Search results for: the health improvement network (THIN)

Authors: Samia Moulebhar, Chahrazed Bendenia, Souhila Bendenia, Hanaa Merad-dib, Sarra Merabet, Sid Ahmed Khantar, Baghdad Hadri

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The photovoltaic solar field is today experiencing exponential advancement with the exploitation of new technological sectors of nanoparticles, namely the field of solar cells based on organic polymer materials. These cells are flexible, easy to process and low cost. This work includes a presentation of the conjugated polymer materials used in the design of photovoltaic technology devices while determining their properties and then the models used for the modeling of thin film photovoltaic cells heterojunction.

Keywords: photovoltaic, cells, nanoparticles, organic

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

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Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

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Authors: Daham Owaid Matrood, Naqaa Hussein Raheem

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Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

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Authors: Alvaro Quino, Roger Trejo, Gary Duran, Jordy Viso

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This article presents a study on the lightening and improvement of properties of soil extracted in the province of Talara in the department of Piura -Peru, to be used in filling in the construction of embankments for roads. This soft soil has a high percentage of elastic settlement and consolidation settlement. Currently, there are different methods that seek to mitigate the impact of this problem, which have achieved favorable results. As a contribution to these investigations, we propose the use of two lightening materials to be used in the filling of embankments; these materials are expanded polystyrene beads (EPS) and rice husk ash (RHA). Favorable results were obtained, such as a reduction of 14.34% of the volumetric weight, so the settlement will be reduced. In addition, it is observed that as the RHA dosage increases, the shear resistance increases. In this article, soil mechanics tests were performed to determine the effectiveness of this method in lightening and improving properties for the soil under study.

Keywords: sandy clay soils, rice husk ash, expanded polystyrene, soft soils

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Authors: Liudmila Garzanova, Lidia Ananyeva, Olga Koneva, Olga Ovsyannikova, Oxana Desinova, Mayya Starovoytova, Rushana Shayahmetova, Anna Khelkovskaya-Sergeeva

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Objectives. There is very few data on changes of the musculoskeletal manifestations (artritis, arthralgia, muscle weakness, etc.) in systemic sclerosis (SSc) on rituximab (RTX) therapy. The aim of our study was to assess the severity of the musculoskeletal involvement in SSc patients (pts) and its changes during RTX therapy. Methods. Our study included 103 pts with SSc. The mean followup period was 12.6±10.7 months. The mean age was 47±12.9 years, female-87 pts (84%), the diffuse cutaneous subset of the disease had 55 pts (53%). The mean disease duration was 6.2±5.5 years. All pts had interstitial lung disease (ILD) and were positive for ANA, 67% of them were positive for antitopoisomerase-1. All patients received prednisolone at a dose of 11.3±4.5 mg/day, immunosuppressants at inclusion received 47% of them. Pts received RTX due to the ineffectiveness of previous therapy for ILD. The cumulative mean dose of RTX was 1.7±0.6 grams. Arthritis was observed in 22 pts (21%), arthralgias in 47 pts (46%). Muscle weakness was observed in 17 pts (17%). Tendon friction rubs was established in 7 pts (7%). The results at baseline and at the end of the follow up are presented in the form of mean values. Results. There was an improvement of all outcome parameters and musculoskeletal manifestations on RTX therapy. There was a decrease in the number of pts with arthritis from 22 (21%) to 10 (9%), a decrease in the number of pts with arthralgias from 47 (46%) to 31 (30%). The number of pts with muscle weakness decreased from 17 (17%) to 7 (7%). The number of pts with tendon friction rubs decreased from 7 (7%) to 3 (3%). The creatine phosphokinase decreased from 365.5±186 to 70.8±50.4 (p=0.00006). The C-reactive protein (CRP) decreased from 23.2±31.3 to 8.62±7.4 (p=0.001). The dose of prednisolone was reduced from 11.3±4.5 to 9.8±3.5 mg/day (p=0.0004). Conclusion. In our study, musculoskeletal involvement was detected in almost half of the patients with SSc-ILD. There was an improvement of musculoskeletal manifestations despite a small cumulative dose of RTX. We also managed to reduce the dose of glucocorticosteroids. The improvement of musculoskeletal manifestations was accompanied by a decrease in laboratory parameters - creatine phosphokinase and CRP. RTX is effective option for treatment of musculoskeletal manifestations in SSc.

