Search results for: scientific data mining

Authors: Mohammed Abdelsabour-Khalaf, F. Yusuf , B Brand-Saberi

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Purpose: Applications of nanotechnology nowadays extended to include a wide range of scientific areas such electron micrscopy and gene expression. The aim of the current study was to investigate the developmental expression pattern of genes involved in human glomerulo-nephropathies associated with massive proteinuria and podocyte differentiation using the chicken mesonephros as a model system. Method: We performed in situ hybridization using chicken specific mRNA probes for genes expressed in the early nephron and slit diaphragm genes. The probes used were cNeph1, cNeph2, cSim1, cLmx1b, and cAtoh8. Chicken embryos from Hamburger Hamilton developmental stage HH19 (E3) to HH 34 (E9) were used for the in situ hybridization (ISH). ISH was performed on whole mount embryos which were sectioned by vibratome. Results: Our result show that Neph1, Neph2, Sim1. Lmx1b and Atoh8 genes are dynamically expressed during nephron morphogenesis and Neph1 and Atoh8 are also specifically expressed in the podocytes during late stages of differentiation. Conclusion: We conclude from our results that the genes implicated in congenital and acquired glomerulo-nephropathies like Neph1 and Neph2 are dynamically expressed during mesonephros development pointing towards a role in the formation of the filtration barrier and the differentiation of the mesonephric podocytes. Thus the avian mesonephros could serve as a model to study human kidney diseases.

Keywords: mesonephros, chicken embryo, gene expression, immunohistochemistry

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Authors: Antonella Falanga, Armando Cartenì

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Today, an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of these kinds of data. This surge of information has revolutionized many sectors, such as business for improving decision-making processes, healthcare for clinical record analysis and medical research, education for enhancing teaching methodologies, agriculture for optimizing crop management, finance for risk assessment and fraud detection, media and entertainment for personalized content recommendations, emergency for a real-time response during crisis/events, and also mobility for the urban planning and for the design/management of public and private transport services. Big data's pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the one investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety and all the autonomous and connected vehicles applications. Benefits include a reduction in travel times, road accidents and pollutant emissions. Within these issues, the proper transport demand estimation is crucial for sustainable transportation planning. Evaluating the impact of sustainable mobility policies starts with a quantitative analysis of travel demand. Achieving transportation decarbonization goals hinges on precise estimations of demand for individual transport modes. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, this study explores the usefulness impact of big data within transport demand estimation. This research focuses on leveraging (big) data collected during the COVID-19 pandemic to estimate the evolution of the mobility demand in Italy. Estimation results reveal in the post-COVID-19 era, more than 96 million national daily trips, about 2.6 trips per capita, with a mobile population of more than 37.6 million Italian travelers per day. Overall, this research allows us to conclude that big data better enhances rational decision-making for mobility demand estimation, which is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, cloud computing, decision-making, mobility demand, transportation

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Authors: Jean-Paul Kapuya Bulaba Nyembwe, John Omomoluwa Ogundiran, Manuel Carlos Gameiro da Silva

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Indoor environmental quality (IEQ) remains a global concern because it impacts people's comfort, health, performance, and general well-being. People spend a significant amount of time in buildings or while commuting, hence ensuring the minimal risk in indoor spaces by ensuring suitable IEQ. IEQ studies are limited regarding developing sub-Saharan countries, whereas there is also a huge risk and concern for the current population and geometric growth as many cities in the region will become mega-cities by 2040 (World Bank report). The absence of suitable IEQ regulations and energy poverty are reasons to assess the IEQ gaps for increased awareness of sustainable interventions to minimize the associated risk. This study evaluates the gaps and potential hazards that exist in the IEQ of sub-Saharan countries using empirical studies of hospital occupants and BRT bus passengers and drivers. The Surveys were conducted in 3 cities of the Democratic Republic of Congo and Lagos metropolis of Nigeria. The results suggest that gaps exist in IEQ for these regions. The gaps indicate existential risk to people’s health, comfort, and well-being. The inferential conclusions are that there is a need for further scientific studies, improvement in IEQ conditions, and ensuring suitable regulations for developing sub-Saharan countries.

