Search results for: gravitational search algorithm

Authors: Kana Matsuyanagi

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Anorexia Nervosa (AN) is a psychiatric disorder characterized by a relentless pursuit of thinness and strict restriction of food. The current therapeutic approaches for AN predominantly revolve around outpatient psychotherapies, which create significant financial barriers for the majority of affected patients, hindering their access to treatment. Nonetheless, AN exhibit one of the highest mortality and relapse rates among psychological disorders, underscoring the urgent need to provide patients with an affordable self-treatment tool, enabling those unable to access conventional medical intervention to address their condition autonomously. To this end, a neurofeedback software, termed RelaxNeuron, was developed with the objective of providing an economical and portable means to aid individuals in self-managing AN. Electroencephalography (EEG) was chosen as the preferred modality for RelaxNeuron, as it aligns with the study's goal of supplying a cost-effective and convenient solution for addressing AN. The primary aim of the software is to ameliorate the negative emotional responses towards food stimuli and the accompanying aberrant eye-tracking patterns observed in AN patient, ultimately alleviating the profound fear towards food an elemental symptom and, conceivably, the fundamental etiology of AN. The core functionality of RelaxNeuron hinges on the acquisition and analysis of EEG signals, alongside an electrocardiogram (ECG) signal, to infer the user's emotional state while viewing dynamic food-related imagery on the screen. Moreover, the software quantifies the user's performance in accurately tracking the moving food image. Subsequently, these two parameters undergo further processing in the subsequent algorithm, informing the delivery of either negative or positive feedback to the user. Preliminary test results have exhibited promising outcomes, suggesting the potential advantages of employing RelaxNeuron in the treatment of AN, as evidenced by its capacity to enhance emotional regulation and attentional processing through repetitive and persistent therapeutic interventions.

Keywords: Anorexia Nervosa, fear conditioning, neurofeedback, BCI

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Authors: Xiaowei Zhang, Min Xu, Saeid Habibi, Fengjun Yan, Ryan Ahmed

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Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.

Keywords: lithium-ion batteries, genetic algorithm optimization, battery aging test, parameter identification

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Authors: Gulhan Sen

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Disasters could result in many deaths and injuries. In these difficult times, accessible resources are limited, demand and supply balance is distorted, and there is a need to make urgent interventions. Disproportionateness between accessible resources and intervention capacity makes triage a necessity in every stage of disaster response. Healthcare professionals, who are in charge of triage, have to evaluate swiftly and make ethical decisions about which patients need priority and urgent intervention given the limited available resources. For such critical times in disaster triage, 'doing the greatest good for the greatest number of casualties' is adopted as a code of practice. But there is no guide for healthcare professionals about ethical decision-making during disasters, and this study is expected to use as a source in the preparation of the guide. This study aimed to examine whether the qualities healthcare professionals in Izmir related to disaster triage were adequate and whether these qualities influence their capacity to make ethical decisions. The researcher used a survey developed for data collection. The survey included two parts. In part one, 14 questions solicited information about socio-demographic characteristics and knowledge levels of the respondents on ethical principles of disaster triage and allocation of scarce resources. Part two included four disaster scenarios adopted from existing literature and respondents were asked to make ethical decisions in triage based on the provided scenarios. The survey was completed by 215 healthcare professional working in Emergency-Medical Stations, National Medical Rescue Teams and Search-Rescue-Health Teams in Izmir. The data was analyzed with SPSS software. Chi-Square Test, Mann-Whitney U Test, Kruskal-Wallis Test and Linear Regression Analysis were utilized. According to results, it was determined that 51.2% of the participants had inadequate knowledge level of ethical principles of disaster triage and allocation of scarce resources. It was also found that participants did not tend to make ethical decisions on four disaster scenarios which included ethical dilemmas. They stayed in ethical dilemmas that perform cardio-pulmonary resuscitation, manage limited resources and make decisions to die. Results also showed that participants who had more experience in disaster triage teams, were more likely to make ethical decisions on disaster triage than those with little or no experience in disaster triage teams(p < 0.01). Moreover, as their knowledge level of ethical principles of disaster triage and allocation of scarce resources increased, their tendency to make ethical decisions also increased(p < 0.001). In conclusion, having inadequate knowledge level of ethical principles and being inexperienced affect their ethical decision-making during disasters. So results of this study suggest that more training on disaster triage should be provided on the areas of the pre-impact phase of disaster. In addition, ethical dimension of disaster triage should be included in the syllabi of the ethics classes in the vocational training for healthcare professionals. Drill, simulations, and board exercises can be used to improve ethical decision making abilities of healthcare professionals. Disaster scenarios where ethical dilemmas are faced should be prepared for such applied training programs.

Keywords: disaster triage, medical ethics, ethical principles of disaster triage, ethical decision-making

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Authors: E Tolufase

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The demand, high costs and health implications of using energy derived from hydrocarbon compound have necessitated the continuous search for alternative source of energy. The World energy market is facing some challenges viz: depletion of fossil fuel reserves, population explosion, lack of energy security, economic and urbanization growth and also, in Nigeria some rural areas still depend largely on wood, charcoal, kerosene, petrol among others, as the sources of their energy. To overcome these short falls in energy supply and demand, as well as taking into consideration the risks from global climate change due to effect of greenhouse gas emissions and other pollutants from fossil fuels’ combustion, brought a lot of attention on efficiently harnessing the renewable energy sources. A very promising among the renewable energy resources for a clean energy technology for power production, vehicle and domestic usage is biogas. Therefore, optimization of biogas yield and quality is imperative. Hence, this study investigated yield and quality of biogas using low cost bio-digester and combination of various feed stocks referred to as co-digestion. Batch/Discontinuous Bio-digester type was used because it was cheap, easy, plausible and appropriate for different substrates used to get the desired results. Three substrates were used; cow dung, chicken droppings and lemon grass digested in five separate 21 litre digesters, A, B, C, D, and E and the gas collection system was designed using locally available materials. For single digestion we had; cow dung, chicken droppings, lemon grass, in Bio-digesters A, B, and C respectively, the co-digested three substrates in different mixed ratio 7:1:2 in digester D and E in ratio 5:3:2. The respective feed-stocks materials were collected locally, digested and analyzed in accordance with standard procedures. They were pre-fermented for a period of 10 days before being introduced into the digesters. They were digested for a retention period of 28 days, the physiochemical parameters namely; pressure, temperature, pH, volume of the gas collector system and volume of biogas produced were all closely monitored and recorded daily. The values of pH and temperature ranged 6.0 - 8.0, and 220C- 350C respectively. For the single substrate, bio-digester A(Cow dung only) produced biogas of total volume 0.1607m3(average volume of 0.0054m3 daily),while B (Chicken droppings ) produced 0.1722m3 (average of 0.0057m3 daily) and C (lemon grass) produced 0.1035m3 (average of 0.0035m3 daily). For the co-digested substrates in bio-digester D the total biogas produced was 0.2007m³ (average volume of 0.0067m³ daily) and bio-digester E produced 0.1991m³ (average volume of 0.0066m³ daily) It’s obvious from the results, that combining different substrates gave higher yields than when a singular feed stock was used and also mixing ratio played some roles in the yield improvement. Bio-digesters D and E contained the same substrates but mixed with different ratios, but higher yield was noticed in D with mixing ratio of 7:1:2 than in E with ratio 5:3:2.Therefore, co-digestion of substrates and mixing proportions are important factors for biogas production optimization.

