Search results for: the health improvement network (THIN)

Authors: Hilal Uslu, Salih Eroglu, Betul Ozkan, Ozcan Bulantekin, Alper Kuscu

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In this study, the strawberry (Fragaria L.) fruits, which were pretreated with ultrasound (US), were worked on in the microwave by using 90W power. Then mathematical modelling was applied to dried fruits by using different experimental thin layer models. The sliced fruits were subjected to ultrasound treatment at a frequency of 40 kHz for 10, 20, and 30 minutes, in an ultrasonic water bath, with a ratio of 1:4 to fruit/water. They are then dried in the microwave (90W). The drying process continued until the product moisture was below 10%. By analyzing the moisture change of the products at a certain time, eight different thin-layer drying models, (Newton, page, modified page, Midilli, Henderson and Pabis, logarithmic, two-term, Wang and Singh) were tested for verification of experimental data. MATLAB R2015a statistical program was used for the modelling, and the best suitable model was determined with R²adj (coefficient of determination of compatibility), and root mean square error (RMSE) values. According to analysis, the drying model that best describes the drying behavior for both drying conditions was determined as the Midilli model by high R²adj and low RMSE values. Control, 10, 20, and 30 min US for groups R²adj and RMSE values was established as respectively; 0,9997- 0,005298; 0,9998- 0,004735; 0,9995- 0,007031; 0,9917-0,02773. In addition, effective diffusion coefficients were calculated for each group and were determined as 3,80x 10⁻⁸, 3,71 x 10⁻⁸, 3,26 x10⁻⁸ ve 3,5 x 10⁻⁸ m/s, respectively.

Keywords: mathematical modelling, microwave drying, strawberry, ultrasound

Procedia PDF Downloads 153

Authors: Sau Kam Adeline Chan

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World Health Organization had listed ‘Communication during Patient Care Handovers’ as one of its highest 5 patient safety initiatives. Clinical handover means transfer of accountability and responsibility of clinical information from one health professional to another. The main goal of clinical handover is to convey patient’s current condition and treatment plan accurately. Ineffective communication at point of care is globally regarded as the main cause of the sentinel event. Situation, Background, Assessment and Recommendation (SBAR), a communication tool, is extensively regarded as an effective communication tool in healthcare setting. Nonetheless, just by scenario-based program in nursing school or attending workshops on SBAR would not be enough for freshly graduated nurses to apply it competently in a complex clinical practice. To what extend and in-depth of information should be conveyed during handover process is not easy to learn. As such, on-site coaching is essential to upgrade their expertise on the usage of SBAR and ultimately to avoid any clinical error. On-site coaching for all freshly graduated nurses on the usage of SBAR in clinical handover was commenced in August 2014. During the preceptorship period, freshly graduated nurses were coached by the preceptor. After that, they were gradually assigned to take care of a group of patients independently. Nurse leaders would join in their shift handover process at patient’s bedside. Feedback and support were given to them accordingly. Discrepancies on their clinical handover process were shared with them and documented for further improvement work. Owing to the constraint of manpower in nurse leader, about coaching for 30 times were provided to a nurse in a year. Staff satisfaction survey was conducted to gauge their feelings about the coaching and look into areas for further improvement. Number of clinical error avoided was documented as well. The nurses reported that there was a significant improvement particularly in their confidence and knowledge in clinical handover process. In addition, the sense of empowerment was developed when liaising with senior and experienced nurses. Their proficiency in applying SBAR was enhanced and they become more alert to the critical criteria of an effective clinical handover. Most importantly, accuracy of transferring patient’s condition was improved and repetition of information was avoided. Clinical errors were prevented and quality patient care was ensured. Using SBAR as a communication tool looks simple. The tool only provides a framework to guide the handover process. Nevertheless, without on-site training, loophole on clinical handover still exists, patient’s safety will be affected and clinical error still happens.