Keywords: health hazards, hospitals indoor environmental quality, indoor spaces, occupants, sub-Saharan countries, vehicles

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Authors: Valdrina Ajeti, Icko Gjorgoski

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Stress can be a reason for some physiological and biological disorders in the body. The antioxidative defense system is necessary for the maintenance of redox homeostasis in organisms. Because of its antioxidant effect, alkaline water (AW) is the focus of scientific interest. Adding AW and co-treatment with sodium ascorbate (SA) is expected for the organism to act preventively to hyperthermic stress. To investigate the effect of AW and SA on glucose and cortisol levels during acute hyperthermic stress, white female Wistar laboratory rats, divided into three groups of 10 individuals, were exposed to heat for 80 min, for 21 days. Acute hyperthermic exposure at 41˚C was a cause for oxidative stress. The first group is the control group, the second group is treated with AW, and the third group with AW and SA. Plasma glucose levels were determined by colorimetric method and cortisol was measured using the enzyme-linked immunosorbent assay method. The comparison of the means was made using the Tukey test. Differences were considered significant at a level of p < 0.05. Our results show that levels of glucose and cortisol have been increased in the group treated with AW on the 21st day after treatment (p < 0.0001), but not on the 7th and 14th day as compared to the control group. Also, co-treatment of animals with AW and SA significantly increased the levels of glucose and cortisol on the 21st day after treatment showing a synergistic effect. The individual action of AW, as well as synergism with SA, caused a high protective effect on oxidative damage.

Keywords: alkaline water, sodium ascorbate, hyperthermic stress, glucose, cortisol

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Authors: Ismahafezi Ismail, Mohd Shahrizal Sunar

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The motion of a realistic 3D humanoid character is very important especially for the industries developing computer animations and games. However, this type of motion is seen with a very complex dimensional data as well as body position, orientation, and joint rotation. Integrated Style Motion Editor (ISME), on the other hand, is a method used to alter the 3D humanoid motion capture data utilised in computer animation and games development. Therefore, this study was carried out with the purpose of demonstrating a method that is able to manipulate and deform different motion styles by integrating Key Pose Deformation Technique and Trajectory Control Technique. This motion editing method allows the user to generate new motions from the original motion capture data using a simple interface control. Unlike the previous method, our method produces a realistic humanoid motion style in real time.

Keywords: computer animation, humanoid motion, motion capture, motion editing

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed.

Keywords: speed, Kriging, arterial, traffic volume

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Authors: Ioan Corneliu Salisteanu, Valentin Dogaru Ulieru, Mihaita Nicolae Ardeleanu, Alin Pohoata, Bogdan Salisteanu, Stefan Broscareanu

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Creating a personalized statistic for an individual within the population using IT systems, based on the searches and intercepted spheres of interest they manifest, is just one 'atom' of the artificial intelligence analysis network. However, having the ability to generate statistics based on individual data intercepted from large demographic areas leads to reasoning like that issued by a human mind with global strategic ambitions. The DiaMOTO device is a technical sensory system that allows the interception of car events caused by a driver, positioning them in time and space. The device's connection to the vehicle allows the creation of a source of data whose analysis can create psychological, behavioural profiles of the drivers involved. The SiaMOTO system collects data from many vehicles equipped with DiaMOTO, driven by many different drivers with a unique fingerprint in their approach to driving. In this paper, we aimed to explain the software infrastructure of the SiaMOTO system, a system designed to monitor and improve driver driving behaviour, as well as the criteria and algorithms underlying the intelligent analysis process.

Keywords: artificial intelligence, data processing, driver behaviour, driver monitoring, SiaMOTO

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Authors: Ahmed Hossam ElMolla, Mohamed Hatem Saleh, Hamza Mostafa, Lara Mamdouh, Yassin Wael

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Artificial intelligence (AI) is reshaping industries, and its potential to revolutionize renewable energy and data center operations is immense. By harnessing AI's capabilities, we can optimize energy consumption, predict fluctuations in renewable energy generation, and improve the efficiency of data center infrastructure. This convergence of technologies promises a future where energy is managed more intelligently, sustainably, and cost-effectively. The integration of AI into renewable energy systems unlocks a wealth of opportunities. Machine learning algorithms can analyze vast amounts of data to forecast weather patterns, solar irradiance, and wind speeds, enabling more accurate energy production planning. AI-powered systems can optimize energy storage and grid management, ensuring a stable power supply even during intermittent renewable generation. Moreover, AI can identify maintenance needs for renewable energy infrastructure, preventing costly breakdowns and maximizing system lifespan. Data centers, which consume substantial amounts of energy, are prime candidates for AI-driven optimization. AI can analyze energy consumption patterns, identify inefficiencies, and recommend adjustments to cooling systems, server utilization, and power distribution. Predictive maintenance using AI can prevent equipment failures, reducing energy waste and downtime. Additionally, AI can optimize data placement and retrieval, minimizing energy consumption associated with data transfer. As AI transforms renewable energy and data center operations, modified Key Performance Indicators (KPIs) will emerge. Traditional metrics like energy efficiency and cost-per-megawatt-hour will continue to be relevant, but additional KPIs focused on AI's impact will be essential. These might include AI-driven cost savings, predictive accuracy of energy generation and consumption, and the reduction of carbon emissions attributed to AI-optimized operations. By tracking these KPIs, organizations can measure the success of their AI initiatives and identify areas for improvement. Ultimately, the synergy between AI, renewable energy, and data centers holds the potential to create a more sustainable and resilient future. By embracing these technologies, we can build smarter, greener, and more efficient systems that benefit both the environment and the economy.