Keywords: arthritis, musculoskeletal involvement, systemic sclerosis, rituximab

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Authors: Mario Robinson, Moe Rabea

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This research examines the effects of heat treatment processes on the mechanical properties and corrosion resistanceof1045 and 4140 Steel Alloysfor industrial applications. Heat treatment processes of full annealing, normalizing, quenching, and tempering are carried out on the alloy samples. The mechanical and corrosion resistance tests of the heat treated samples are carried out, and the results obtained are related to their SEMmorphologies analysis. The results show that the heat treatment processes have an effect on the tensile strength, impact, and a significant effect on the corrosion resistance of the alloy samples. With respect to the strain characteristics, significant improvement in the ductility of the samples is recorded in the full annealing and alloy tempered samples. Thus, for application requiring strength and ductility, such as in aerospace industries, this tempered heat treated alloy could be used. In addition, the quenched sample shows a significant improvement in hardness.

Keywords: heat treatment, corrosion resistance, steel, industrial appilcations

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Authors: Rabia Mushtaq, Uroosa Javaid

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It has been observed that health professionals are at higher danger of stress in light of the fact that being a specialist is physically and emotionally demanding. The study aimed to investigate the relationship between workplace stress and mental wellbeing among health professionals. Sample of 120 male and female health professionals belonging to two age groups, i.e., early adulthood and middle adulthood, was employed through purposive sampling technique. Job stress scale, mindful attention awareness scale, and Warwick Edinburgh mental wellbeing scales were used for the measurement of study variables. Results of the study indicated that job stress has a significant negative relationship with mental wellbeing among health professionals. The current study opened the door for more exploratory work on mindfulness among health professionals. Yielding outcomes helped in consolidating adapting procedures among workers to improve their mental wellbeing and lessen the job stress.

Keywords: health professionals, job stress, mental wellbeing, mindfulness

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Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany

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In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.

Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities

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Authors: Ibrahim Ozkan

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In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.

Keywords: cooperative network, adaptive modulation and coding, hybrid ARQ, correlated fading

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Authors: Jiri F. Urbanek, Jiri Barta, Oldrich Svoboda, Jiri J. Urbanek

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The organizations of European and Czech critical infrastructure have specific position, mission, characteristics and behaviour in European Union and Czech state/ business environments, regarding specific requirements for regional and global security environments. They must respect policy of national security and global rules, requirements and standards in all their inherent and outer processes of supply-customer chains and networks. A controlling is generalized capability to have control over situational policy. This paper aims and purposes are to introduce the controlling as quite new necessary process attribute providing for critical infrastructure is environment the capability and profit to achieve its commitment regarding to the effectiveness of the quality management system in meeting customer/ user requirements and also the continual improvement of critical infrastructure organization’s processes overall performance and efficiency, as well as its societal security via continual planning improvement via DYVELOP modelling.

Keywords: added value, DYVELOP, controlling, environments, process approach

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Authors: Zofia Hammerin, Goran Basic, Disa Bergnehr

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Ever since the Ottawa charter in 1986, health promotion through schools has been stressed across the globe. Both in the global and national discourse, schools are made responsible not only for providing education but also for working with pupil health and well-being. In Sweden, where the study is set, it is emphasized in national directives that promoting pupil health should be part of the school practice. Since the Swedish school system is decentralized, these directives need to be interpreted and recontextualized locally. This study aims to explore how the student comes across in Swedish local health policy documents. The data consists of 37 such documents called student health plans collected from different high schools throughout Sweden. The analysis was inspired by critical discourse analysis, and tentative results are divided into two main themes; the invisible actor and the passive recipient. The pupil is largely invisible in the documents, and the discourse instead focuses on school health service staff and, to some extent, the teachers. When the pupils are visible, they mainly come across as passive recipients of health promoting actions. Since participation, taking action, and feeling empowered are key aspects of health promotion, the findings could impact the pupils’ possibilities for health and well-being.

