Search results for: artificial intelligence in medicine

Authors: A. Kabiri-Samani, M. J. Seddighi

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There is no doubt that a key factor in the manifestation of architecture is the interaction of human and nature. Explaining the type of relationship defined by “the architect” between architecture and nature opens a window towards understanding the theoretical conceptions of the architect as the creator of “architecture”. Now, if these theoretical foundations are put under scrutiny from the viewpoint of Islam, and an architect considers the relationship of human and nature within the context of Islam, he would let nature to manifest itself in architecture. The reasons for such a relationship is explicable in terms of the degree and nature of knowledge of the architect about nature; while the way it comes to existence is explained by defining the force of nature – ruling the entire nature – and its acts. It is by the scientific command of the architect and his mastery in the hermetic force of nature that the material bodies of buildings evolve from artificial to natural. Additionally, the presence of nature creates hermetic architectural spaces for the spiritual development of humans while serving for living at different levels.

Keywords: nature, Islam, cognition, science, presence, elixir

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Authors: Martin Cai, Khadijah B. Hashim, Leng Leo, Edmund F. Tian

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Analysis of traditional Chinese medicinal (TCM) supplements has always been a laborious task, particularly in the case of multi‐ingredient formulations. Traditionally, herbal extracts are analysed using one or few markers compounds. In the recent years, however, pharmaceutical companies are introducing health supplements of TCM active ingredients to cater to the needs of consumers in the fast-paced society in this age. As such, new problems arise in the aspects of composition identification as well as quality analysis. In most cases of products or supplements formulated with multiple TCM herbs, the chemical composition, and nature of each raw material differs greatly from the others in the formulation. This results in a requirement for individual analytical processes in order to identify the marker compounds in the various botanicals. Thin-layer Chromatography (TLC) is a simple, cost effective, yet well-regarded method for the analysis of natural products, both as a Pharmacopeia-approved method for identification and authentication of herbs, and a great analytical tool for the discovery of chemical compositions in herbal extracts. Recent technical advances introduced High-Performance TLC (HPTLC) where, with the help of automated equipment and improvements on the chromatographic materials, both the quality and reproducibility are greatly improved, allowing for highly standardised analysis with greater details. Here we report an industrial consultancy project with ONI Global Pte Ltd for the analysis of LAC Liver Protector, a TCM formulation aimed at improving liver health. The aim of this study was to identify 4 key components of the supplement using HPTLC, following protocols derived from Chinese Pharmacopeia standards. By comparing the TLC profiles of the supplement to the extracts of the herbs reported in the label, this project proposes a simple and cost-effective analysis of the presence of the 4 marker compounds in the multi‐ingredient formulation by using 4 different HPTLC methods. With the increasing trend of small and medium-sized enterprises (SMEs) bringing natural products and health supplements into the market, it is crucial that the qualities of both raw materials and end products be well-assured for the protection of consumers. With the technology of HPTLC, science can be incorporated to help SMEs with their quality control, thereby ensuring product quality.

Keywords: traditional Chinese medicine supplement, high performance thin layer chromatography, active ingredients, product quality

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Authors: Ruzanna Muradyan

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Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: L. Parisi

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Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.

Keywords: kinetics, kinematics, cyclograms, neural networks, transtibial amputation

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Authors: Dariush Jafari, Seyed Ali Jafari

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The recommended limit for cadmium concentration in potable water is less than 0.005 mg/L. A continuous biosorption process using indigenous red seaweed, Gracilaria corticata, was performed to remove cadmium from the potable water. The process was conducted under fixed conditions and the breakthrough curves were achieved for three consecutive sorption-desorption cycles. A modeling based on Artificial Neural Network (ANN) was employed to fit the experimental breakthrough data. In addition, a simplified semi empirical model, Thomas, was employed for this purpose. It was found that ANN well described the experimental data (R2>0.99) while the Thomas prediction were a bit less successful with R2>0.97. The adjusted design parameters using the nonlinear form of Thomas model was in a good agreement with the experimentally obtained ones. The results approve the capability of ANN to predict the cadmium concentration in potable water.