Keywords: data center, artificial intelligence, renewable energy, energy efficiency, sustainability, optimization, predictive analytics, energy consumption, energy storage, grid management, data center optimization, key performance indicators, carbon emissions, resiliency

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

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

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

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Authors: Hadi Ardiny, Amir Mohammad Beigzadeh

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Through the utilization of a combination of various sensors and data fusion methods, the detection of potential nuclear threats can be significantly enhanced by extracting more information from different data. In this research, an experimental and modeling approach was employed to track a radioactive source by combining a surveillance camera and a radiation detector (NaI). To run this experiment, three mobile robots were utilized, with one of them equipped with a radioactive source. An algorithm was developed in identifying the contaminated robot through correlation between camera images and camera data. The computer vision method extracts the movements of all robots in the XY plane coordinate system, and the detector system records the gamma-ray count. The position of the robots and the corresponding count of the moving source were modeled using the MCNPX simulation code while considering the experimental geometry. The results demonstrated a high level of accuracy in finding and locating the target in both the simulation model and experimental measurement. The modeling techniques prove to be valuable in designing different scenarios and intelligent systems before initiating any experiments.

Keywords: nuclear threats, radiation detector, MCNPX simulation, modeling techniques, intelligent systems

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Authors: Maryam Khalilzadeh Poshtegal, Seyed Ahmad Mirbagheri, Mojtaba Noury

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In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria.

Keywords: simulation, water level fluctuation, urmia lake, support vector machine

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Authors: Nor Mazlin Zahari, Lian Gan, Xuerui Mao

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The power production in wind farms and the mechanical loads on the turbines are strongly impacted by the wake of the wind turbine. Thus, there is a need for understanding and modelling the turbine wake dynamic in the wind farm and the layout optimization. Having a good wake model is important in predicting plant performance and understanding fatigue loads. In this paper, the Dynamic Mode Decomposition (DMD) was applied to the simulation data generated by a Direct Numerical Simulation (DNS) of flow around a turbine, perturbed by upstream inflow noise. This technique is useful in analyzing the wake flow, to predict its future states and to reflect flow dynamics associated with the coherent structures behind wind turbine wake flow. DMD was employed to describe the dynamic of the flow around turbine from the DNS data. Since the DNS data comes with the unstructured meshes and non-uniform grid, the interpolation of each occurring within each element in the data to obtain an evenly spaced mesh was performed before the DMD was applied. DMD analyses were able to tell us characteristics of the travelling waves behind the turbine, e.g. the dominant helical flow structures and the corresponding frequencies. As the result, the dominant frequency will be detected, and the associated spatial structure will be identified. The dynamic mode which represented the coherent structure will be presented.

Keywords: coherent structure, Direct Numerical Simulation (DNS), dominant frequency, Dynamic Mode Decomposition (DMD)

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Authors: S. Tireche, A. Tairi abdelaziz

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Environmental damage has increased steadily in recent decades: Depletion of natural resources, destruction of the ozone layer, greenhouse effect, degradation of the quality of life, land use etc. New terms have emerged as: "Prevention rather than cure" or "polluter pays" falls within the principles of common sense, their practical implementation still remains fragmented. Among the avenues to be explored, one of the most promising is certainly one that focuses on product design. Indeed, where better than during the design phase, can reduce the source of future impacts on the environment? What choices or those of design, they influence more on the environmental characteristics of products? The most currently recognized at the international level is the analysis of the life cycle (LCA) and Life Cycle Assessment, subject to International Standardization (ISO 14040-14043). LCA provides scientific and objective assessment of potential impacts of the product or service, considering its entire life cycle. This approach makes it possible to minimize impacts to the source in pollution prevention. It is widely preferable to curative approach, currently majority in the industrial crops, led mostly by a report of pollution. The "product" is to reduce the environmental impacts of a given product, taking into account all or part of its life cycle. Currently, there are emerging tools, known as eco-design. They are intended to establish an environmental profile of the product to improve its environmental performance. They require a quantity sufficient information on the product for each phase of its life cycle: raw material extraction, manufacturing, distribution, usage, end of life (recycling or incineration or deposit) and all stages of transport. The assessment results indicate the sensitive points of the product studied, points on which the developer must act.