Keywords: health promotion, high school, student, sweden

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Authors: Berger Saintius, Edna Ariste, Djeamsly Salomon

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Background: Maternal mortality is a preventable global health problem that affects developed, developing, and underdeveloped countries alike. Globally, maternal mortality rates have declined since 1990, but 830 women die every day from pregnancy and childbirth-related causes that are often preventable. Haiti, with a number of 529 maternal deaths per 100,000 live births, is one of the countries with the highest maternal mortality rate in the Caribbean. This study consists of analyzing maternal death surveillance data in Haiti from 2017-2020. Method : A descriptive study was conducted; data were extracted from the National Epidemiological Surveillance Network of maternal deaths from 2017 to 2020. Sociodemographic variables were analyzed. Excel and Epi Info 7.2 were used for data analysis. Frequency and proportion measurements were calculated. Results: 756 deaths were recorded for the study period: 42 (6%) in 2017, 168 (22%) in 2018, 265 (35%) in 2019, and 281 (37%) in 2020. The North Department recorded the highest number of deaths, 167 (22%). 83(11%) in Les Cayes. 96% of these deaths are people aged between 15 and 49. Conclusion. Maternal mortality is a major health problem in Haiti. Mobilization, participation, and involvement of communities, increase in obstetric care coverage and promotion of Family Planning are among the strategies to fight this problem.

Keywords: epidemiology, maternal death, hospital, Haiti

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Pierre-Andre Mudry, Jean Decaix, Jeremy Schmid, Cesar Papilloud, Cecile Munch-Alligne

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In most of the Swiss Alpine regions, the availability of water resources is usually adequate even in times of drought, as evidenced by the 2003 and 2018 summers. Indeed, important natural stocks are for the moment available in the form of snow and ice, but the situation is likely to change in the future due to global and regional climate change. In addition, alpine mountain regions are areas where climate change will be felt very rapidly and with high intensity. For instance, the ice regime of these regions has already been affected in recent years with a modification of the monthly availability and extreme events of precipitations. The current research, focusing on the municipality of Val de Bagnes, located in the canton of Valais, Switzerland, is part of a project led by the Altis company and achieved in collaboration with WSL, BlueArk Entremont, and HES-SO Valais-Wallis. In this region, water occupies a key position notably for winter and summer tourism. Thus, multiple actors want to apprehend the future needs and availabilities of water, on both the 2050 and 2100 horizons, in order to plan the modifications to the water supply and distribution networks. For those changes to be salient and efficient, a good knowledge of the current water distribution networks is of most importance. In the current case, the water drinking network is well documented, but this is not the case for the irrigation one. Since the water consumption for irrigation is ten times higher than for drinking water, data acquisition on the irrigation network is a major point to determine future scenarios. This paper first presents the instrumentation and simulation of the irrigation network using custom-designed IoT devices, which are coupled with a digital clone simulated to reduce the number of measuring locations. The developed IoT ad-hoc devices are energy-autonomous and can measure flows and pressures using industrial sensors such as calorimetric water flow meters. Measurements are periodically transmitted using the LoRaWAN protocol over a dedicated infrastructure deployed in the municipality. The gathered values can then be visualized in real-time on a dashboard, which also provides historical data for analysis. In a second phase, a digital clone of the irrigation network was modeled using EPANET, a software for water distribution systems that performs extended-period simulations of flows and pressures in pressurized networks composed of reservoirs, pipes, junctions, and sinks. As a preliminary work, only a part of the irrigation network was modelled and validated by comparisons with the measurements. The simulations are carried out by imposing the consumption of water at several locations. The validation is performed by comparing the simulated pressures are different nodes with the measured ones. An accuracy of +/- 15% is observed on most of the nodes, which is acceptable for the operator of the network and demonstrates the validity of the approach. Future steps will focus on the deployment of the measurement devices on the whole network and the complete modelling of the network. Then, scenarios of future consumption will be investigated. Acknowledgment— The authors would like to thank the Swiss Federal Office for Environment (FOEN), the Swiss Federal Office for Agriculture (OFAG) for their financial supports, and ALTIS for the technical support, this project being part of the Swiss Pilot program 'Adaptation aux changements climatiques'.