Keywords: anaerobic, batch, biogas, biodigester, digestion, fermentation, optimization

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Authors: A. Kachmazova, A. Shadiev, Y. Teterin, P. Yartcev

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Introduction: Biliary calculi disease (LCS) still occupies the leading position among urgent diseases of the abdominal cavity, manifesting itself from asymptomatic course to life-threatening states. Nowadays arsenal of diagnostic methods for choledocholithiasis is quite wide: ultrasound, hepatobiliscintigraphy (HBSG), magnetic resonance imaging (MRI), endoscopic retrograde cholangiography (ERCP). Among them, transabdominal ultrasound (TA ultrasound) is the most accessible and routine diagnostic method. Nowadays ERCG is the "gold" standard in diagnosis and one-stage treatment of biliary tract obstruction. However, transpapillary techniques are accompanied by serious postoperative complications (postmanipulative pancreatitis (3-5%), endoscopic papillosphincterotomy bleeding (2%), cholangitis (1%)), the lethality being 0.4%. GBSG and MRI are also quite informative methods in the diagnosis of choledocholithiasis. Small size of concrements, their localization in intrapancreatic and retroduodenal part of common bile duct significantly reduces informativity of all diagnostic methods described above, that demands additional studying of this problem. Materials and Methods: 890 patients with the diagnosis of cholelithiasis (calculous cholecystitis) were admitted to the Sklifosovsky Scientific Research Institute of Hospital Medicine in the period from August, 2020 to June, 2021. Of them 115 people with mechanical jaundice caused by concrements in bile ducts. Results: Final EUS diagnosis was made in all patients (100,0%). In all patients in whom choledocholithiasis diagnosis was revealed or confirmed after EUS, ERCP was performed urgently (within two days from the moment of its detection) as the X-ray operation room was provided; it confirmed the presence of concrements. All stones were removed by lithoextraction using Dormia basket. The postoperative period in these patients had no complications. Conclusions: EUS is the most informative and safe diagnostic method, which allows to detect choledocholithiasis in patients with discrepancies between clinical-laboratory and instrumental methods of diagnosis in shortest time, that in its turn will help to decide promptly on the further tactics of patient treatment. We consider it reasonable to include EUS in the diagnostic algorithm for choledocholithiasis. Disclosure: Nothing to disclose.

Keywords: endoscopic ultrasonography, choledocholithiasis, common bile duct, concrement, ERCP

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Authors: Ravi Ande, Mousumi Hazari

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One of the common geophysical techniques for mapping subsurface geo-electrical structures, extensive hydro-geological research, and engineering and environmental geophysics applications is the use of time domain electromagnetic (TDEM)/transient electromagnetic (TEM) soundings. A large transmitter loop for energising the ground and a small receiver loop or magnetometer for recording the transient voltage or magnetic field in the air or on the surface of the earth, with the receiver at the center of the loop or at any random point inside or outside the source loop, make up a large loop TEM system. In general, one can acquire data using one of the configurations with a large loop source, namely, with the receiver at the center point of the loop (central loop method), at an arbitrary in-loop point (in-loop method), coincident with the transmitter loop (coincidence-loop method), and at an arbitrary offset loop point (offset-loop method), respectively. Because of the mathematical simplicity associated with the expressions of EM fields, as compared to the in-loop and offset-loop systems, the central loop system (for ground surveys) and coincident loop system (for ground as well as airborne surveys) have been developed and used extensively for the exploration of mineral and geothermal resources, for mapping contaminated groundwater caused by hazardous waste and thickness of permafrost layer. Because a proper analytical expression for the TEM response over the layered earth model for the large loop TEM system does not exist, the forward problem used in this inversion scheme is first formulated in the frequency domain and then it is transformed in the time domain using Fourier cosine or sine transforms. Using the EMLCLLER algorithm, the forward computation is initially carried out in the frequency domain. As a result, the EMLCLLER modified the forward calculation scheme in NLSTCI to compute frequency domain answers before converting them to the time domain using Fourier Cosine and/or Sine transforms.

Keywords: time domain electromagnetic (TDEM), TEM system, geoelectrical sounding structure, Fourier cosine

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Authors: Neha Devi, P. K. Joshi

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Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. One of the most significant forms of global disturbance, impacting community dynamics, biogeochemical cycles and local and regional climate across a wide range of ecosystems ranging from boreal forests to tropical rainforest is wildfire Assessment of fire danger is a function of forest type, fuelwood stock volume, moisture content, degree of senescence and fire management strategy adopted in the ground. Remote sensing has potential of reduction the uncertainty in mapping fuels. Hyperspectral remote sensing is emerging to be a very promising technology for wildfire fuels characterization. Fine spectral information also facilitates mapping of biophysical and chemical information that is directly related to the quality of forest fire fuels including above ground live biomass, canopy moisture, etc. We used Hyperion imagery acquired in February, 2016 and analysed four fuel characteristics using Hyperion sensor data on-board EO-1 satellite, acquired over the Shiwalik Himalayas covering the area of Champawat, Uttarakhand state. The main objective of this study was to present an overview of methodologies for mapping fuel properties using hyperspectral remote sensing data. Fuel characteristics analysed include fuel biomass, fuel moisture, and fuel condition and fuel type. Fuel moisture and fuel biomass were assessed through the expression of the liquid water bands. Fuel condition and type was assessed using green vegetation, non-photosynthetic vegetation and soil as Endmember for spectral mixture analysis. Linear Spectral Unmixing, a partial spectral unmixing algorithm, was used to identify the spectral abundance of green vegetation, non-photosynthetic vegetation and soil.