Keywords: eco design, impact, life cycle analysis (LCA), sustainability

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Authors: Amadou Fall Dia, Maurras Ulbricht Togbe, Aliou Boly, Zakia Kazi Aoul, Elisabeth Metais

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Existing RDF (Resource Description Framework) Stream Processing (RSP) systems allow continuous processing of RDF data issued from different application domains such as weather station measuring phenomena, geolocation, IoT applications, drinking water distribution management, and so on. However, processing window phase often expires before finishing the entire session and RSP systems immediately delete data streams after each processed window. Such mechanism does not allow optimized exploitation of the RDF data streams as the most relevant and pertinent information of the data is often not used in a due time and almost impossible to be exploited for further analyzes. It should be better to keep the most informative part of data within streams while minimizing the memory storage space. In this work, we propose an RDF graph summarization system based on an explicit and implicit expressed needs through three main approaches: (1) an approach for user queries (SPARQL) in order to extract their needs and group them into a more global query, (2) an extension of the closeness centrality measure issued from Social Network Analysis (SNA) to determine the most informative parts of the graph and (3) an RDF graph summarization technique combining extracted user query needs and the extended centrality measure. Experiments and evaluations show efficient results in terms of memory space storage and the most expected approximate query results on summarized graphs compared to the source ones.

Keywords: centrality measures, RDF graphs summary, RDF graphs stream, SPARQL query

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Authors: Khuram Nawaz Sadozai, Rizwan Ahmad, Munawar Raza Kazmi, Awais Habib

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Climate change and crop production are intrinsically associated with each other. Therefore, this research study is designed to assess the impact of climate change on pulses production in Southern districts of Khyber Pakhtunkhwa (KP) Province of Pakistan. Two pulses (i.e. chickpea and mung bean) were selected for this research study with respect to climate change. Climatic variables such as temperature, humidity and precipitation along with pulses production and area under cultivation of pulses were encompassed as the major variables of this study. Secondary data of climatic variables and crop variables for the period of thirty four years (1986-2020) were obtained from Pakistan Metrological Department and Agriculture Statistics of KP respectively. Panel data set of chickpea and mung bean crops was estimated separately. The analysis validate that both data sets were a balanced panel data. The Hausman specification test was run separately for both the panel data sets whose findings had suggested the fixed effect model can be deemed as an appropriate model for chickpea panel data, however random effect model was appropriate for estimation of the panel data of mung bean. Major findings confirm that maximum temperature is statistically significant for the chickpea yield. This implies if maximum temperature increases by 1 0C, it can enhance the chickpea yield by 0.0463 units. However, the impact of precipitation was reported insignificant. Furthermore, the humidity was statistically significant and has a positive association with chickpea yield. In case of mung bean the minimum temperature was significantly contributing in the yield of mung bean. This study concludes that temperature and humidity can significantly contribute to enhance the pulses yield. It is recommended that capacity building of pulses growers may be made to adapt the climate change strategies. Moreover, government may ensure the availability of climate change resistant varieties of pulses to encourage the pulses cultivation.

Keywords: climate change, pulses productivity, agriculture, Pakistan

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Authors: Huang Dennis, Aurelio Aziel, Burra Venkata Durga Kumar

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Nowadays, smartphones and mobile operating systems have been popularly widespread in our daily lives. As people use smartphones, they tend to store more private and essential data on their devices, because of this it is very important to develop more secure mobile operating systems and cloud storage to secure the data. However, several factors can cause security risks in mobile operating systems such as malware, malicious app, phishing attacks, ransomware, and more, all of which can cause a big problem for users as they can access the user's private data. Those problems can cause data loss, financial loss, identity theft, and other serious consequences. Other than that, during the pandemic, people will use their mobile devices more and do all sorts of transactions online, which may lead to more victims of online scams and inexperienced users being the target. With the increase in attacks, researchers have been actively working to develop several countermeasures to enhance the security of operating systems. This study aims to provide an overview of the security and privacy issues in mobile operating systems, identifying the potential risk of operating systems, and the possible solutions. By examining these issues, we want to provide an easy understanding to users and researchers to improve knowledge and develop more secure mobile operating systems.