Keywords: hydraulic digital clone, IoT water monitoring, LoRaWAN water measurements, EPANET, irrigation network

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Authors: Zekang Lan, Yan Xu, Yingkun Huang, Dian Huang, Shengzhong Feng

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Jobs on high-performance computing (HPC) clusters can suffer significant performance degradation due to inter-job network interference. Topology-aware job allocation problem (TJAP) is such a problem that decides how to dedicate nodes to specific applications to mitigate inter-job network interference. In this paper, we study the window-based TJAP on a fat-tree network aiming at minimizing the cost of communication hop, a defined inter-job interference metric. The window-based approach for scheduling repeats periodically, taking the jobs in the queue and solving an assignment problem that maps jobs to the available nodes. Two special allocation strategies are considered, i.e., static continuity assignment strategy (SCAS) and dynamic continuity assignment strategy (DCAS). For the SCAS, a 0-1 integer programming is developed. For the DCAS, an approach called neural simulated algorithm (NSA), which is an extension to simulated algorithm (SA) that learns a repair operator and employs them in a guided heuristic search, is proposed. The efficacy of NSA is demonstrated with a computational study against SA and SCIP. The results of numerical experiments indicate that both the model and algorithm proposed in this paper are effective.

Keywords: high-performance computing, job allocation, neural simulated annealing, topology-aware

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Authors: Lamiaa Shehata

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The current (COVID-19) pandemic is one of the international crises, and a lot of efforts have been directed toward the improvement of efficient vaccines, however vaccine hesitancy is one of the universal menaces that make the fulfillment of society immunity very hard. The World Health Organization acknowledges vaccine hesitancy as the society’s maximum risk to people's health protection, especially in little and moderate-revenue nations. Social media is strong in observing audience behaviors and evaluating the circulation, which would supply useful data for strategy makers. It has a significant function in spreading facts during the pandemic, it could assist to boost protective manners. The objective of this study is to determine the effects of social media exposure on vaccine hesitancy. Data were collected using a survey in a form of a structured questionnaire conducted during December 2021- January 2022 using convenient sampling techniques (680) in Egypt and the United Arab Emirates. The results revealed that there was a significant relationship between the high exposure to social media and the refusal of the Covid19 vaccine also, the percentage of the refusal of the vaccine is higher in Egypt, however, UAE forced people to take the vaccine. Furthermore, public attitudes toward COVID-19 vaccination vary from gender and region. In conclusion, policymakers must adjust their policies through the use of social media to immediate actions to vaccine-related news to support vaccination approval.

Keywords: COVID-19, hesitancy, social media, vaccine

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Authors: Aditya Purohit, Neha Bansal

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Unplanned urbanization and multidirectional sprawl of the cities have resulted in increased motorization and deteriorating transport conditions like traffic congestion, longer commuting, pollution, increased carbon footprint, and above all increased fatalities. In order to overcome these problems, various practices have been adopted including– promoting and implementing mass transport; traffic junction channelization; smart transport etc. However, these methods are found to be primarily focusing on vehicular mobility rather than people accessibility. With this research gap, this paper tries to resolve the mobility issues for Ahmedabad city in India, which being the economic capital Gujarat state has a huge commuter and visitor inflow. This research aims to resolve the traffic congestion and urban mobility issues focusing on Gujarat State Regional Transport Corporation (GSRTC) for the city of Ahmadabad by analyzing the existing operations and network structure of GSRTC followed by finding possibilities of integrating it with other modes of urban transport. The network restructuring (NR) methodology is used with appropriate variations, based on commuter demand and growth pattern of the city. To do these ‘scenarios’ based on priority issues (using 12 parameters) and their best possible solution, are established after route network analysis for 2700 population sample of 20 traffic junctions/nodes across the city. Approximately 5% sample (of passenger inflow) at each node is considered using random stratified sampling technique two scenarios are – Scenario 1: Resolving mobility issues by use of Special Purpose Vehicle (SPV) in joint venture to GSRTC and Private Operators for establishing feeder service, which shall provide a transfer service for passenger for movement from inner city area to identified peripheral terminals; and Scenario 2: Augmenting existing mass transport services such as BRTS and AMTS for using them as feeder service to the identified peripheral terminals. Each of these has now been analyzed for the best suitability/feasibility in network restructuring. A desire-line diagram is constructed using this analysis which indicated that on an average 62% of designated GSRTC routes are overlapping with mass transportation service routes of BRTS and AMTS in the city. This has resulted in duplication of bus services causing traffic congestion especially in the Central Bus Station (CBS). Terminating GSRTC services on the periphery of the city is found to be the best restructuring network proposal. This limits the GSRTC buses at city fringe area and prevents them from entering into the city core areas. These end-terminals of GSRTC are integrated with BRTS and AMTS services which help in segregating intra-state and inter-state bus services. The research concludes that absence of integrated multimodal transport network resulted in complexity of transport access to the commuters. As a further scope of research comparing and understanding of value of access time in total travel time and its implication on generalized cost on trip and how it varies city wise may be taken up.