Keywords: ANN, biosorption, cadmium, packed-bed, potable water

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Authors: L. Mouzai, M. Bouhadef

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Rills begin to be generated once overland flow shear capacity overcomes the soil surface resistance. This resistance depends on soil texture, the arrangement of soil particles and on chemical and physical properties. The rill density could affect soil erosion, especially when the distance between the rills (interrill) contributes to the variation of the rill characteristics, and consequently on sediment concentration. To investigate this point, agricultural sandy soil, a soil tray of 0.2x1x3m³ and a piece of hardwood rectangular in shape to build up rills were the base of this work. The results have shown that small lines have been developed between the rills and the flow acceleration increased in comparison to the flow on the flat surface (interrill). Sediment concentration increased with increasing rill number (density).

Keywords: artificial rainfall, experiments, rills, soil erosion, transport capacity

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Authors: Telmen Dashdondov, Selenge Erdenechimeg

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Padus asiatica contains large quantities of polyphenolic compounds, and it is one of the most consumed fruits throughout the country. These compounds have the biological activity of the fruit and have long been used in traditional Mongolian medicine for diarrhea, coughs, pneumonia, and gastritis. In this study, we studied the solvents that can be used to make extracts from dried raw fruits; in order to determine the amount of tannin in Padus asiatica, we selected three solvents: distilled water, 20% ethanol, and 40% ethanol, and determined the amount of tannin. As a result, the amount of extract (distilled water) was 11.8%, the amount of extract (20% ethanol) was 15.7%, and the amount of extract (40% ethanol) was 8.2%. Therefore, it was found that tannins are extracted better in 20% ethanol solution.

Keywords: Padus asiatica, tannin, diarrhea, Mongolian medicinal plant

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Authors: Kok-Chong Yong

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Poly(butadiene-co-acrylonitrile)-polyaniline Dodecylbenzenesulfonate [NBR-PAni.DBSA] blends with useful electrical conductivity (up to 0.1 S/cm) were prepared and their corrosion inhibiting behaviours for carbon steel were successfully assessed for the first time. The level of compatibility between NBR and PAni.DBSA was enhanced through the introduction of 1.0 wt % hydroquinone. As found from both total immersion and electrochemical corrosion tests, NBR-PAni.DBSA blends with 10.0-30.0 wt% of PAni.DBSA content exhibited the best corrosion inhibiting behaviour for carbon steel, either in acid or artificial brine environment. On the other hand, blends consisting of very low and very high PAni.DBSA contents (i.e. ≤ 5.0 wt % and ≥ 40.0 wt %) showed significantly poorer corrosion inhibiting behaviour for carbon steel.

Keywords: conductive rubber, nitrile rubber, polyaniline, carbon steel, corrosion inhibition

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Authors: N. Szczepanska, B. Kudlak, G. Yotova, S. Tsakovski, J. Namiesnik