Keywords: forest fire fuel, Hyperion, hyperspectral, linear spectral unmixing, spectral mixture analysis

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Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

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Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Samar Swaid

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Today, corporates are moving toward the adoption of Design-Thinking techniques to develop products and services, putting their consumer as the heart of the development process. One of the leading companies in Design-Thinking, IDEO (Innovation, Design, Engineering Organization), defines Design-Thinking as an approach to problem-solving that relies on a set of multi-layered skills, processes, and mindsets that help people generate novel solutions to problems. Design thinking may result in new ideas, narratives, objects or systems. It is about redesigning systems, organizations, infrastructures, processes, and solutions in an innovative fashion based on the users' feedback. Tim Brown, president and CEO of IDEO, sees design thinking as a human-centered approach that draws from the designer's toolkit to integrate people's needs, innovative technologies, and business requirements. The application of design thinking has been witnessed to be the road to developing innovative applications, interactive systems, scientific software, healthcare application, and even to utilizing Design-Thinking to re-think business operations, as in the case of Airbnb. Recently, there has been a movement to apply design thinking to machine learning and artificial intelligence to ensure creating the "wow" effect on consumers. The Association of Computing Machinery task force on Data Science program states that" Data scientists should be able to implement and understand algorithms for data collection and analysis. They should understand the time and space considerations of algorithms. They should follow good design principles developing software, understanding the importance of those principles for testability and maintainability" However, this definition hides the user behind the machine who works on data preparation, algorithm selection and model interpretation. Thus, the Data Science program includes design thinking to ensure meeting the user demands, generating more usable machine learning tools, and developing ways of framing computational thinking. Here, describe the fundamentals of Design-Thinking and teaching modules for data science programs.

Keywords: data science, design thinking, AI, currculum, transformation

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Authors: Debasish Nandy

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India’s foreign policy towards all of her neighbor countries is determinate on the basis of multi-dimensional factors. India’s relations with its South Asian neighbor can be classified into three categories. In the first category, there are four countries -Sri Lanka, Bangladesh, Nepal, and Afghanistan- whose bilateral relationships have encompassed cooperation, irritants, problems and crisis at different points in time. With Pakistan, the relationship has been perpetually adversarial. The third category includes Bhutan and Maldives whose relations are marked by friendship and cooperation, free of any bilateral problems. It is needless to say that Jawaharlal Nehru emphasized on friendly relations with the neighboring countries. The subsequent Prime Ministers of India especially I.K. Gujral had advocated in making of peaceful and friendly relations with the subcontinental countries. He had given a unique idea to foster bilateral relations with the neighbors. His idea is known as ‘Gujral Doctrine’. A dramatical change has been witnessed in Indian foreign policy since 1991.In the post-Cold War period, India’s national security has been vehemently threatened by terrorism, which originated from Pakistan-Afghanistan and partly Bangladesh. India has required a cooperative security, which can be made by mutual understanding among the South Asian countries. Additionally, the countries of South Asia need to evolve the concept of ‘Cooperative Security’ to explain the underlying logic of regional cooperation. According to C. Rajamohan, ‘cooperative security could be understood, as policies of governments, which see themselves as former adversaries or potential adversaries to shift from or avoid confrontationist policies.’ A cooperative security essentially reflects a policy of dealing peacefully with conflicts, not merely by abstention from violence or threats but by active engagement in negotiation, a search for practical solutions and with a commitment to preventive measures. Cooperative assumes the existence of a condition in which the two sides possess the military capabilities to harm each other. Establishing cooperative security runs into a complex process building confidence. South Asian nations often engaged with hostility to each other. Extra-regional powers have been influencing their powers in this region since a long time. South Asian nations are busy to purchase military equipment. In spite of weakened economic systems, these states are spending a huge amount of money for their security. India is the big power in this region in every aspect. The big states- small states syndrome is a negative factor in this respect. However, India will have to an initiative to extended ‘track II diplomacy’ or soft diplomacy for its security as well as the security of this region.Confidence building measures could help rejuvenate not only SAARC but also build trust and mutual confidence between India and its neighbors in South Asia. In this paper, I will focus on different aspects of India’s policy towards it, South-Asian neighbors. It will also be searched that how India is dealing with these countries by using a mixed type of diplomacy – both idealistic and realistic points of view. Security and cooperation are two major determinants of India’s foreign policy towards its South Asian neighbors.

Keywords: bilateral, diplomacy, infiltration, terrorism

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Authors: Dmitriy Yu. Korulkin, Raissa A. Muzychkina

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The last decade the growth of severe and disseminated forms of inflammatory diseases is observed in Kazakhstan, in particular, septic shock, which progresses on 3-15% of patients with infectious complications of postnatal period. In terms of the rate of occurrence septic shock takes third place after hemorrhagic and cardiovascular shock, in terms of lethality it takes first place. The structure of obstetric sepsis has significantly changed. Currently the first place is taken by postabortive sepsis (40%) that is connected with usage of imperfect methods of artificial termination of pregnancy in late periods (intraamnial injection of sodium chloride, glucose). The second place is taken by postnatal sepsis (32%); the last place is taken by septic complications of caesarean section (28%). In this connection, search for and assessment of effectiveness of new medicines for treatment of postoperative infectious complications, having biostimulating effect and speeding up regeneration processes, is very promising and topical. Essential oil was obtained by the method hydrodistillation air-dry aerial part of Sedum L. plants using Clevenger apparatus. Pilot batch of plant medicinal product based on Sedum essential oils was produced by Chimpharm JSC, Santo Member of Polpharma Group (Kazakhstan). During clinical test of the plant medicinal product based on Sedum L. essential oils 37 female patients at the age from 35 to 57 with clinical signs of complicated postoperative processes and 12 new mothers with clinical signs of inflammatory process on sutures on anterior abdominal wall after caesarean section and partial disruption of surgical suture line on perineum were examined. Medicine usage methods - surgical wound treatment 2 times a day, treatment with other medicines of local action was not performed. Before and after treatment general clinical test, determination of immune status, bacterioscopic test of wound fluid was performed to all women, medical history data was taken into account, wound cleansing and healing time, full granulations, side effects and complications, satisfaction with the used medicine was assessed. On female patients with inflammatory infiltration and partial disruption of surgical suture line anesthetic wound healing effect of plant medicinal product based on Sedum L. essential oils was observed as early as on the second day after beginning of using it, wound cleansing took place, as a rule, within the first row days. Hyperemia in the area of suture line also was not observed for 2-3-d day of usage of medicine, good constant course was observed. The absence of clinical effect on this group of patients was not registered. The represented data give evidence of that clinical effect was accompanied with normalization of changed laboratory findings. No allergic responses or side effects were observed during usage of the plant medicinal products based on Sedum L. essential oils.

