from rat tail tendons and 1% solution in 0.1M acetic acid was prepared. The samples were cross-linked by the addition of 5%, 10% and 20% of squaric acid. The mixtures of all reagents were incubated 30 min on magnetic stirrer and then dialyzed against deionized water. The FTIR spectra show that the collagen structure is not changed by cross-linking by squaric acid. Although the mechanical properties of the collagen material deteriorate, the temperature of thermal denaturation of collagen increases after cross-linking, what indicates that the protein network was created. The lyophilized collagen gels exhibit porous structure and the pore size decreases with the higher addition of squaric acid. Also the swelling ability is lower after the cross-linking. The in vitro study demonstrates that the materials are attractive for 3T3 cells. The addition of squaric acid causes formation of cross-ling bonds in the collagen materials and the transparent, stiff hydrogels are obtained. The changes of physicochemical properties of the material are typical for cross-linking process, except mechanical properties – it requires further experiments. However, the results let us to conclude that squaric acid is a suitable cross-linker for protein materials for medicine and tissue engineering.

Keywords: collagen, squaric acid, cross-linking, hydrogel

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Authors: Maria Grazia Albanesi, Riccardo Amadeo

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This study presents a new model of the human image quality assessment process: the aim is to highlight the foundations of the image quality metrics proposed in literature, by identifying the cognitive/physiological or mathematical principles of their development and the relation with the actual human quality assessment process. The model allows to create a novel categorization of objective and subjective image quality metrics. Our work includes an overview of the most used or effective objective metrics in literature, and, for each of them, we underline its main characteristics, with reference to the rationale of the proposed model and categorization. From the results of this operation, we underline a problem that affects all the presented metrics: the fact that many aspects of human biases are not taken in account at all. We then propose a possible methodology to address this issue.

Keywords: eye-tracking, image quality assessment metric, MOS, quality of user experience, visual perception

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Authors: Mohammad Hossein Mohammadi Sanjani, Ashknaz Oraee, Oriol Gomis Bellmunt, Vinicius Albernaz Lacerda Freitas

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The use of distributed and renewable sources in power systems has grown significantly, recently. One the most popular sources are wind farms which have grown massively. However, ¬wind farms are connected to the grid, this can cause problems such as reduced voltage stability, frequency fluctuations and reduced dynamic stability. Variable speed generators (asynchronous) are used due to the uncontrollability of wind speed specially Doubley Fed Induction Generators (DFIG). The most important disadvantage of DFIGs is its sensitivity to voltage drop. In the case of faults, a large volume of reactive power is induced therefore, use of FACTS devices such as SVC and STATCOM are suitable for improving system output performance. They increase the capacity of lines and also passes network fault conditions. In this paper, in addition to modeling the reactive power control system in a DFIG with converter, FACTS devices have been used in a DFIG wind turbine to improve the stability of the power system containing two synchronous sources. In the following paper, recent optimal control systems have been designed to minimize fluctuations caused by system disturbances, for FACTS devices employed. For this purpose, a suitable method for the selection of nine parameters for MPSH-phase-post-phase compensators of reactive power compensators is proposed. The design algorithm is formulated ¬¬as an optimization problem searching for optimal parameters in the controller. Simulation results show that the proposed controller Improves the stability of the network and the fluctuations are at desired speed.

Keywords: renewable energy sources, optimization wind power plant, stability, reactive power compensator, double-feed induction generator, optimal control, genetic algorithm

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

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Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: drug addiction, artificial neural networks, multilayer perceptron (MLP), decision support system

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