Keywords: freshly graduated nurse, competency of clinical handover, quality, clinical error

Procedia PDF Downloads 150

Authors: Adewusi Adeoluwawale, Oronti Iyabosola Busola, Akinola Iretiayo, Komolafe Olusola Aderibigbe

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The complex and radial structure of the low voltage distribution network (415V) makes it vulnerable to faults which are due to system and the environmental related factors. Besides these, the protective scheme employed on the low voltage network which is the fuse cannot be monitored remotely such that in the event of sustained fault, the utility will have to rely solely on the complaint brought by customers for loss of supply and this tends to increase the length of outages. A microcontroller based fault detection scheme is hereby developed to detect low voltage and high voltage fault conditions which are common faults on this network. Voltages below 198V and above 242V on the distribution feeders are classified and detected as low voltage and high voltages respectively. Results shows that the developed scheme produced a good response time in the detection of these faults.

Keywords: fault detection, low voltage distribution feeders, outage times, sustained faults

Procedia PDF Downloads 543

Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

Procedia PDF Downloads 367

Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

Procedia PDF Downloads 114

Authors: Lilia Edit Aparicio Pico, Paulo Cesar Coronado Sánchez, Roberto Ferro Escobar

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This paper presents the implementation of telemedicine services for physiotherapy, occupational therapy, and speech therapy rehabilitation, utilizing telebroadcasting of audiovisual content to enhance comprehensive patient recovery in rural areas of San Vicente del Caguán municipality, characterized by high levels of social conflict in Colombia. The region faces challenges such as dysfunctional problems, physical rehabilitation needs, and a high prevalence of hearing diseases, leading to neglect and substandard health services. Limited access to healthcare due to communication barriers and transportation difficulties exacerbates these issues. To address these challenges, a research initiative was undertaken to leverage information and communication technologies (ICTs) to improve healthcare quality and accessibility for this vulnerable population. The primary objective was to develop a tele-rehabilitation system to provide asynchronous online therapies and teleconsultation services for patient follow-up during the recovery process. The project comprises two components: Communication systems and human development. A technological component involving the establishment of a wireless network connecting rural centers and the development of a mobile application for video-based therapy delivery. Communications systems will be provided by a radio link that utilizes internet provided by the Colombian government, located in the municipality of San Vicente del Caguán to connect two rural centers (Pozos and Tres Esquinas) and a mobile application for managing videos for asynchronous broadcasting in sidewalks and patients' homes. This component constitutes an operational model integrating information and telecommunications technologies. The second component involves pedagogical and human development. The primary focus is on the patient, where performance indicators and the efficiency of therapy support were evaluated for the assessment and monitoring of telerehabilitation results in physical, occupational, and speech therapy. They wanted to implement a wireless network to ensure audiovisual content transmission for tele-rehabilitation, design audiovisual content for tele-rehabilitation based on services provided by the ESE Hospital San Rafael in physiotherapy, occupational therapy, and speech therapy, develop a software application for fixed and mobile devices enabling access to tele-rehabilitation audiovisual content for healthcare personnel and patients and finally to evaluate the technological solution's contribution to the ESE Hospital San Rafael community. The research comprised four phases: wireless network implementation, audiovisual content design, software application development, and evaluation of the technological solution's impact. Key findings include the successful implementation of virtual teletherapy, both synchronously and asynchronously, and the assessment of technological performance indicators, patient evolution, timeliness, acceptance, and service quality of tele-rehabilitation therapies. The study demonstrated improved service coverage, increased care supply, enhanced access to timely therapies for patients, and positive acceptance of teletherapy modalities. Additionally, the project generated new knowledge for potential replication in other regions and proposed strategies for short- and medium-term improvement of service quality and care indicators

Keywords: e-health, medical informatics, telemedicine, telerehabilitation, virtual therapy

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Authors: Hannah Riva, Sarah Mazal, Jessica L. Marquez, Michael Rains