Keywords: antiinflammatory, bioactive substances, essential oils, isolation, sedum L., wound healing

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Authors: Laura Alvarez-Alonso, Francisco Garcia-Carro, Jorge Loredo

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Population growth and industrial development imply an increase in the energy demands and the problems caused by emissions of greenhouse effect gases, which has inspired the search for clean sources of energy. Hydrogen (H₂) is expected to play a key role in the world’s energy future by replacing fossil fuels. The properties of H₂ make it a green fuel that does not generate pollutants and supplies sufficient energy for power generation, transportation, and other applications. Supercritical Water Gasification (SCWG) represents an attractive alternative for the recovery of energy from wastes. SCWG allows conversion of a wide range of raw materials into a fuel gas with a high content of hydrogen and light hydrocarbons through their treatment at conditions higher than those that define the critical point of water (temperature of 374°C and pressure of 221 bar). Methane used as a transport fuel is another important gasification product. The number of different uses of gas and energy forms that can be produced depending on the kind of material gasified and type of technology used to process it, shows the flexibility of SCWG. This feature allows it to be integrated with several industrial processes, as well as power generation systems or waste-to-energy production systems. The final aim of this work is to study which conditions and equipment are the most efficient and advantageous to explore the possibilities to obtain streams rich in H₂ from oily wastes, which represent a major problem both for the environment and human health throughout the world. In this paper, the relative complexity of technology needed for feasible gasification process cycles is discussed with particular reference to the different feedstocks that can be used as raw material, different reactors, and energy recovery systems. For this purpose, a review of the current status of SCWG technologies has been carried out, by means of different classifications based on key features as the feed treated or the type of reactor and other apparatus. This analysis allows to improve the technology efficiency through the study of model calculations and its comparison with experimental data, the establishment of kinetics for chemical reactions, the analysis of how the main reaction parameters affect the yield and composition of products, or the determination of the most common problems and risks that can occur. The results of this work show that SCWG is a promising method for the production of both hydrogen and methane. The most significant choices of design are the reactor type and process cycle, which can be conveniently adopted according to waste characteristics. Regarding the future of the technology, the design of SCWG plants is still to be optimized to include energy recovery systems in order to reduce costs of equipment and operation derived from the high temperature and pressure conditions that are necessary to convert water to the SC state, as well as to find solutions to remove corrosion and clogging of components of the reactor.

Keywords: hydrogen production, organic wastes, supercritical water gasification, system integration, waste-to-energy

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Authors: Benjamin K. Ghansah, Richard K. Appoh, Iliya Nababa, Eric K. Forkuo

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The Nasia River is an important source of water for domestic and agricultural purposes to the inhabitants of its catchment. Major farming activities takes place within the floodplain of the river and its network of tributaries. The actual inundation extent of the river system is; however, unknown. Reasons for this lack of information include financial constraints and inadequate human resources as flood modelling is becoming increasingly complex by the day. Knowledge of the inundation extent will help in the assessment of risk posed by the annual flooding of the river, and help in the planning of flood recession agricultural activities. This study used a simple terrain based algorithm, Height Above Nearest Drainage (HAND), to delineate the floodplain of the Nasia River and its tributaries. The HAND model is a drainage normalized digital elevation model, which has its height reference based on the local drainage systems rather than the average mean sea level (AMSL). The underlying principle guiding the development of the HAND model is that hillslope flow paths behave differently when the reference gradient is to the local drainage network as compared to the seaward gradient. The new terrain model of the catchment was created using the NASA’s SRTM Digital Elevation Model (DEM) 30m as the only data input. Contours (HAND Contour) were then generated from the normalized DEM. Based on field flood inundation survey, historical information of flooding of the area as well as satellite images, a HAND Contour of 2m was found to best correlates with the flood inundation extent of the river and its tributaries. A percentage accuracy of 75% was obtained when the surface area created by the 2m contour was compared with surface area of the floodplain computed from a satellite image captured during the peak flooding season in September 2016. It was estimated that the flooding of the Nasia River and its tributaries created a floodplain area of 1011 km².

Keywords: digital elevation model, floodplain, HAND contour, inundation extent, Nasia River

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Authors: Monika Soni, Shovonlal Bhowmick, Chandra Bhattacharya, Jitendra Sharma, Prafulla Dutta, Jagadish Mahanta

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Aedes aegypti and Aedes albopictus are considered as two major vectors to transmit dengue virus. In North-east India, two states viz. Assam and Arunachal Pradesh are known to be high endemic zone for dengue and Chikungunya viral infection. The taxonomical classification of medically important vectors are important for mapping of actual evolutionary trends and epidemiological studies. However, misidentification of mosquito species in field-collected mosquito specimens could have a negative impact which may affect vector-borne disease control policy. DNA barcoding is a prominent method to record available species, differentiate from new addition and change of population structure. In this study, a combined approach of a morphological and molecular technique of DNA barcoding was adopted to explore sequence variation in mitochondrial cytochrome c oxidase subunit I (COI) gene within dengue vectors. The study has revealed the map distribution of the dengue vector from two states i.e. Assam and Arunachal Pradesh, India. Approximate five hundred mosquito specimens were collected from different parts of two states, and their morphological features were compared with the taxonomic keys. The analysis of detailed taxonomic study revealed identification of two species Aedes aegypti and Aedes albopictus. The species aegypti comprised of 66.6% of the specimen and represented as dominant dengue vector species. The sequences obtained through standard DNA barcoding protocol were compared with public databases, viz. GenBank and BOLD. The sequences of all Aedes albopictus have shown 100% similarity whereas sequence of Aedes aegypti has shown 99.77 - 100% similarity of COI gene with that of different geographically located same species based on BOLD database search. From dengue prevalent different geographical regions fifty-nine sequences were retrieved from NCBI and BOLD databases of the same and related taxa to determine the evolutionary distance model based on the phylogenetic analysis. Neighbor-Joining (NJ) and Maximum Likelihood (ML) phylogenetic tree was constructed in MEGA6.06 software with 1000 bootstrap replicates using Kimura-2-Parameter model. Data were analyzed for sequence divergence and found that intraspecific divergence ranged from 0.0 to 2.0% and interspecific divergence ranged from 11.0 to 12.0%. The transitional and transversional substitutions were tested individually. The sequences were deposited in NCBI: GenBank database. This observation claimed the first DNA barcoding analysis of Aedes mosquitoes from North-eastern states in India and also confirmed the range expansion of two important mosquito species. Overall, this study insight into the molecular ecology of the dengue vectors from North-eastern India which will enhance the understanding to improve the existing entomological surveillance and vector incrimination program.