Keywords: mass transportation, multi-modal integration, network restructuring, travel behavior, urban transport

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Authors: Dinamarie Fonzone

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Employer-sponsored health insurance (ESI) strategic decision-making processes rely on financial analysis to guide leadership in choosing plans that will produce optimal organizational spending outcomes. These financial decision-making methods have not abated ESI costs. Previously unrecognized external social determinants, the impact on ESI plan spending, and other organizational strategies are emerging and are important considerations for organizational decision-makers and change management practitioners. The purpose of thisstudy is to examine the relationship between the social determinants of health (SDoH), employer-sponsored health insurance (ESI) plans, andthe unintended consequence of health inequity. A quantitative research design using selectemployee records from an existing employer human capital management database will be analyzed. Statistical regressionmethods will be used to study the relationships between certainSDoH (employee income, neighborhood geographic living area, and health care access) and health plan utilization, cost, and chronic disease prevalence. The discussion will include an application of the social gradient of health theory to the study findings, organizational transformation through changes in ESI decision-making mental models, and the connection of ESI health inequity to organizational development and changediversity, equity, and inclusion strategies.

Keywords: employer-sponsored health insurance, social determinants of health, health inequity, mental models, organizational development, organizational change, social gradient of health theory

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Authors: Maren J. Coffman, Victoria C. Scott, J. Claire Schuch, Ashley N. Kelley, Jeri L. Ryan

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Postpartum Depression (PPD) is a leading cause of postpartum morbidity. PPD affects 7.1% of postpartum women and 19.2% of postpartum women when including minor depression. Lower-income women and ethnic minorities are more at risk for developing PPD and face multiple attitudinal and institutional barriers to receiving care. This study aims to identify PPD among low-income women and connect them to appropriate services in order to reduce the illness burden and enhance access to care. Screenings were conducted in two Women, Infants, and Children (WIC) clinics in the city of Charlotte, North Carolina, USA, from April 2017 to April 2018. WIC is a supplemental nutrition program that provides healthcare and nutrition to low-income pregnant women, breastfeeding women, and children under the age of 5. Additionally, a qualitative study was conducted to better understand the PPD continuum of care in order to identify opportunities for improvement. Mothers with infants were screened for depression risk using the PHQ-2. Mothers who scored ≥ 2 completed two additional standardized screening tools (PHQ-7, to complete the PHQ-9, and the Edinburgh) to assess depressive symptomatology. If indicated they may be suffering from depression, women were referred for case management services. Open-ended questions were used to understand treatment barriers. Four weeks after the initial survey, a follow-up telephone call was made to see if women had received care. Seven focus groups with WIC staff and managers, referral agency staff, local behavioral health professionals, and students examining the screenings, are being conducted March - April, 2018 to gather information related to current screening practices, referrals, follow up and treatment. Mothers (n = 231 as of February, 2018) were screened in English (65%) or Spanish (35%). According to preliminary results, 29% of mothers screened were at risk for postpartum depression (PHQ-2 ≥ 2). There were significant differences in preliminary screening results based on survey language (

Keywords: health disparities, maternal health, mental health, postpartum depression

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Authors: Johana Jaramillo, Robin Kalfat, Dmitriy A. Dikin

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The cement production process is one of the major sources of carbon dioxide (CO₂), a potent greenhouse gas. Indeed, as a result of its cement manufacturing process, concrete contributes approximately 8% of global greenhouse gas emissions. In addition to environmental concerns, concrete also has a low tensile and ductility strength, which can lead to cracks. Graphene nanoplatelets (GNPs) have proven to be an eco-friendly solution for improving the mechanical and durability properties of concrete. The current research investigates the effects of preparing concrete enhanced with GNPs by using different wet dispersions techniques and mixing methods on its mechanical properties. Concrete specimens were prepared with 0.00 wt%, 0.10 wt%, 0.20 wt%, 0.30 wt% and wt% GNPs. Compressive and flexural strength of concrete at age 7 days were determined. The results showed that the maximum improvement in mechanical properties was observed when GNPs content was 0.20 wt%. The compressive and flexural were improved by up to 17.5% and 8.6%, respectively. When GNP dispersions were prepared by the combination of a drill and an ultrasonic probe, mechanical properties experienced maximum improvement.