Keywords: mobile operating system, security, privacy, Malware

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Authors: Stella C. Okenu, Stephen O. Adikwu, Martins E. Okoro

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The geothermal energy resource potential of the Lower Benue Trough (LBT) in Nigeria was evaluated in this study using spectral analysis of high-resolution aeromagnetic (HRAM) data. The reduced to the equator aeromagnetic data was divided into sixteen (16) overlapping blocks, and each of the blocks was analyzed to obtain the radial averaged power spectrum which enabled the computation of the top and centroid depths to magnetic sources. The values were then used to assess the Curie Point Depth (CPD), geothermal gradients, and heat flow variations in the study area. Results showed that CPD varies from 7.03 to 18.23 km, with an average of 12.26 km; geothermal gradient values vary between 31.82 and 82.50°C/km, with an average of 51.21°C/km, while heat flow variations range from 79.54 to 206.26 mW/m², with an average of 128.02 mW/m². Shallow CPD zones that run from the eastern through the western and southwestern parts of the study area correspond to zones of high geothermal gradient values and high subsurface heat flow distributions. These areas signify zones associated with anomalous subsurface thermal conditions and are therefore recommended for detailed geothermal energy exploration studies.

Keywords: geothermal energy, curie-point depth, geothermal gradient, heat flow, aeromagnetic data, LBT

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Authors: Vo Thi Hai Quan

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Nam Con Son sedimentary basin is one of the very important oil and gas basins in offshore Vietnam. Hai Thach field of block 05-2 contains mostly gas accumulations in fine-grained, sand/mud-rich turbidite system, which was deposited in a turbidite channel and fan environment. Major Upper Miocene reservoir of HT-3X lies above a well-developed unconformity. The main objectives of this study are to reconstruct depositional environment and to assess the reservoir quality using data from 14 meters of core samples and digital wireline data of the well HT-3X. The wireline log and core data showed that the vertical sequences of representative facies of the well mainly range from Tb to Te divisions of Bouma sequences with predominance of Tb and Tc compared to Td and Te divisions. Sediments in this well were deposited in a submarine fan association with very fine to fine-grained, homogeneous sandstones that have high porosity and permeability, high- density turbidity currents with longer transport route from the sediment source to the basin, indicating good quality of reservoir. Sediments are comprised mainly of the following sedimentary structures: massive, laminated sandstones, convoluted bedding, laminated ripples, cross-laminated ripples, deformed sandstones, contorted bedding.

Keywords: Hai Thach field, Miocene sand, turbidite, wireline data

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Authors: Saseyi Olaitan Olaoluwa, Osifeko Olalekan Remigious

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The study was meant to identify the impediments to female sports management and participation in the selected colleges. Seven colleges of education in the south west parts of the country were selected for the study. A total of one hundred and five subjects were sampled to supply data. Only one hundred adequately completed and returned, copies of the questionnaire were used for data analysis. The collected data were analysed descriptively. The result of the study showed that inadequate fund, personnel, facilities equipment, supplies, management of sports, supervision and coaching were some of the impediments to female sports management and participation. Athletes were not encouraged to participate. Based on the findings, it was recommended that the government should come to the aid of the colleges by providing fund and other needs that will make sports attractive for enhanced participation.

Keywords: female sports, impediments, management, Nigeria, south west, colleges

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Authors: Munir Kassa, Mengistu Hailemariam, Meghan Obermeyer, Kefelegn Baruda, Yonas Getachew, Asnakech Dessie