Keywords: COI, dengue vectors, DNA barcoding, molecular identification, North-east India, phylogenetics

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

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Authors: J. A. Sawicki, C. Ebrahimi

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The first geochemical, petrological, X-ray diffraction, Raman, Mössbauer, and oxygen isotopic analyses of very intriguing 13-kg Sooke #1 stone covered in 70% of its surface with black fusion crust, found in and recovered from Sooke Basin, near Juan de Fuca Strait, in British Columbia, were reported as poster #2775 at LPSC52 in March. Our further analyses reported in poster #6305 at 84AMMS in August and comparisons with the Mössbauer spectra of Martian meteorite MIL03346 and Martian rocks in Gusev Crater reported by Morris et al. suggest that Sooke #1 find could be a stony achondrite of Martian polymict breccia type ejected from early watery Mars. Here, the Raman spectra of a carbon-rich ~1-mm² fossil-like white area identified in this rock on a surface of polished cut have been examined in more detail. The low-intensity 532 nm and 633 nm beams of the InviaRenishaw microscope were used to avoid any destructive effects. The beam was focused through the microscope objective to a 2 m spot on a sample, and backscattered light collected through this objective was recorded with CCD detector. Raman spectra of dark areas outside fossil have shown bands of clinopyroxene at 320, 660, and 1020 cm-1 and small peaks of forsteritic olivine at 820-840 cm-1, in agreement with results of X-ray diffraction and Mössbauer analyses. Raman spectra of the white area showed the broad band D at ~1310 cm-1 consisting of main mode A1g at 1305 cm⁻¹, E2g mode at 1245 cm⁻¹, and E1g mode at 1355 cm⁻¹ due to stretching diamond-like sp3 bonds in diamond polytype lonsdaleite, as in Ovsyuk et al. study. The band near 1600 cm-1 mostly consists of D2 band at 1620 cm-1 and not of the narrower G band at 1583 cm⁻¹ due to E2g stretching in planar sp2 bonds that are fundamental building blocks of carbon allotropes graphite and graphene. In addition, the broad second-order Raman bands were observed with 532 nm beam at 2150, ~2340, ~2500, 2650, 2800, 2970, 3140, and ~3300 cm⁻¹ shifts. Second-order bands in diamond and other carbon structures are ascribed to the combinations of bands observed in the first-order region: here 2650 cm⁻¹ as 2D, 2970 cm⁻¹ as D+G, and 3140 cm⁻¹ as 2G ones. Nanodiamonds are abundant in the Universe, found in meteorites, interplanetary dust particles, comets, and carbon-rich stars. The diamonds in meteorites are presently intensely investigated using Raman spectroscopy. Such particles can be formed by CVD process and during major impact shocks at ~1000-2300 K and ~30-40 GPa. It cannot be excluded that the fossil discovered in Sooke #1 could be a remnant of an alien carbon organism that transformed under shock impact to nanodiamonds. We trust that for the benefit of research in astro-bio-geology of meteorites, asteroids, Martian rocks, and soil, this find deserves further, more thorough investigations. If possible, the Raman SHERLOCK spectrometer operating on the Perseverance Rover should also search for such objects in the Martian rocks.

Keywords: achondrite, nanodiamonds, lonsdaleite, raman spectra

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Authors: Harriet Koshie Lamptey, Richard Boateng

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Mobile learning (m-learning) is a novel approach to knowledge acquisition and dissemination and is gaining global attention. Steady progress in wireless technologies and the portability of communication devices continue to broaden the scope and use of mobiles. With the convergence of Web functionality onto mobile platforms and the affordability and availability of mobile technology, m-learning has the potential of being the next prevalent channel of education in both formal and informal settings. There is substantive literature on developed countries but the state in developing countries (DCs) however appears vague. This paper is a synthesis of extant literature on mobile learning in DCs. The research interest is based on the fact that in DCs, mobile communication and internet connectivity are popular. However, its use in education is under explored. There are some reviews on the state, conceptualizations, trends and teacher education, but to the authors’ knowledge, no study has focused on mobile learning adoption and integration issues. This study examines issues and gaps associated with its adoption and integration in DCs higher education institutions. A qualitative build-up of literature was conducted using articles pooled from electronic databases (Google Scholar and ERIC). To enable criteria for inclusion and incorporate diverse study perspectives, search terms used were m-learning, DCs, higher education institutions, challenges, benefits, impact, gaps and issues. The synthesis revealed that though mobile technology has diffused globally, its pedagogical pursuit in DCs remains quite low. The absence of a mobile Web and the difficulty of resource conversion into mobile format due to lack of funding and technical competence is a stumbling block. Again, the lack of established design and implementation rules to guide the development of m-learning platforms in DCs is a hindrance. The absence of access restrictions on devices poses security threats to institutional systems. Negative perceptions that devices are taking over faculty roles lead to resistance in some situations. Resistance to change can be a hindrance to the acceptance and success of new systems. Lack of interest for m-learning is also attributed to lower technological literacy levels of the underprivileged masses. Scholarly works on m-learning in DCs is yet to mature. Most technological innovations are handed down from developed countries, and this constantly creates a lag for DCs. Lack of theoretical grounding was also identified which reduces the objectivity of study reports. The socio-cultural terrain of DCs results in societies with different views and needs that have been identified as a hindrance to research. Institutional commitment decisions, adequate funding for the necessary infrastructural development as well as multiple stakeholder participation is important for project success. Evidence suggests that while adoption decisions are readily made, successful integration of the concept for its full benefits to be realized is often neglected. Recommendations to findings were made to provide possible remedies to identified issues.