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In the scientific literature related to the widely understood issue of packaging materials designed to have contact with food (food contact materials), there is much information on raw materials used for their production, as well as their physiochemical properties, types, and parameters. However, not much attention is given to the issues concerning migration of toxic substances from packaging and its actual influence on the health of the final consumer, even though health protection and food safety are the priority tasks. The goal of this study was to estimate the impact of particular foodstuff packaging type, food production, and storage conditions on the degree of leaching of potentially toxic compounds and endocrine disruptors to foodstuffs using the acute toxicity test Microtox and XenoScreen YES YAS assay. The selected foodstuff packaging materials were metal cans used for fish storage and tetrapak. Five stimulants respectful to specific kinds of food were chosen in order to assess global migration: distilled water for aqueous foods with a pH above 4.5; acetic acid at 3% in distilled water for acidic aqueous food with pH below 4.5; ethanol at 5% for any food that may contain alcohol; dimethyl sulfoxide (DMSO) and artificial saliva were used in regard to the possibility of using it as an simulation medium. For each packaging three independent variables (temperature and contact time) factorial design simulant was performed. Xenobiotics migration from epoxy resins was studied at three different temperatures (25°C, 65°C, and 121°C) and extraction time of 12h, 48h and 2 weeks. Such experimental design leads to 9 experiments for each food simulant as conditions for each experiment are obtained by combination of temperature and contact time levels. Each experiment was run in triplicate for acute toxicity and in duplicate for estrogen disruption potential determination. Multi-factor analysis of variation (MANOVA) was used to evaluate the effects of the three main factors solvent, temperature (temperature regime for cup), contact time and their interactions on the respected dependent variable (acute toxicity or estrogen disruption potential). From all stimulants studied the most toxic were can and tetrapak lining acetic acid extracts that are indication for significant migration of toxic compounds. This migration increased with increase of contact time and temperature and justified the hypothesis that food products with low pH values cause significant damage internal resin filling. Can lining extracts of all simulation medias excluding distilled water and artificial saliva proved to contain androgen agonists even at 25°C and extraction time of 12h. For tetrapak extracts significant endocrine potential for acetic acid, DMSO and saliva were detected.

Keywords: food packaging, extraction, migration, toxicity, biotest

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Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

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For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network

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Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe

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The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.

Keywords: non-linear systems, fuzzy set Models, neural network, control law

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Authors: Kohji Mitsubayashi, Takahiro Arakawa, Kohji Mitsubayashi

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‘Cavitas sensors’ attached to human body cavities such as a contact lens type and a mouthguard (‘no implantable', ‘no wearable’) attracted attention as self-detachable devices for daily medicine. In this contribution, the soft contact lens glucose sensor for tear sugar monitoring will be introduced. And the mouthguard sensor with dental materials integrated with Bluetooth low energy (BLE) wireless module for real-time monitoring of saliva glucose would also be demonstrated. In the near future, those self-detachable cavitas sensors are expected to improve quality of life in view of the aging of society.

Keywords: cavitas sensor, biosensor, contact lens, mouthguard

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Authors: Hocine Terrab , Abdelhafid Bayadi, Adel Kara, Ayman El-Hag

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Insulator flashover under polluted conditions has been a serious threat on the reliability of power systems. It is known that the flashover process is mainly affected by the environmental conditions such as; the pollution level and humidity. Those are the essential parameters influencing the wetting process. This paper presents an investigation of the characteristics of leakage current (LC) developed on the surface of porcelain insulator at contaminated conditions under AC voltage. The study is done in an artificial fog chamber and the LC is characterized for different stages; dry, wetted and presence of discharge activities. Time-frequency and spectral analysis are adopted to calculate the evolution of LC characteristics with various stages prior to flashover occurrence. The preliminary results could be used in analysing the LC to develop more effective diagnosis of early signs of dry band arcing as an indication for insulation washing.

Keywords: flashover, harmonic components, leakage current, phase angle, statistical analysis

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis

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Authors: Mohammad Asadpour

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Cryptosporidium spp. is zoonotic pathogens transmissible from a variety of animals to humans and is a considerable public health concern. Calves have been identified in numerous reports as a major source of environmental contamination with this pathogen. Parasite has a different species that are the cases of zoonotic disease in immunodeficient people and neonatal calves. Cryptosporidium oocysts are extremely resistant to chlorine and other physical cases that commonly used in drinking water. Reproduction of resistant oocytes is a way for this monoxenous parasite to remain in the environment. Cryptosporidium parvum is the most important species that has human and cattle genotypes. Cryptosporidium is one of the most important causes of diarrhea in neonatal calves and also, one of the four causes of diarrhea symptoms in pre-weaned calves. Because of the incompetent immune system in calves, Cryptosporidium infection is the cause of a lot of problems in raising farms.