Keywords: concrete, dispersion techniques, graphene nanoplatelets, mechanical properties, mixing methods

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Authors: Ahmad Saadiq, Neeraj Sahu

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Sediment, which comprises of solid particles of mineral and organic material are transported by water. In river systems, the amount of sediment transported is controlled by both the transport capacity of the flow and the supply of sediment. The transport of sediment in rivers is important with respect to pollution, channel navigability, reservoir ageing, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. The sediment load transported in a river is a very complex hydrological phenomenon. Hence, sediment transport has attracted the attention of engineers from various aspects, and different methods have been used for its estimation. So, several experimental equations have been submitted by experts. Though the results of these methods have considerable differences with each other and with experimental observations, because the sediment measures have some limits, these equations can be used in estimating sediment load. In this present study, two black box models namely, an SRC (Sediment Rating Curve) and ANN (Artificial Neural Network) are used in the simulation of the suspended sediment load. The study is carried out for Seonath subbasin. Seonath is the biggest tributary of Mahanadi river, and it carries a vast amount of sediment. The data is collected for Jondhra hydrological observation station from India-WRIS (Water Resources Information System) and IMD (Indian Meteorological Department). These data include the discharge, sediment concentration and rainfall for 10 years. In this study, sediment load is estimated from the input parameters (discharge, rainfall, and past sediment) in various combination of simulations. A sediment rating curve used the water discharge to estimate the sediment concentration. This estimated sediment concentration is converted to sediment load. Likewise, for the application of these data in ANN, they are normalised first and then fed in various combinations to yield the sediment load. RMSE (root mean square error) and R² (coefficient of determination) between the observed load and the estimated load are used as evaluating criteria. For an ideal model, RMSE is zero and R² is 1. However, as the models used in this study are black box models, they don’t carry the exact representation of the factors which causes sedimentation. Hence, a model which gives the lowest RMSE and highest R² is the best model in this study. The lowest values of RMSE (based on normalised data) for sediment rating curve, feed forward back propagation, cascade forward back propagation and neural network fitting are 0.043425, 0.00679781, 0.0050089 and 0.0043727 respectively. The corresponding values of R² are 0.8258, 0.9941, 0.9968 and 0.9976. This implies that a neural network fitting model is superior to the other models used in this study. However, a drawback of neural network fitting is that it produces few negative estimates, which is not at all tolerable in the field of estimation of sediment load, and hence this model can’t be crowned as the best model among others, based on this study. A cascade forward back propagation produces results much closer to a neural network model and hence this model is the best model based on the present study.

Keywords: artificial neural network, Root mean squared error, sediment, sediment rating curve

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Authors: V. Samulionis, J. Banys, A. Sánchez-Ferrer

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Inorganic nanoparticles are used for fabrication of various composites based on polymer materials because they exhibit a good homogeneity and solubility of the composite material. Multifunctional materials based on composites of a polymer containing inorganic nanotubes are expected to have a great impact on industrial applications in the future. An emerging family of such composites are polyurea elastomers with inorganic MoS2 nanotubes or MoSI nanowires. Polyurea elastomers are a new kind of materials with higher performance than polyurethanes. The improvement of mechanical, chemical and thermal properties is due to the presence of hydrogen bonds between the urea motives which can be erased at high temperature softening the elastomeric network. Such materials are the combination of amorphous polymers above glass transition and crosslinkers which keep the chains into a single macromolecule. Polyurea exhibits a phase separated structure with rigid urea domains (hard domains) embedded in a matrix of flexible polymer chains (soft domains). The elastic properties of polyurea can be tuned over a broad range by varying the molecular weight of the components, the relative amount of hard and soft domains, and concentration of nanoparticles. Ultrasonic methods as non-destructive techniques can be used for elastomer composites characterization. In this manner, we have studied the temperature dependencies of the longitudinal ultrasonic velocity and ultrasonic attenuation of these new polyurea elastomers and composites with inorganic nanoparticles. It was shown that in these polyurea elastomers large ultrasonic attenuation peak and corresponding velocity dispersion exists at 10 MHz frequency below room temperature and this behaviour is related to glass transition Tg of the soft segments in the polymer matrix. The relaxation parameters and Tg depend on the segmental molecular weight of the polymer chains between crosslinking points, the nature of the crosslinkers in the network and content of MoS2 nanotubes or MoSI nanowires. The increase of ultrasonic velocity in composites modified by nanoparticles has been observed, showing the reinforcement of the elastomer. In semicrystalline polyurea elastomer matrices, above glass transition, the first order phase transition from quasi-crystalline to the amorphous state has been observed. In this case, the sharp ultrasonic velocity and attenuation anomalies were observed near the transition temperature TC. Ultrasonic attenuation maximum related to glass transition was reduced in quasicrystalline polyureas indicating less influence of soft domains below TC. The first order phase transition in semicrystalline polyurea elastomer samples has large temperature hysteresis (> 10 K). The impact of inorganic MoS2 nanotubes resulted in the decrease of the first order phase transition temperature in semicrystalline composites.