Keywords: developing countries, higher education institutions, mobile learning, literature review

Procedia PDF Downloads 225

Authors: María Dolores Caro, Eugenio M. Fedriani, Ángel F. Tenorio

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The transportation of dangerous goods by lorries throughout Europe must be done by using the roads conforming the European Road Transport Network. In this network, there are several parking areas where lorry drivers can park to rest according to the regulations. According to the "European Agreement concerning the International Carriage of Dangerous Goods by Road", parking areas where lorries transporting dangerous goods can park to rest, must follow several security stipulations to keep safe the rest of road users. At this respect, these lorries must be parked in adapted areas with strict and permanent surveillance measures. Moreover, drivers must satisfy several restrictions about resting and driving time. Under these facts, one may expect that there exist enough parking areas for the transport of this type of goods in order to obey the regulations prescribed by the European Union and its member countries. However, the already-existing parking areas are not sufficient to cover all the stops required by drivers transporting dangerous goods. Our main goal is, starting from the already-existing parking areas and the loading-and-unloading location, to provide an optimal answer to the following question: how many additional parking areas must be built and where must they be located to assure that lorry drivers can transport dangerous goods following all the stipulations about security and safety for their stops? The sense of the word “optimal” is due to the fact that we give a global solution for the location of parking areas throughout the whole European Road Transport Network, adjusting the number of additional areas to be as lower as possible. To do so, we have modeled the problem using graph theory since we are working with a road network. As nodes, we have considered the locations of each already-existing parking area, each loading-and-unloading area each road bifurcation. Each road connecting two nodes is considered as an edge in the graph whose weight corresponds to the distance between both nodes in the edge. By applying a new efficient algorithm, we have found the additional nodes for the network representing the new parking areas adapted for dangerous goods, under the fact that the distance between two parking areas must be less than or equal to 400 km.

Keywords: trans-european transport network, dangerous goods, parking areas, graph-based modeling

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Authors: Sungho Kim, Jaechoul Shin, Yujin Baek, Nakseok Kim, Kyungnam Kim, Shinhaeng Jo

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The climate change and increase in heavy traffic have been accelerating damages that cause the problems such as pothole on asphalt pavement. Pothole causes traffic accidents, vehicle damages, road casualties and traffic congestion. A quick and efficient maintenance method is needed because pothole is caused by stripping and accelerates pavement distress. In this study, we propose a rapid and systematic pothole management by developing a pothole automated repairing equipment including a volume measurement system of pothole. Three kinds of cold mix asphalt mixture were investigated to select repair materials. The materials were evaluated for satisfaction with quality standard and applicability to automated equipment. The volume measurement system of potholes was composed of multi-sensor that are combined with laser sensor and ultrasonic sensor and installed in front and side of the automated repair equipment. An algorithm was proposed to calculate the amount of repair material according to the measured pothole volume, and the system for releasing the correct amount of material was developed. Field test results showed that the loss of repair material amount could be reduced from approximately 20% to 6% per one point of pothole. Pothole rapid automated repair equipment will contribute to improvement on quality and efficient and economical maintenance by not only reducing materials and resources but also calculating appropriate materials. Through field application, it is possible to improve the accuracy of pothole volume measurement, to correct the calculation of material amount, and to manage the pothole data of roads, thereby enabling more efficient pavement maintenance management. Acknowledgment: The author would like to thank the MOLIT(Ministry of Land, Infrastructure, and Transport). This work was carried out through the project funded by the MOLIT. The project name is 'development of 20mm grade for road surface detecting roadway condition and rapid detection automation system for removal of pothole'.

Keywords: automated equipment, management, multi-beam sensor, pothole

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Lucas Nascimento, Ana Rita, Margarida Soares, André Ribeiro, Zlatina Genisheva, Hugo Silva, Joana Carvalho

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The footwear industry is an essential fashion industry, focusing on producing various types of footwear, such as shoes, boots, sandals, sneakers, and slippers. Global footwear consumption has doubled every 20 years since the 1950s. It is estimated that in 1950, each person consumed one new pair of shoes yearly; by 2005, over 20 billion pairs of shoes were consumed. To meet global footwear demand, production reached $24.2 billion, equivalent to about $74 per person in the United States. This means three new pairs of shoes per person worldwide. The issue of footwear waste is related to the fact that shoe production can generate a large amount of waste, much of which is difficult to recycle or reuse. This waste includes scraps of leather, fabric, rubber, plastics, toxic chemicals, and other materials. The search for alternative solutions for waste treatment and valorization is increasingly relevant in the current context, mainly when focused on utilizing waste as a source of substitute materials. From the perspective of the new circular economy paradigm, this approach is of utmost importance as it aims to preserve natural resources and minimize the environmental impact associated with sending waste to landfills. In this sense, the incorporation of waste into industrial sectors that allow for the recovery of large volumes, such as road construction, becomes an urgent and necessary solution from an environmental standpoint. This study explores the use of plastic waste from the footwear industry as a substitute for virgin polymers in bitumen modification, a solution that presents a more sustainable future. Replacing conventional polymers with plastic waste in asphalt composition reduces the amount of waste sent to landfills and offers an opportunity to extend the lifespan of road infrastructures. By incorporating waste into construction materials, reducing the consumption of natural resources and the emission of pollutants is possible, promoting a more circular and efficient economy. In the initial phase of this study, waste materials from end-of-life footwear were selected, and plastic waste with the highest potential for application was separated. Based on a literature review, EVA (ethylene vinyl acetate) and PU (polyurethane) were identified as the polymers suitable for modifying 50/70 classification bitumen. Each polymer was analysed at concentrations of 3% and 5%. The production process involved the polymer's fragmentation to a size of 4 millimetres after heating the materials to 180 ºC and mixing for 10 minutes at low speed. After was mixed for 30 minutes in a high-speed mixer. The tests included penetration, softening point, viscosity, and rheological assessments. With the results obtained from the tests, the mixtures with EVA demonstrated better results than those with PU, as EVA had more resistance to temperature, a better viscosity curve and a greater elastic recovery in rheology.

Keywords: footwear waste, hot asphalt pavement, modified bitumen, polymers

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Authors: Dewi Retno Sari Saputro, Purnami Widyaningsih, Hendrika Handayani

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Extreme data on an observation can occur due to unusual circumstances in the observation. The data can provide important information that can’t be provided by other data so that its existence needs to be further investigated. The method for obtaining extreme data is one of them using maxima block method. The distribution of extreme data sets taken with the maxima block method is called the distribution of extreme values. Distribution of extreme values is Gumbel distribution with two parameters. The parameter estimation of Gumbel distribution with maximum likelihood method (ML) is difficult to determine its exact value so that it is necessary to solve the approach. The purpose of this study was to determine the parameter estimation of Gumbel distribution with quasi-Newton BFGS method. The quasi-Newton BFGS method is a numerical method used for nonlinear function optimization without constraint so that the method can be used for parameter estimation from Gumbel distribution whose distribution function is in the form of exponential doubel function. The quasi-New BFGS method is a development of the Newton method. The Newton method uses the second derivative to calculate the parameter value changes on each iteration. Newton's method is then modified with the addition of a step length to provide a guarantee of convergence when the second derivative requires complex calculations. In the quasi-Newton BFGS method, Newton's method is modified by updating both derivatives on each iteration. The parameter estimation of the Gumbel distribution by a numerical approach using the quasi-Newton BFGS method is done by calculating the parameter values that make the distribution function maximum. In this method, we need gradient vector and hessian matrix. This research is a theory research and application by studying several journals and textbooks. The results of this study obtained the quasi-Newton BFGS algorithm and estimation of Gumbel distribution parameters. The estimation method is then applied to daily rainfall data in Purworejo District to estimate the distribution parameters. This indicates that the high rainfall that occurred in Purworejo District decreased its intensity and the range of rainfall that occurred decreased.