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A 70-year-old female with a Fitzpatrick skin phenotype II presented with a 13-year history of a scaly rash located on the left breast and bilateral pretibial regions. The patient’s past medical history was otherwise unremarkable, with the exception of surgery involving the left breast. Physical examination revealed infiltrative hyperpigmented scaly plaques and nodules located on the left breast and pretibial regions bilaterally. A negative systemic workup excluded organ involvement. A clinical diagnosis of cutaneous sarcoidosis was made. Prior treatments included triamcinolone 0.1% topical cream and clobetasol 0.05% ointment, which failed to show improvement. Full-body narrow-band UVB (NBUVB) treatment was performed on a tri-weekly basis for eight months. NBUVB dosage was slowly titrated from 300 mJ/cm2 to a final dose of 1800 mJ/cm2 to prevent discomfort and burning sensations. Throughout the duration of her treatment, the patient adhered to a regimen of clobetasol 0.05% topical ointment applied twice daily in two-week intervals. Improvement was noticed after two months, with continued improvement up to eight months. The patient is continuing NBUVB phototherapy treatments for maintenance. In our case, NBUVB phototherapy treatment demonstrated promising results with improvement after two months of treatment. Physicians should consider NBUVB phototherapy as an effective option for patients presenting with cutaneous sarcoidosis.

Keywords: dermatology, sarcoidosis, phototherapy, ultraviolet

Procedia PDF Downloads 74

Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

Procedia PDF Downloads 82

Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Nureni Asafe Yekini, Olawale Nasiru Lawal, Bola Dada, Gabriel Adeyemi Okunlola

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This study is focused on the strengths and challenges associated with using the online social network as a rapidly evolving medium in marketing tourism services and businesses among the youths in Nigeria. The paper examines the Nigerian tourists’ attitude, mainly towards three aspects: application of Internet for travel and tourism; usage of online social networks in sharing travel and tourism experiences; and trust in electronic-media for marketing tourism businesses and services. The aim of this research is to determine the level of application of internet tools in marketing tourism businesses and services in Nigeria. This study reports an empirical analysis based on data obtained from a survey among 1004 Nigerian tourists. The outcome confirms the research hypothesis and points to crucial importance of introducing online social network site for marketing tourism businesses and services in Nigeria, and increasing the awareness for Nigeria as a tourist destination. Moreover, the paper strongly recommends the use of online social network as a tool for marketing tourism businesses and services, and the need for identifying effective framework for application of ICT tools in marketing tourism businesses and services in Nigeria at large.

Keywords: tourism business, internet, online social networks, tourism services, ICT

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

Procedia PDF Downloads 90

Authors: Shin-Pin Tseng

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Nowadays, numerous spectral amplitude coding (SAC) fiber-optic code-division-multiple-access (FO-CDMA) techniques were appealing due to their capable of providing moderate security and relieving the effects of multiuser interference (MUI). Nonetheless, the performance of the previous network is degraded due to fixed in-phase cross-correlation (IPCC) value. Based on the above problems, a new SAC FO-CDMA network using partial M-sequence (PMS) code is presented in this study. Because the proposed PMS code is originated from M-sequence code, the system using the PMS code could effectively suppress the effects of MUI. In addition, two-code keying (TCK) scheme can applied in the proposed SAC FO-CDMA network and enhance the whole network performance. According to the consideration of system flexibility, simple optical encoders/decoders (codecs) using fiber Bragg gratings (FBGs) were also developed. First, we constructed a diagram of the SAC FO-CDMA network, including (N/2-1) optical transmitters, (N/2-1) optical receivers, and one N×N star coupler for broadcasting transmitted optical signals to arrive at the input port of each optical receiver. Note that the parameter N for the PMS code was the code length. In addition, the proposed SAC network was using superluminescent diodes (SLDs) as light sources, which then can save a lot of system cost compared with the other FO-CDMA methods. For the design of each optical transmitter, it is composed of an SLD, one optical switch, and two optical encoders according to assigned PMS codewords. On the other hand, each optical receivers includes a 1 × 2 splitter, two optical decoders, and one balanced photodiode for mitigating the effect of MUI. In order to simplify the next analysis, the some assumptions were used. First, the unipolarized SLD has flat power spectral density (PSD). Second, the received optical power at the input port of each optical receiver is the same. Third, all photodiodes in the proposed network have the same electrical properties. Fourth, transmitting '1' and '0' has an equal probability. Subsequently, by taking the factors of phase‐induced intensity noise (PIIN) and thermal noise, the corresponding performance was displayed and compared with the performance of the previous SAC FO-CDMA networks. From the numerical result, it shows that the proposed network improved about 25% performance than that using other codes at BER=10-9. This is because the effect of PIIN was effectively mitigated and the received power was enhanced by two times. As a result, the SAC FO-CDMA network using PMS codes has an opportunity to apply in applications of the next-generation optical network.