Keywords: inorganic nanotubes, polyurea elastomer composites, ultrasonic velocity, ultrasonic attenuation

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Authors: Wael Elkholy, Ashraf Sakr, Mahmoud Qandeel, Adam Elkholy

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Lumbar disc herniation is a common pathology that may cause significant low back pain and radicular pain that could profoundly impair daily life activities of individuals. Patients who undergo surgical treatment for lumbar disc herniation usually present with radiculopathy along with low back pain (LBP) instead of radiculopathy alone. When discectomy is performed, improvement in leg radiating pain is observed due to spinal nerve irritation. However, long-term LBP due to degenerative changes in the disc may occur postoperatively. In addition, limited research has been reported on the short-term (within 1 year) improvement in LBP after discectomy. In this study we would like to share our minimally invasive open technique for lumbar discectomy with annuloplasty and nuceloplasty as a technique for effective reduction of both axial and radicular pain.

Keywords: nucleoplasty, sinuvertebral nerve cauterization, annuloplasty, discogenic low back pain, axial pain, radicular pain, minimally invasive lumbar discectomy

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Authors: Pradapet Krutchangthong, Jirawat Sudsawart

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This research aims to study the health tourism administration and factors related to health tourism promotion at Ban Nam Chieo Community, Laem Ngop, Trat Province. The sample in this research is 361 tourists who use the service and Ban Nam Chieo Community residents who provide the service. Sampling was done from a population size of 3,780 using Taro Yamane’s formula. The tools used in the study were questionnaires and interviews. The statistics used in this research are percentage, mean and standard deviation. The result of Model Development of Health Tourism at Ban Nam Chieo Community, Laem Ngop , Trat Province shows that most of them are female with bachelor degree. They are government officers with an average income between 16,001-20,000 Baht. Suggested health system activities for health tourism development are: 1) health massage, 2) herbal compress, 3) exercise in the water by walking on shell. Meanwhile, factors related to health tourism promotion at Ban Nam Chieo Community, Laem Ngop, Trat Province are: 1) understanding the context of the community and service providers, 2) cooperation from related government and private sectors.

Keywords: health tourism, health system activities, promotion, administration

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Authors: U. K. Tamuli

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Introduction: North East India is graced with natural beauty and hazards. This area is prone to major earthquakes, floods, landslides, accidents, terrorist activities etc. Academy of Trauma (AOT), an NGO of Doctors, conducts training programs, mock drills, field trials amongst the doctors and paramedics in North East India. The present study is to evaluate the efficacy of such training in terms of sensitivity, awareness, and delivery systems of the products. Here the health care delivery system for disaster management is inadequate. Clear guideline of mass casualty management is unavailable. AOT has initiated steps to increase the awareness and handling of mass casualty management to improve the emergency health care delivery system. Method: AOT has conducted training programmes on emergency health management, mass casualty management and hospital preparedness amongst 800 doctors and 1200 paramedics in twenty-two districts of Assam in Northeast India. The training module consists of lectures, hands-on workshop using manikins, mock drills, distribution of manuals, emergency management exercises, periodic exchange of experience and debriefings. AOT evaluates the impact of these trainings by conducting pre and post tests of delegates, trainer’s evaluation, delegate’s satisfaction and confidence level and their suggestions. Results: The module, training, hands-on workshops, mock drills were highly appreciated. There is significant improvement in scores on the post-training tests. The confidence level of the participants has risen to deal with emergency medical situation Conclusion: These kinds of trainings increase the awareness of the medical members to handle mass casualties in different situations. One such training actually sensitises the delegates. Repetition of such training, TOT (Training-of-Trainers) programs, and individual efforts of delegates are extremely important for sustenance and success of health care delivery service during disasters in the developing countries. Further collaboration, assistance, networking, suggestions from established global agencies in this field will be highly appreciated.