Keywords: Cryptosporidium spp, dairy calves, importance, veterinary medicine

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Authors: Zachary Muthii Rukenya, James Mbaria, Peter Mbaabu, Kiama Stephen Gitahi, Ronald Onzago

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Aloe turkanensis is one of the widely used medicinal shrub and in Kenya the plant is mainly found in Baringo, Isiolo, Laikipia, Turkana and West Pokot Counties where it is used in ethno-medicine and ethno-veterinary medicine. The Turkana community uses the plant products to manage malaria, wounds, stomach ache, constipation, pain, skin infection, poultry diseases and retained afterbirth in cows. This evaluated the efficacy and safety of the plant obtained from Turkana County, Kenya. Preliminary data on the use of the plant in the county was collected through observation, photographing and interviews. A sample of the whole plant was harvested in Natira sublocation, in ex-Turkana west district in February 2012 after identification by Aloe-working group herbalists who voluntarily provided information on its medicinal uses. Botanical identification was done at Kenya Forest Research Centre in Karura where voucher specimen was deposited. Cold maceration using 70% methanol and distilled water was used for extraction. Bioassays were to determine the effects of the plant extracts on brine shrimp and selected bacterial and fungal cultures. The extracts were tested in-vitro activity against standard cultures of B. cereus (ATCC 11778), S. aureus (ATCC25923), P. aeroginosa (ATCC 27853), E. coli (ATCC 25922) and a human infections clinical isolate of C. albicans. The extracts of Aloe turkanensis inhibited the growth B. cereus (100-200 mg/ml), S. aureus (50-100 mg/ml), P. aeroginosa (200mg/ml), E. coli (400mg/ml) while C. albicans was not affected. The extracts also inhibited the growth of S. aureus and B. cereus with mean diameters of inhibition zones being 19.75±1 mm and 18.5±05 mm reapectively. Phytochemical screening showed the presence of alkaloids, tarpenoids, steroids, quinones, saponins and tannins in the plant extracts. The extract was found to be non-toxic at a concentration of 1000µg/ml with a 100% survival of Brine Shrimp larva. It was concluded that Aloe turkanensis growing the study area has metabolites that inhibit the growth of microorganisms and is however, there is need for further studies to validate the in-vivo bioactivity of the plant and more generate adequate toxicological data.to support conservation, value chain addition of its products and widespread use as a herbal remedy.

Keywords: Aloe turkanensis, bioactivity, cultivated, human infections

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Authors: Y. Kourd, D. Lefebvre, N. Guersi

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This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.

Keywords: neural networks, decision trees, diagnosis, behaviors

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Authors: Jun-Yong Lee, Hyoung-Gook Kim

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In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing

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Authors: Rabia Bashir, Sajjad Akbar

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There is an inevitable demand of web mining due to rapid increase of huge information on the Internet, but the striking variety of web structures has made required content retrieval a difficult task. To counter this issue, Web Content Mining (WCM) emerges as a potential candidate which extracts and integrates suitable resources of data to users. In past few years, research has been done on several extraction techniques for WCM i.e. agent-based, template-based, assumption-based, statistic-based, wrapper-based and machine learning. However, it is still unclear that either these approaches are efficiently tackling the significant challenges of WCM or not. To answer this question, this paper identifies these challenges such as language independency, structure flexibility, performance, automation, dynamicity, redundancy handling, intelligence, relevant content retrieval, and privacy. Further, mapping of these challenges is done with existing extraction mechanisms which helps to adopt the most suitable WCM approach, given some conditions and characteristics at hand.