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Improving the quality of sexual and reproductive health services that women receive is expected to have an impact on women’s satisfaction with the services, on their continued use and, ultimately, on their ability to achieve their fertility goals or reproductive intentions. Surprisingly, however, there is little empirical evidence of either whether this expectation is correct, or how best to improve service quality within sexual and reproductive health programs so that these impacts can be achieved. The Recent focus on quality has prompted more physicians to do quality improvement work, but often without the needed skill sets, which results in poorly conceived and ultimately unsuccessful improvement initiatives. As this renders the work unpublishable, it further impedes progress in the field of health care improvement and widens the quality chasm. Moreover, since 2014, the Center for International Reproductive Health Training (CIRHT) has worked diligently with 11 teaching hospitals across Ethiopia to increase access to contraception and abortion care services. This work has included improving pre-service training through education and curriculum development, expanding hands-on training to better learn critical techniques and counseling skills, and fostering a “team science” approach to research by encouraging scientific exploration. This is the first time this systematic approach has been applied and documented to improve access to high-quality services in Ethiopia. The purpose of this article is to report initiatives undertaken, and findings concluded by the clinical service team at CIRHT in an effort to provide a pragmatic approach to quality improvement projects. An audit containing nearly 300 questions about several aspects of patient care, including structure, process, and outcome indicators was completed by each teaching hospital’s quality improvement team. This baseline audit assisted in identifying major gaps and barriers, and each team was responsible for determining specific quality improvement aims and tasks to support change interventions using Shewart’s Cycle for Learning and Improvement (the Plan-Do-Study-Act model). To measure progress over time, quality improvement teams met biweekly and compiled monthly data for review. Also, site visits to each hospital were completed by the clinical service team to ensure monitoring and support. The results indicate that applying an evidence-based, participatory approach to quality improvement has the potential to increase the accessibility and quality of services in a short amount of time. In addition, continued ownership and on-site support are vital in promoting sustainability. This approach could be adapted and applied in similar contexts, particularly in other African countries.

Keywords: abortion, contraception, quality improvement, service provision

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Authors: Maryam Tabrizi, Shahrzad Aarup

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Objectives- Understanding the close association between oral health and overall health for older adults at the right time and right place, a person, focus treatment through Project ECHO telementoring. Methodology- Data from monthly ECHO telementoring sessions were provided for three years. Sessions including case presentations, overall health conditions, considering medications, organ functions limitations, including the level of cognition. Contributions- Providing the specialist level of providing care to all elderly regardless of their location and other health conditions and decreasing oral health inequity by increasing workforce via Project ECHO telementoring program worldwide. By 2030, the number of adults in the USA over the age of 65 will increase more than 60% (approx.46 million) and over 22 million (30%) of 74 million older Americans will need specialized geriatrician care. In 2025, a national shortage of medical geriatricians will be close to 27,000. Most individuals 65 and older do not receive oral health care due to lack of access, availability, or affordability. One of the main reasons is a significant shortage of Oral Health (OH) education and resources for the elderly, particularly in rural areas. Poor OH is a social stigma, a thread to quality and safety of overall health of the elderly with physical and cognitive decline. Poor OH conditions may be costly and sometimes life-threatening. Non-traumatic dental-related emergency department use in Texas alone was over $250 M in 2016. Most elderly over the age of 65 present with at least one or multiple chronic diseases such as arthritis, diabetes, heart diseases, and chronic obstructive pulmonary disease (COPD) are at higher risk to develop gum (periodontal) disease, yet they are less likely to get dental care. In addition, most older adults take both prescription and over-the-counter drugs; according to scientific studies, many of these medications cause dry mouth. Reduced saliva flow due to aging and medications may increase the risk of cavities and other oral conditions. Most dental schools have already increased geriatrics OH in their educational curriculums, but the aging population growth worldwide is faster than growing geriatrics dentists. However, without the use of advanced technology and creating a network between specialists and primary care providers, it is impossible to increase the workforce, provide equitable oral health to the elderly. Project ECHO is a guided practice model that revolutionizes health education and increases the workforce to provide best-practice specialty care and reduce health disparities. Training oral health providers for utilizing the Project ECHO model is a logical response to the shortage and increases oral health access to the elderly. Project ECHO trains general dentists & hygienists to provide specialty care services. This means more elderly can get the care they need, in the right place, at the right time, with better treatment outcomes and reduces costs.

Keywords: geriatric, oral health, project echo, chronic disease, oral health

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Authors: Kulwinder Singh Mann

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The innovative features of information system, known as Radiology Information System (RIS), for electronic medical records has shown a good impact in the hospital. The objective is to help and make their work easier; such as for a physician to access the patient’s data and for a patient to check their bill transparently. The interoperability of RIS with the other intra-hospital information systems it interacts with, dealing with the compatibility and open architecture issues, are accomplished by two novel mechanisms. The first one is the particular message handling system that is applied for the exchange of information, according to the Health Level Seven (HL7) protocol’s specifications and serves the transfer of medical and administrative data among the RIS applications and data store unit. The second one implements the translation of information between the formats that HL7 and Digital Imaging and Communication in Medicine (DICOM) protocols specify, providing the communication between RIS and Picture and Archive Communication System (PACS) which is used for the increasing incorporation of modern medical imaging equipment.