Keywords: spectral amplitude coding, SAC, fiber-optic code-division multiple-access, FO-CDMA, partial M-sequence, PMS code, fiber Bragg grating, FBG

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

Procedia PDF Downloads 178

Authors: Raed Hanandeh

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This empirical study examines how knowledge sharing behaviors mediate the effect Technology-driven strategy on virtual organization success in Jordanian tourism sector firms. The results reveal that Social network ties are positively related to web knowledge seeking, web knowledge contributing and interactive system, but negatively related to accidental knowledge leakage. Furthermore, all types of knowledge sharing behavior are positively related to virtual organization success. Data collected from 23 firms. The total number of questionnaires mailed, 250 questionnaires were delivered. 214 were considered valid out of 241 Responses were received. The findings provide evidence that knowledge sharing behavior play a mediating role between Social network ties and virtual organization success and show that, web knowledge seeking, web knowledge contributing and interactive system playing an important impact on virtual organization success through knowledge sharing behaviors.

Keywords: social network ties, virtual organization success, knowledge sharing behaviors, web knowledge

Procedia PDF Downloads 274

Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang

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Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.

Keywords: creativity, default mode network, neural activation, SCAMPER

Procedia PDF Downloads 101

Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

Procedia PDF Downloads 109

Authors: Ghulam Mustafa Halepota, Zaib Dahar

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Background: Low acceptance of FP services, particularly in hard to reach areas where geographic, economic, or social barriers limit-service uptake. Moreover, limited resources appeared to be a reflection of dismal contraceptive use in Pakistan. People’s Primary Health Care Initiative (PPHI) is a Public Private Partnership Program of Government of Sindh which aims to improve maternal child health through accessible family planning services in far flung areas. In 2015 PPHI launched special family planning camps to have achieved a rapid improvement in CPR. On quarterly basis, these camps focus on Long Acting Reversible Contraceptives (LARC). These camps are arranged at 250 BHU Plus (24/7 MCHCs). The Organization manages 1140 primary health care facilities all over Sindh province and focuses on maternal, newborn and child health which includes antenatal care, labor/delivery, postnatal care, family planning, immunization, nutrition, BEmONC, CEmONC, diagnostic laboratories, ambulance services. Under the FPRH program, the organization launched special family planning camps in far flung areas to achieve a rapid improvement in CPR-committed to FP 2020 goal. Objective: To assess the performance of special FP camps for the improvement of long acting reversible contraceptive in hard to reach areas. Methodology: Outreach camps are organized on quarterly basis in 250 BHUs and maternal and child health centers (available-24/7). Using observational study design, the study reports 2 years data of special FP camps conducted in 23 various districts of Sindh during April 2015-April 2017. These special camps served a range of modern contraceptive methods including IUCDs, implants, condoms, pills, and injections. Moreover, 125 male medical officers are trained across Sindh in LARC and 554 female have been trained in implants and IUCD insertions. MSI Impact calculator was used to determine health and demographic impact of services. Results: This intervention has brought exceptional results, and the response has been overwhelming in time. Total 2048 special camps during 2015 till April 2017 have been carried out. 231796 MWRAs visited camps 91% opted modern FP, of which 45% opted Implants, 6% selected IUCDs from LARC (long term reversible contraceptive) from short term, 17% opted injectable 18% choose pills, and 12% used condoms. This intervention created a high contraceptive impact in rural Sindh an estimated 125048 FP users have been created, of this 111846 LARC users and 13498 are SARC users, through this intervention an estimated 55774 unintended pregnancies, 36299 live births, 9394, 80 maternal deaths, 926 and 6077 unsafe abortion have been averted. Moreover, the intervention created an economic impact and saved 2,409,563 direct health expenditure on each woman with reproductive age. Conclusion: Special FP Camps along with routine services is an effective and acceptable model for increase in provision of long-acting and permanent methods in hard to reach areas. This innovative approach by PHHI-Sindh has also been adopted in other provinces of Pakistan.