Keywords: content mining challenges, web content mining, web content extraction approaches, web information retrieval

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Authors: Mohammed Saad Alanazi

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Satisfaction of travelers with services provided in the airports is a sign of competitiveness and the corporate image of the airport. This study conducted a systematic literature review of recent studies published after 2017 regarding the factors that positively influence travelers’ satisfaction and encourage them to report positive reviews online. This study found variations among the studies found. They used several research methodologies, and datasets and focused on different airports, yet, they commonly categorized airport services into seven categories that should receive high intention because their qualities were found increasing review rate and positivity. It was found that studies targeting travelers’ satisfaction and intention of revisiting tended to use primary sources of data (survey); meanwhile, studies concerned positivity and negativity of comments towards airport services often used online reviews provided by travelers.

Keywords: business Intelligence, airport service quality, passenger satisfaction, thorough analysis

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Authors: Taissir Fekih Romdhane

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This paper proposes a mobile web application named Pharmacy-Station that sells medicines and permits user to search for medications based on their symptoms, making it is easy to locate a specific drug online without the need to visit a pharmacy where it may be out of stock. This application is developed using the jQuery Mobile framework, which uses many web technologies and languages such as HTML5, PHP, JavaScript and CSS3. To test the proposed application, we used data from popular pharmacies in Saudi Arabia that included important information such as location, contact, and medicines in stock, etc. This document describes the different steps followed to create the Pharmacy-Station application along with screenshots. Finally, based on the results, the paper concludes with recommendations and further works planned to improve the Pharmacy-Station mobile application.

Keywords: pharmacy, mobile application, jquery mobile framework, search, medicine

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Authors: Simon B. N. Thompson

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Retinoblastoma is a rare type of childhood genetic cancer that affects children worldwide. The diagnosis is often missed due to lack of education and difficulty in presentation of the tumor. Frequently, the tumor on the retina is noticed by photography when the red-eye flash, commonly seen in normal eyes, is not produced. Instead, a yellow or white colored patch is seen or the child has a noticeable strabismus. Early detection can be life-saving though often results in removal of the affected eye. Remaining functioning in the healthy eye when the child is young has resulted in super-vision and high or above-average intelligence. Technological advancement of cameras has helped in early detection. Brain imaging has also made possible early detection of neurological diseases and, together with the monitoring of cortisol levels and yawning frequency, promises to be the next new early diagnostic tool for the detection of neurological diseases where cortisol insufficiency is particularly salient, such as multiple sclerosis and Cushing’s disease.

Keywords: cortisol, neurological disease, retinoblastoma, Thompson cortisol hypothesis, yawning

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Authors: Susanne Ferschl, Peter Holzmüller, Elisabeth Wacker

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Ageing populations, advances in medical sciences and digitalization, diversity and social disparities, as well as the increasing need for skilled healthcare professionals, are challenging healthcare systems around the globe. To address these challenges, future healthcare systems need to center on human needs taking into account the living environments that shape individuals’ knowledge of and opportunities to access healthcare. Moreover, health should be considered as a common good and an integral part of securing livelihoods for all people. Therefore, the adoption of a systems approach, as well as inter-disciplinary and inter-sectoral cooperation among healthcare providers, are essential. Additionally, the active engagement of target groups in the planning and design of healthcare structures is indispensable to understand and respect individuals’ health and livelihood needs. We will present the research project b4 – identifying needs | building bridges | developing health care in the social space, which is situated within this reasoning and accompanies the cross-sectoral cooperation project Brückenschlag (building bridges) in a Bavarian district. Brückenschlag seeks to explore effective ways of health care linking university medicine (Maximalversorgung | maximum care) with regional inpatient, outpatient, rehabilitative, and preventive care structures (Regionalversorgung | regional care). To create advantages for both (potential) patients and the involved cooperation partners, project b4 qualitatively assesses needs and motivations among professionals, population groups, and political stakeholders at individual and collective levels. Besides providing an overview of the project structure as well as of regional population and healthcare characteristics, the first results of qualitative interviews conducted with different health experts will be presented. Interviewed experts include managers of participating hospitals, nurses, medical specialists working in the hospital and registered doctors operating in practices in rural areas. At the end of the project life and based on the identified factors relevant to the success -and also for failure- of participatory cooperation in health care, the project aims at informing other districts embarking on similar systems-oriented and human-centered healthcare projects. Individuals’ health care needs in dependence on the social space in which they live will guide the development of recommendations.