Keywords: RIS, PACS, HIS, HL7, DICOM, messaging service, interoperability, digital images

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Authors: Stephane Laussac, Jacques Piazzola, Gilles Tedeschi

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

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

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Authors: Win Thandar Soe, Innocent Mpawenimana, Mathieu Di Fazio, Cécile Belleudy, Aung Ze Ya

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Intelligent electronic equipment and automation network is the brain of high-tech energy management systems in critical role of smart homes dominance. Smart home is a technology integration for greater comfort, autonomy, reduced cost, and energy saving as well. These services can be provided to home owners for managing their home appliances locally or remotely and consequently allow them to automate intelligently and responsibly their consumption by individual or collective control systems. In this study, three smart plugs are described and one of them tested on typical household appliances. This article proposes to collect the data from the wireless technology and to extract some smart data for energy management system. This smart data is to quantify for three kinds of load: intermittent load, phantom load and continuous load. Phantom load is a waste power that is one of unnoticed power of each appliance while connected or disconnected to the main. Intermittent load and continuous load take in to consideration the power and using time of home appliances. By analysing the classification of loads, this smart data will be provided to reduce the communication of wireless sensor network for energy management system.

Keywords: energy management, load profile, smart plug, wireless sensor network

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Authors: Hadi Rouhi Belvirdi

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The construction of underground structures using the traditional method, which begins with excavation and the implementation of the foundation of the underground structure, continues with the construction of the main structure from the ground up, and concludes with the completion of the final ceiling, is known as the Bottom-Up Method. In contrast to this method, there is an advanced technique called the Top-Down Method, which has practically replaced the traditional construction method in large projects in industrialized countries in recent years. Unlike the traditional approach, this method starts with the construction of surrounding walls, columns, and the final ceiling and is completed with the excavation and construction of the foundation of the underground structure. Some of the most significant advantages of this method include the elimination or minimization of formwork surfaces, the removal of temporary bracing during excavation, the creation of some traffic facilities during the construction of the structure, and the possibility of using it in limited and high-traffic urban spaces. Despite these numerous advantages, unfortunately, there is still insufficient awareness of this method in our country, to the extent that it can be confidently stated that most stakeholders in the construction industry are unaware of the existence of such a construction method. However, it can be utilized as a very important execution option alongside other conventional methods in the construction of underground structures. Therefore, due to the extensive practical capabilities of this method, this article aims to present a methodology for constructing underground structures based on the aforementioned advanced method to the scientific community of the country, examine the advantages and limitations of this method and their impacts on time and costs, and discuss its application in urban spaces. Finally, some underground structures executed in the Ahvaz urban rail, which are being implemented using this advanced method to the best of our best knowledge, will be introduced.

Keywords: top-down method, bottom-up method, underground structure, construction method

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Authors: Ghulam Murshid, Samil Ullah

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Scientific studies suggested that the incremented greenhouse gas concentration in the atmosphere, particularly of carbon dioxide (CO2) is one of the major factors in global warming. The concentration of CO2 in our climate has crossed the milestone level of 400 parts per million (ppm) hence breaking the record of human history. A report by 49 researchers from 10 countries said, 'Global CO2 emissions from burning fossil fuels will rise to a record 36 billion metric tons (39.683 billion tons) this year.' Main contributors of CO2 in to the atmosphere are usage of fossil fuel, transportation sector and power generation plants. Among all available technologies, which include; absorption via chemicals, membrane separation, cryogenic and adsorption are in practice around the globe. Adsorption of CO2 using metal organic frameworks (MOF) is getting interest of researcher around the globe. In the current work, MOF-74 as well as modified MOF-74 with a sterically hindered amine (AMP) was synthesized and characterized. The modification was carried out using a sterically hindered amine in order to study the effect on its adsorption capacity. Resulting samples were characterized by using Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscope (FESEM), Thermal Gravimetric Analyser (TGA) and Brunauer-Emmett-Teller (BET). The FTIR results clearly confirmed the formation of MOF-74 structure and the presence of AMP. FESEM and TEM revealed the topography and morphology of the both MOF-74 and amine modified MOF. BET isotherm result shows that due to the addition of AMP in to the structure, significant enhancement of CO2 adsorption was observed.