Keywords: capacity building, North East India, non-governmental organization, trauma

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Authors: Yuan-Ching Lin, Sin-Yu Chen, Pei-Yu Wu

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This study reports the improvement in the wear performance of A6061 aluminum alloy clad with mixed powders of titanium carbide (TiC), copper (Cu) and aluminum (Al) using the gas tungsten arc welding (GTAW) method. The wear performance of the A6061 clad layers was evaluated by performing pin-on-disc mode wear test. Experimental results clearly indicate an enhancement in the hardness of the clad layer by about two times that of the A6061 substrate without cladding. Wear test demonstrated a significant improvement in the wear performance of the clad layer when compared with the A6061 substrate without cladding. Moreover, the interface between the clad layer and the A6061 substrate exhibited superior metallurgical bonding. Due to this bonding, the clad layer did not spall during the wear test; as such, massive wear loss was prevented. Additionally, massive oxidized particulate debris was generated on the worn surface during the wear test; this resulted in three-body abrasive wear and reduced the wear behavior of the clad surface.

Keywords: GTAW、A6061 aluminum alloy, 、surface modification, tribological study, TiC powder cladding

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Authors: Habib Gorine, Rabia Saleh

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Mobile Ad-Hoc Networks are the special type of wireless networks which share common security requirements with other networks such as confidentiality, integrity, authentication, and availability, which need to be addressed in order to secure data transfer through the network. Their routing protocols are vulnerable to various malicious attacks which could have a devastating consequence on data security. In this paper, three types of attacks such as selfish, gray hole, and black hole attacks have been applied to the two most important routing protocols in MANET named dynamic source routing and ad-hoc on demand distance vector in order to analyse and compare the impact of these attacks on the Network performance in terms of throughput, average delay, packet loss, and consumption of energy using NS2 simulator.

Keywords: MANET, wireless networks, routing protocols, malicious attacks, wireless networks simulation

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Authors: Khair Jadaan, Qasem Alqasem

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School children are vulnerable road user groups and are particularly at high risk calling for the need to improve their safety. Safe Routes to Schools (SRTS) concept is considered as one safety improvement approach that would effectively help improve school children’s safety. This paper aims to determine the best practice SRTS for Jordan based on the international experience attained through extensive and selected literature review falling under the 5 E’s and additionally on information/data collected through a survey performed using an online predesigned questionnaire to investigate the reactions and attitudes of students and their parents towards the proposed SRTS program. Data are analyzed using SPSS and MS software, especially Excel, in addition to some literature reviews inserted in this study. The results represent some recommendations that are strongly believed to help decision makers to develop the current safety conditions of the school routes. The challenges that the implementation of this program would face including COVID-19 protection for teachers and students are addressed.

Keywords: children, COVID-19, Jordan, safety, school, SRTS, 5 E’s

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Authors: Sathapath Kilaso

Abstract:

Thailand has a large range of geographic diversity. Thailand can be divided into 5 regions which are North Region, East Region, West Region, South Region and North-East Region which each region has a different geographic and climate. Especially in North Region, the geographic is mountain and intermontane plateau which will be a reason that the roads in the North Region have a lot of bends. So the driver in the North Region road will have to have a very high skill of driving. If the accident is occurred, the emergency rescue will have a hard time to reach the accident area and rescue the victim of the accident as the long distance and steep road. This article will apply the concept of the wireless sensor network with the micro-controller to alert the driver when the driver reaches the very dangerous bend.

Keywords: wireless sensor network, motion sensor, smart alert, dangerous bend

Procedia PDF Downloads 274