Keywords: inequalities, special camps, family planning services, hard to reach areas

Procedia PDF Downloads 187

Authors: Mahdi Jalili

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Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.

Keywords: EEG, brain, functional networks, network science, graph theory

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Authors: Hossain Samani, Mohammad Ehteram

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In this paper, we consider finite volume method (FVM) in water hammer. We will simulate these techniques on a looped network with complex boundary conditions. After comparing methods, we see the FVM method as the best method. We compare the results of FVM with experimental data. Finite volume using staggered grid is applied for solving water hammer equations.

Keywords: hydraulic transient, water hammer, interpolation, non-liner interpolation

Procedia PDF Downloads 350

Authors: Diogo Prosdocimi, Don Ross, Matthew Townshend

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Road authorities are mandated within limited budgets to both deliver improved access to basic services and facilitate economic growth. This responsibility is further complicated if maintenance backlogs and funding shortfalls exist, as evident in many countries including South Africa. These conditions require authorities to make difficult prioritisation decisions, with the effect that Road Asset Management Systems with a one-dimensional focus on traffic volumes may overlook the maintenance of low-volume roads that provide isolated communities with vital access to basic services. Given these challenges, this paper overlays the full South African road network with geo-referenced information for population, primary and secondary schools, and healthcare facilities to identify the network of connective roads between communities and basic service centres. This connective network is then rationalised according to the Gross Value Added and number of jobs per mesozone, administrative and functional road classifications, speed limit, and road length, location, and name to estimate the Basic Access Road Network. A two-step floating catchment area (2SFCA) method, capturing a weighted assessment of drive-time to service centres and the ratio of people within a catchment area to teachers and healthcare workers, is subsequently applied to generate a Multivariate Road Index. This Index is used to assign higher maintenance priority to roads within the Basic Access Road Network that provide more people with better access to services. The relatively limited incidence of Basic Access Roads indicates that authorities could maintain the entire estimated network without exhausting the available road budget before practical economic considerations get any purchase. Despite this fact, a final case study modelling exercise is performed for the Namakwa District Municipality to demonstrate the extent to which optimal relocation of schools and healthcare facilities could minimise the Basic Access Road Network and thereby release budget for investment in roads that best promote GDP growth.

Keywords: basic access roads, multivariate road index, road prioritisation, two-step floating catchment area method

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Authors: S. S. Dol

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Improvement in vehicle efficiency can reduce global fossil fuels consumptions. For that sole reason, Shell Global Corporation introduces Shell Eco-marathon where student teams require to design, build and test energy-efficient vehicles. Hence, this paper will focus on design processes and the development of a fuel economic vehicle which satisfying the requirements of the competition. In this project, three components are designed and analyzed, which are the body, chassis and powertrain of the vehicle. Optimum design for each component is produced through simulation analysis and theoretical calculation in which improvement is made as the project progresses.

Keywords: energy efficient, drag force, chassis, powertrain

Procedia PDF Downloads 338

Authors: H. S. Haroyan, V. R. Tadevosyan

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High gain broadband plasmonic slot nano-antenna has been considered. The theory of plasmonic slot nano-antenna (PSNA) has been developed. The analytical model takes into account also the electrical field inside the metal due to imperfectness of metal in optical range, as well as numerical investigation based on FEM method has been realized. It should be mentioned that Yagi-Uda configuration improves directivity in the plane of structure. In contrast, in this paper the possibility of directivity improvement of proposed PSNA in perpendicular plane of structure by using reflection metallic surface placed under the slot in fixed distance has been demonstrated. It is well known that a directivity improvement brings to the antenna gain increasing. This method of diagram improving is also well known from RF antenna design theory. Moreover the improvement of directivity in the perpendicular plane gives more flexibility in such application as improving the light and atom, ion, molecule interactions by using such type of plasmonic slot antenna. By the analogy of dipole type optical antennas the widening of working wavelengths has been realized by using bowtie geometry of slots, which made the antenna broadband.