Keywords: cross-sectoral collaboration in health care, human-centered health care, regional health care, individual and structural health conditions

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Authors: Ramazan Bakir, Gizem Kayar

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It is known that Alzheimer and Dementia are the two most common types of Neurodegenerative diseases and their visibility is getting accelerated for the last couple of years. As the population sees older ages all over the world, researchers expect to see the rate of this acceleration much higher. However, unfortunately, there is no known pharmacological cure for both, although some help to reduce the rate of cognitive decline speed. This is why we encounter with non-pharmacological treatment and tracking methods more for the last five years. Many researchers, including well-known associations and hospitals, lean towards using non-pharmacological methods to support cognitive function and improve the patient’s life quality. As the dementia symptoms related to mind, learning, memory, speaking, problem-solving, social abilities and daily activities gradually worsen over the years, many researchers know that cognitive support should start from the very beginning of the symptoms in order to slow down the decline. At this point, life of a patient and caregiver can be improved with some daily activities and applications. These activities include but not limited to basic word puzzles, daily cleaning activities, taking notes. Later, these activities and their results should be observed carefully and it is only possible during patient/caregiver and M.D. in-person meetings in hospitals. These meetings can be quite time-consuming, exhausting and financially ineffective for hospitals, medical doctors, caregivers and especially for patients. On the other hand, digital support systems are showing positive results for all stakeholders of healthcare systems. This can be observed in countries that started Telemedicine systems. The biggest potential of our system is setting the inter-user communication up in the best possible way. In our project, we propose Machine Learning based digitalization of validated traditional cognitive tests (e.g. MOCA, Afazi, left-right hemisphere), their analyses for high-quality follow-up and communication systems for all stakeholders. R. Bakir and G. Kayar are with Gefeasoft, Inc, R&D – Software Development and Health Technologies company. Emails: ramazan, gizem @ gefeasoft.com This platform has a high potential not only for patient tracking but also for making all stakeholders feel safe through all stages. As the registered hospitals assign corresponding medical doctors to the system, these MDs are able to register their own patients and assign special tasks for each patient. With our integrated machine learning support, MDs are able to track the failure and success rates of each patient and also see general averages among similarly progressed patients. In addition, our platform also supports multi-player technology which helps patients play with their caregivers so that they feel much safer at any point they are uncomfortable. By also gamifying the daily household activities, the patients will be able to repeat their social tasks and we will provide non-pharmacological reminiscence therapy (RT – life review therapy). All collected data will be mined by our data scientists and analyzed meaningfully. In addition, we will also add gamification modules for caregivers based on Naomi Feil’s Validation Therapy. Both are behaving positively to the patient and keeping yourself mentally healthy is important for caregivers. We aim to provide a therapy system based on gamification for them, too. When this project accomplishes all the above-written tasks, patients will have the chance to do many tasks at home remotely and MDs will be able to follow them up very effectively. We propose a complete platform and the whole project is both time and cost-effective for supporting all stakeholders.

Keywords: alzheimer’s, dementia, cognitive functionality, cognitive tests, serious games, machine learning, artificial intelligence, digitalization, non-pharmacological, data analysis, telemedicine, e-health, health-tech, gamification

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Authors: Ainur Sharip, Jose E. Perez, Nouf Alsharif, Aldo I. M. Bandeas, Enzo D. Fabrizio, Timothy Ravasi, Jasmeen S. Merzaban, Jürgen Kosel