Keywords: parameter estimation, Gumbel distribution, maximum likelihood, broyden fletcher goldfarb shanno (BFGS)quasi newton

Procedia PDF Downloads 326

Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

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Authors: Ruairi J. McMillan

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Introduction: This critical analysis aims to ascertain how well neuroanatomical aetiologies are communicated within 20 case reports of aphasia. Neuroanatomical visualisations based on dissected brain specimens were produced and combined with white matter tract and vascular taxonomies of function in order to address the most consistently underreported features found within the aphasic case study reports. Together, these approaches are intended to integrate aphasiological knowledge from the past 20 years with aphasiological diagnostics, and to act as prototypal resources for both researchers and clinical professionals. The medico-legal precedent for aphasia diagnostics under Canadian, US and UK case law and the neuroimaging/neurological diagnostics relative to the functional capacity of aphasic patients are discussed in relation to the major findings of the literary analysis, neuroimaging protocols in clinical use today, and the neuroanatomical aetiologies of different aphasias. Basic Methodology: Literature searches of relevant scientific databases (e.g, OVID medline) were carried out using search terms such as aphasia case study (year) & stroke induced aphasia case study. A series of 7 diagnostic reporting criteria were formulated, and the resulting case studies were scored / 7 alongside clinical stroke criteria. In order to focus on the diagnostic assessment of the patient’s condition, only the case report proper (not the discussion) was used to quantify results. Statistical testing established if specific reporting criteria were associated with higher overall scores and potentially inferable increases in quality of reporting. Statistical testing of whether criteria scores were associated with an unclear/adjusted diagnosis were also tested, as well as the probability of a given criterion deviating from an expected estimate. Major Findings: The quantitative analysis of neuroanatomically driven diagnostics in case studies of aphasia revealed particularly low scores in the connection of neuroanatomical functions to aphasiological assessment (10%), and in the inclusion of white matter tracts within neuroimaging or assessment diagnostics (30%). Case studies which included clinical mention of white matter tracts within the report itself were distributed among higher scoring cases, as were case studies which (as clinically indicated) related the affected vascular region to the brain parenchyma of the language network. Concluding Statement: These findings indicate that certain neuroanatomical functions are integrated less often within the patient report than others, despite a precedent for well-integrated neuroanatomical aphasiology also being found among the case studies sampled, and despite these functions being clinically essential in diagnostic neuroimaging and aphasiological assessment. Therefore, ultimately the integration and specificity of aetiological neuroanatomy may contribute positively to the capacity and autonomy of aphasic patients as well as their clinicians. The integration of a full aetiological neuroanatomy within the reporting of aphasias may improve patient outcomes and sustain autonomy in the event of medico-ethical investigation.

Keywords: aphasia, language network, functional neuroanatomy, aphasiological diagnostics, medico-legal ethics

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Authors: K. S. Poutanen, P. Dussort, A. Erkner, S. Fiszman, K. Karnik, M. Kristensen, C. F. M. Marsaux, S. Miquel-Kergoat, S. Pentikäinen, P. Putz, R. E. Steinert, J. Slavin, D. J. Mela

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Dietary fiber (DF) intake has been associated with lower body weight or less weight gain. These effects are generally attributed to putative effects of DF on appetite. Many intervention studies have tested the effect of DFs on appetite-related measures, with inconsistent results. However, DF includes a wide category of different compounds with diverse chemical and physical characteristics, and correspondingly diverse effects in human digestion. Thus, inconsistent results between DF consumption and appetite are not surprising. The specific contribution of different compounds with varying physico-chemical properties to appetite control and the mediating mechanisms are not well characterized. This systematic review aimed to assess the influence of specific DF characteristics, including viscosity, gel forming capacity, fermentability, and molecular weight, on appetite-related outcomes in healthy humans. Medline and FSTA databases were searched for controlled human intervention trials, testing the effects of well-characterized DFs on subjective satiety/appetite or energy intake outcomes. Studies were included only if they reported: 1) fiber name and origin, and 2) data on viscosity, gelling properties, fermentability, or molecular weight of the DF materials tested. The search generated 3001 unique records, 322 of which were selected for further consideration from title and abstract screening. Of these, 149 were excluded due to insufficient fiber characterization and 124 for other reasons (not original article, not randomized controlled trial, or no appetite related outcome), leaving 49 papers meeting all the inclusion criteria, most of which reported results from acute testing (<1 day). The eligible 49 papers described 90 comparisons of DFs in foods, beverages or supplements. DF-containing material of interest was efficacious for at least one appetite-related outcome in 51/90 comparisons. Gel-forming DF sources were most consistently efficacious but there were no clear associations between viscosity, MW or fermentability and appetite-related outcomes. A considerable number of papers had to be excluded from the review due to shortcomings in fiber characterization. To build understanding about the impact of DF on satiety/appetite specifically there should be clear hypotheses about the mechanisms behind the proposed beneficial effect of DF material on appetite, and sufficient data about the DF properties relevant for the hypothesized mechanisms to justify clinical testing. The hypothesized mechanisms should also guide the decision about relevant duration of exposure in studies, i.e. are the effects expected to occur during acute time frame (related to stomach emptying, digestion rate, etc.) or develop from sustained exposure (gut fermentation mediated mechanisms). More consistent measurement methods and reporting of fiber specifications and characterization are needed to establish reliable structure-function relationships for DF and health outcomes.