Keywords: broadband antenna, high gain, slot nano-antenna, plasmonics.

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Authors: Xiaoqiang Fu, Baihao Li

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In 1895, the failure of Sino-Japanese prompted the trend of comprehensive and systematic study of western pattern in China. In urban planning and construction, urban reform movement sprang up slowly, which aimed at renovating and reconstructing the traditional cities into modern cities similar to the concessions. During the movement, Chinese traditional city initiated a process of modern urban planning for its modernization. Meanwhile, the traditional planning morphology and system started to disintegrate, on the contrary, western form and technology had become the paradigm. Therefore, the improvement of existing cities had become the prototype of urban planning of early modern China. Currently, researches of the movement mainly concentrate on large cities, concessions, railway hub cities and some special cities resembling those. However, the systematic research about the large number of traditional small and medium-sized cities is still blank, up to now. This paper takes the improvement constructions of small and medium-sized cities in Southern region of Jiangsu Province as the research object. First of all, the criteria of small and medium-sized cities are based on the administrative levels of general office and cities at the county level. Secondly, the suitability of taking the Southern Jiangsu as the research object. The southern area of Jiangsu province called Southern Jiangsu for short, was the most economically developed region in Jiangsu, and also one of the most economically developed and the highest urbanization regions in China. As the most developed agricultural areas in ancient China, Southern Jiangsu formed a large number of traditional small and medium-sized cities. In early modern times, with the help of the Shanghai economic radiation, geographical advantage and powerful economic foundation, Southern Jiangsu became an important birthplace of Chinese national industry. Furthermore, the strong business atmosphere promoted the widespread urban improvement practices, which were incomparable of other regions. Meanwhile, the demonstration of Shanghai, Zhenjiang, Suzhou and other port cities became the improvement pattern of small and medium-sized city in Southern Jiangsu. This paper analyzes the reform movement of the small and medium-sized cities in Southern Jiangsu (1895-1927), including the subjects, objects, laws, technologies and the influence factors of politic and society, etc. At last, this paper reveals the formation mechanism and characteristics of urban improvement movement in early modern China. According to the paper, the improvement of small-medium city was a kind of gestation of the local city planning culture in early modern China，with a fusion of introduction and endophytism.

Keywords: early modern China, improvement of small-medium city, southern region of Jiangsu province, urban planning history of China

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Authors: Gali Pesin, Yuliya Lipshits-Braziler, Sima Amram-Vaknin, Moshe Tatar

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The present study introduces four different patterns of seeking and providing help online among adolescents: (I) ‘Transceivers’ - adolescents who both seek as well as provide help online; (II) ‘Receivers’ - adolescents who seek help online, yet don’t provide it; (III) ‘Transmitters’ - adolescents who provide help online, yet don’t seek it; and (IV) ‘Idles’ - adolescents who refrain from seeking and providing help online. The study examined differences in seeking and providing help online between possible combinations of the four patterns, as well as gender differences within each pattern. Data was collected from 528 adolescents in Israel (59% were girls). Findings revealed that Transceivers are the largest group (45%) in this study, with higher representation of girls (65%). These adolescents seek help mainly around social difficulties, and they turn to peers who are both known and unknown to them. In addition, their preferred way to seek and provide help is through social network sites. Moreover, they often accept and give others emotional support. Receivers are the smallest group (5%) in this study. They turn to both known and unknown professionals more often than to friends and family. In addition, they seek help mostly around health and nutrition issues, and they usually receive instrumental support. For Receivers, the most important factor for seeking help online is anonymity, and the least important factor is familiarity with the help giver. Transmitters represent 16% of the adolescents in this study, with a greater representation of boys (52%). Their main reason to refrain from seeking help online is self-reliance. Nevertheless, these adolescents provide help to others online, mainly to those known to them through posting or responding to posts on social network sites. Idles represent 34% of the adolescents in this study. They refrain from seeking help online mainly due to their preference to seek help face to face, and due to their lack of trust in the internet or those using it. Idles and Transmitters are willing to seek help online mostly from friends and family. In addition, they are willing seek help online mainly regarding questions concerning military or civil service. They consider the most important facilitators for seeking help online as confidentiality and reliability. The present study’s main contribution is exploring the role of providing online help in understanding the adolescent behavior of seeking help online. In addition, the results of the present study have practical implications for the work of mental health providers, such as counseling psychologists and online mental health support.