Keywords: adsorbents, amine, CO2, global warming

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Authors: Obaid Alotaibi

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The geographical location of the State of Kuwait contributed significantly to the suffering of Kuwait in the past, due to the scarcity of natural water resources and the inability of the State's financial resources to provide other water resources to meet the needs of the population. The problem of water scarcity in Kuwait remained until the beginning of the second half of the twentieth century, as the country's economic conditions revived with the emergence and export of oil; which was clearly reflected in the steady growth of the population. To cope with this population, increase, it was necessary to expand the various development programs to include all sectors of the state. The process of development and urbanization could not start without finding solutions to the problem of water shortage in Kuwait. The only option for officials to meet the needs of the population and the different sectors of water development is the desalination of seawater. This process necessitated the establishment of six desalination plants along the coast of Kuwait and extended freshwater arteries to reach everywhere on the land. However, this does not mean that the problem of water shortage has been completely solved. The desalination plants are not meeting the country's future water needs, especially considering the increasing population growth. These stations are nearing completion and they need to be replaced, renovation and maintenance, require significant expenses. Therefore, it was necessary for scientific research to address the issue of water in Kuwait, whether in the field of development of existing resources or in the field of rationalization of consumption and protection of available resources. The study focused on how to address the increasing demand for water resulting from population increase, the impact of water on economic and social development, the prospects of water resources in Kuwait and its ability to meet the needs of the country by 2030.

Keywords: economic, development, Kuwait, social, water resources

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Authors: Dimitris Kokkinos, Panagiotis Prinos

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In the last three decades, climate change has been studied extensively from scientific community, and its consequences are more than clear all around the world. Most countries have carried out a great effort to reduce global warming rates with the ratification and implementation of several international treaties. Moreover, many of them have already adopted national plans in order to adapt to climate change effects and mitigate human and economic losses. Coastal environments, with their inherent physical sensitivity, will face important challenges as a result of projected changes in climate conditions and hundreds of millions of people will be affected. Coastal zones are of high social and economic value and this research focuses on the Mediterranean basin, which is a densely populated and highly urbanized area. With 40% of its land used for human activity and the inevitability of the impacts of the climate change, it is obvious that some form of adaptation measures will be necessary. In this regard, the EU tools, policies and legislation concerning adaptation to climate change are presented. Additionally, the National Adaptation Strategies of State members of the Mediterranean basin are compared and analyzed concerning the coastal areas, along with an overview of projects and programs results focused on coastal issues at different spatial scales. The purpose of this research is to stress the differences between Mediterranean State members at methodologies implemented, to highlight the possible gaps in co-ordination and to emphasize on research initiatives that EU can build upon moving towards an integrated adaptation planning on a region-wide basis.

Keywords: coastal adaptation, Mediterranean Basin, climate change, coastal environments

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Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

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Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: conditional generative adversarial net, market and credit risk management, neural network, time series

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Authors: Kien Vu, Jien Min Soh, Mohamed Jahangir Abubacker, Piyawut Pattamanon, Soojin Lee, Suvro Banerjee

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Recent advances in Generative Artificial Intelligence (GenAI), in particular Large Language Models (LLMs) have shown promise to disrupt multiple industries at scale. However, LLMs also present unique challenges, notably, these so-called "hallucination" which is the generation of outputs that are not grounded in the input data that hinders its adoption into production. Common practice to mitigate hallucination problem is utilizing Retrieval Agmented Generation (RAG) system to ground LLMs'response to ground truth. RAG converts the grounding documents into embeddings, retrieve the relevant parts with vector similarity between user's query and documents, then generates a response that is not only based on its pre-trained knowledge but also on the specific information from the retrieved documents. However, the RAG system is not suitable for tabular data and subsequent data analysis tasks due to multiple reasons such as information loss, data format, and retrieval mechanism. In this study, we have explored a novel methodology that combines planning-and-execution and code generation agents to enhance LLMs' data analysis capabilities. The approach enables LLMs to autonomously dissect a complex analytical task into simpler sub-tasks and requirements, then convert them into executable segments of code. In the final step, it generates the complete response from output of the executed code. When deployed beta version on DataSense, the property insight tool of PropertyGuru, the approach yielded promising results, as it was able to provide market insights and data visualization needs with high accuracy and extensive coverage by abstracting the complexities for real-estate agents and developers from non-programming background. In essence, the methodology not only refines the analytical process but also serves as a strategic tool for real estate professionals, aiding in market understanding and enhancement without the need for programming skills. The implication extends beyond immediate analytics, paving the way for a new era in the real estate industry characterized by efficiency and advanced data utilization.

Keywords: large language model, reasoning, planning and execution, code generation, natural language processing, prompt engineering, data analysis, real estate, data sense, PropertyGuru

Procedia PDF Downloads 90