Keywords: appetite, dietary fiber, physico-chemical properties, satiety

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Authors: Xiaobin Liang, Wei Liang, Laibin Zhang, Xiaoyan Guo

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Pipelines pass through coal mined gobs inevitably in the mining area, the stability of which has great influence on the safety of pipelines. After extensive literature study and field research, it was found that there are a few risk assessment methods for coal mined gob pipelines, and there is a lack of data on the gob sites. Therefore, the fuzzy comprehensive evaluation method is widely used based on expert opinions. However, the subjective opinions or lack of experience of individual experts may lead to inaccurate evaluation results. Hence the accuracy of the results needs to be further improved. This paper presents a comprehensive approach to achieve this purpose by combining bow-tie model and cloud inference. The specific evaluation process is as follows: First, a bow-tie model composed of a fault tree and an event tree is established to graphically illustrate the probability and consequence indicators of pipeline failure. Second, the interval estimation method can be scored in the form of intervals to improve the accuracy of the results, and the censored mean algorithm is used to remove the maximum and minimum values of the score to improve the stability of the results. The golden section method is used to determine the weight of the indicators and reduce the subjectivity of index weights. Third, the failure probability and failure consequence scores of the pipeline are converted into three numerical features by using cloud inference. The cloud inference can better describe the ambiguity and volatility of the results which can better describe the volatility of the risk level. Finally, the cloud drop graphs of failure probability and failure consequences can be expressed, which intuitively and accurately illustrate the ambiguity and randomness of the results. A case study of a coal mine gob pipeline carrying natural gas has been investigated to validate the utility of the proposed method. The evaluation results of this case show that the probability of failure of the pipeline is very low, the consequences of failure are more serious, which is consistent with the reality.

Keywords: bow-tie model, natural gas pipeline, coal mine gob, cloud inference

Procedia PDF Downloads 251

Authors: Ruchira Das

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The major Metropolises of India are resultant of Colonial manifestation of Production, Consumption and Sustenance. These cities grew, survived, and sustained on the basic whims of Colonial Power and Administrative Agendas. They were symbols of power, authority and administration. Within them some Colonial Towns remained as small towns within the close vicinity of the major metropolises and functioned as self–sufficient units until peripheral development due to tremendous pressure occurred in the metropolises. After independence huge expansion in Judiciary and Administration system resulted City Oriented Employment. A large number of people started residing within the city or within commutable distance of the city and it accelerated expansion of the cities. Since then Budgetary and Planning expenditure brought a new pace in Economic Activities. Investment in Industry and Agriculture sector generated opportunity of employment which further led towards urbanization. After two decades of Budgetary and Planning economic activities in India, a new era started in metropolitan expansion. Four major metropolises started further expansion rapidly towards its suburbs. A concept of large Metropolitan Area developed. Cities became nucleus of suburbs and rural areas. In most of the cases such expansion was not favorable to the relationship between City and its hinterland due to absence of visualization of Compact Sustainable Development. The search for solutions needs to weigh the choices between Rural and Urban based development initiatives. Policymakers need to focus on areas which will give the greatest impact. The impact of development initiatives will spread the significant benefit to all. There is an assumption that development integrates Economic, Social and Environmental considerations with equal weighing. The traditional narrower and almost exclusive focus on economic criteria as the determinant of the level of development is thus re–described and expanded. The Social and Environmental aspects are equally important as Economic aspect to achieve Sustainable Development. The arrangement of opportunities for Public, Semi – Public facilities for its citizen is very much relevant to development. It is responsibility of the administration to provide opportunities for the basic requirement of its inhabitants. Development should be in terms of both Industrial and Agricultural to maintain a balance between city and its hinterland. Thus, policy is to formulate shifting the emphasis away from Economic growth towards Sustainable Human Development. The goal of Policymaker should aim at creating environments in which people’s capabilities can be enhanced by the effective dynamic and adaptable policy. The poverty could not be eradicated simply by increasing income. The improvement of the condition of the people would have to lead to an expansion of basic human capabilities. In this scenario the suburbs/rural areas are considered as environmental burden to the metropolises. A new living has to be encouraged in the suburban or rural. We tend to segregate agriculture from the city and city life, this leads to over consumption, but this urbanism model attempts both these to co–exists and hence create an interesting overlapping of production and consumption network towards sustainable Rurbanism.

Keywords: socio–economic progress, sustainability, social equity, urbanism

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

Procedia PDF Downloads 171

Authors: Kathrin F. Schneider, Luis Xavier Unda Galarza

Abstract:

In our modern world, few are the areas that are not permeated by digitalization: we use digital tools for work, study, entertainment, and daily life. Since technology changes rapidly, skills must adapt to the new reality, which gives a dynamic dimension to the set of skills necessary for people's academic, professional, and personal success. The concept of 21st-century digital skills, which includes skills such as collaboration, communication, digital literacy, citizenship, problem-solving, critical thinking, interpersonal skills, creativity, and productivity, have been widely discussed in the literature. Digital transformation has opened many economic opportunities for entrepreneurs for the development of their products, financing possibilities, and product distribution. One of the biggest advantages is the reduction in cost for the entrepreneur, which has opened doors not only for the entrepreneur or the entrepreneurial team but also for corporations through intrapreneurship. The development of students' general literacy level and their digital competencies is crucial for improving the effectiveness and efficiency of the learning process, as well as for students' adaptation to the constantly changing labor market. The digital economy allows a free substantial increase in the supply share of conditional and also innovative products; this is mainly achieved through 5 ways to reduce costs according to the conventional digital economy: search costs, replication, transport, tracking, and verification. Digital entrepreneurship worldwide benefits from such achievements. There is an expansion and democratization of entrepreneurship thanks to the use of digital technologies. The digital transformation that has been taking place in recent years is more challenging for developing countries, as they have fewer resources available to carry out this transformation while offering all the necessary support in terms of cybersecurity and educating their people. The degree of digitization (use of digital technology) in a country and the levels of digital literacy of its people often depend on the economic level and situation of the country. Telefónica's Digital Life Index (TIDL) scores are strongly correlated with country wealth, reflecting the greater resources that richer countries can contribute to promoting "Digital Life". According to the Digitization Index, Ecuador is in the group of "emerging countries", while Chile, Colombia, Brazil, Argentina, and Uruguay are in the group of "countries in transition". According to Herrera Espinoza et al. (2022), there are startups or digital ventures in Ecuador, especially in certain niches, but many of the ventures do not exceed six months of creation because they arise out of necessity and not out of the opportunity. However, there is a lack of relevant research, especially empirical research, to have a clearer vision. Through a self-report questionnaire, the digital skills of students will be measured in an Ecuadorian private university, according to the skills identified as the six 21st-century skills. The results will be put to the test against the variable of the intention to start a digital venture measured using the theory of planned behavior (TPB). The main hypothesis is that high digital competence is positively correlated with the intention to start digital entrepreneurship.

Keywords: new literacies, digital transformation, 21st century skills, theory of planned behavior, digital entrepreneurship

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