Keywords: adolescents, counseling, online help-seeking, online help-providing

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Authors: Faiz Ul Haque Zeya

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In this paper, Fapitow’s bidding strategy and approach to participate in Travel Agent Competition (TAC) is described. Previously, Fapitow is designed using the agents provided by the TAC Team and mainly used their modification for developing our strategy. But later, by observing the behavior of the agent, it is decided to come up with strategies that will be the main cause of improved utilities of the agent, and by theoretical examination, it is evident that the strategies will provide a significant improvement in performance which is later proved by agent’s performance in the games. The techniques and strategies for further possible improvement are also described. TAC provides a real-time, uncertain environment for learning, experimenting, and implementing various AI techniques. Some lessons learned about handling uncertain environments are also presented.

Keywords: agent, travel agent competition, bidding, TAC

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

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Authors: Seyed Mahdi Jameii

Abstract:

Underwater wireless sensor networks have been attracting the interest of many ‎researchers lately, and the past three decades have beheld the rapid progress of ‎underwater acoustic communication. One of the major problems in underwater wireless ‎sensor networks is how to transfer data from the moving node to the base stations and ‎choose the optimized route for data transmission. Secure routing in underwater ‎wireless sensor network (UWCNs) is necessary for packet delivery. Some routing ‎protocols are proposed for underwater wireless sensor networks. However, a few ‎researches have been done on secure routing in underwater sensor networks. In this ‎article, a secure routing protocol is provided to resist against wormhole and sybil ‎attacks. The results indicated acceptable performance in terms of increasing the packet ‎delivery ratio with regards to the attacks, increasing network lifetime by creating ‎balance in the network energy consumption, high detection rates against the attacks, ‎and low-end to end delay.‎

Keywords: attacks, routing, security, underwater wireless sensor networks

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Authors: Mariana Gomez Gomez, Maria Elena Sanchez Vergara

Abstract:

Many sustainable approaches to generate electric energy have emerged in the last few decades; one of them is through solar cells. Yet, this also has the disadvantage of highly polluting inorganic semiconductor manufacturing processes. Therefore, the use of molecular semiconductors must be considered. In this work, allene compounds C24H26O4 and C24H26O5 were used as dopants to manufacture semiconductors films based on PbPc by high-vacuum evaporation technique. IR spectroscopy was carried out to determine the phase and any significant chemical changes which may occur during the thermal evaporation. According to UV-visible spectroscopy and Tauc’s model, the deposition process generated thin films with an activation energy range of 1.47 to 1.55 eV for direct transitions and 1.29 to 1.33 eV for indirect transitions. These values place the manufactured films within the range of low bandgap semiconductors. The flexible devices were manufactured: polyethylene terephthalate (PET), Indium tin oxide (ITO)/organic semiconductor/ Cubic Close Packed (CCP). The characterization of the devices was carried out by evaluating electrical conductivity using the four-probe collinear method. I-V curves were obtained under different lighting conditions at room temperature. OS1 (PbPc/C24H26O4) showed an Ohmic behavior, while OS2 (PbPc/C24H26O5) reached higher current values ​​at lower voltages. The results obtained show that the semiconductors devices doped with allene compounds can be used in the manufacture of optoelectronic devices.

Keywords: electrical properties, optical gap, phthalocyanine, thin film.

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