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Bone marrow-derived human mesenchymal stem cells (MSCs) are attractive candidates for tissue engineering and regenerative medicine, due to their ability to differentiate into osteoblasts, chondrocytes or adipocytes. Differentiation is influenced by biochemical and biophysical stimuli provided by the microenvironment of the cell. Thus, altering the mechanical characteristics of a cell culture scaffold can directly influence a cell’s microenvironment and lead to stem cell differentiation. Mesenchymal stem cells were cultured on densely packed, vertically aligned magnetic iron nanowires (NWs) and the effect of NWs on the cell cytoskeleton rearrangement and differentiation were studied. An electrochemical deposition method was employed to fabricate NWs into nanoporous alumina templates, followed by a partial release to reveal the NW array. This created a cell growth substrate with free-standing NWs. The Fe NWs possessed a length of 2-3 µm, with each NW having a diameter of 33 nm on average. Mechanical stimuli generated by the physical movement of these iron NWs, in response to a magnetic field, can stimulate osteogenic differentiation. Induction of osteogenesis was estimated using an osteogenic marker, osteopontin, and a reduction of stem cell markers, CD73 and CD105. MSCs were grown on the NWs, and fluorescent microscopy was employed to monitor the expression of markers. A magnetic field with an intensity of 250 mT and a frequency of 0.1 Hz was applied for 12 hours/day over a period of one week and two weeks. The magnetically activated substrate enhanced the osteogenic differentiation of the MSCs compared to the culture conditions without magnetic field. Quantification of the osteopontin signal revealed approximately a seven-fold increase in the expression of this protein after two weeks of culture. Immunostaining staining against CD73 and CD105 revealed the expression of antibodies at the earlier time point (two days) and a considerable reduction after one-week exposure to a magnetic field. Overall, these results demonstrate the application of a magnetic NW substrate in stimulating the osteogenic differentiation of MSCs. This method significantly decreases the time needed to induce osteogenic differentiation compared to commercial biochemical methods, such as osteogenic differentiation kits, that usually require more than two weeks. Contact-free stimulation of MSC differentiation using a magnetic field has potential uses in tissue engineering, regenerative medicine, and bone formation therapies.

Keywords: cell substrate, magnetic nanowire, mesenchymal stem cell, stem cell differentiation

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Authors: Tukur Husaini Nahuche

Abstract:

The parent resources play a vital role in the life of the offspring. It help give children basic necessities of life like food, clothing, and housing. In a like manner financial assets allow parents to move into neighborhood with more affluent school systems, to pay school bills, purchase expensive technologies like personal computer, save money for tutoring books, magazines, journals, Newspapers etc. Making of proper provision in the home environment conducive for learning after school hours and creation of other outdoor activities for them are what necessitate in enhancing and accelerating children’s learning opportunities. Indeed, this paper intends to discuss parental investment in education, parent income resources, parental education, occupation, and income as relatively influencing children’s access to quality education. With the hope that families would provide equal opportunities for children irrespective of their sex, intelligence, subject choice,etc.

Keywords: parental investment, children's access, quality education

Procedia PDF Downloads 549

Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

Abstract:

We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

Procedia PDF Downloads 543

Authors: Hamoud Alshammari, Adnan Almezani, Hamdan Alshammari, Faris Alharbi

Abstract:

In this study we have examined of antimicrobial activity for unripe Juglan Regia phenolic extracts against a wide range of pathogenic microorganisms. Walnut (Juglans regia L.) is a member of Juglandaceae family used as a remedy in folk medicine. Leaves, barks, fruits and husk (peel) reported to harbor distinctive medical effect. In our study, we examined the anti-microbial effect against a set of gram positive and negative bacteria and even we have tested them against eukaryotic candida strains in a concentration gradual manner. Ethyl acetate extract of J. regia had the best antibacterial activity when compared with ciprofloxacin. The Minimum inhibition concentration for S. aureus, P. aerogenosa and S. epidermidis MIC was 0.85 mg/mL.

Keywords: antimicrobial, J. regia, S. aureus, phytochemistry

Procedia PDF Downloads 199

Authors: Slim Chokri

Abstract:

Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

Procedia PDF Downloads 434