Search results for: advanced numerical modelling

Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Ronahlee Asuncion

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The nature of their work and employment relationship make domestic workers easy prey to abuse, maltreatment, and exploitation. Considering their plight, this research was conceptualized and examined the: a) level of awareness of Filipino domestic workers on occupational safety and health (OSH); b) their issues/problems/concerns on OSH; c) their intervention strategies at work to address OSH related issues/problems/concerns; d) issues/problems/concerns of government, employers, and non-government organizations with regard to implementation of OSH to Filipino domestic workers; e) the role of government, employers and non-government organizations to help Filipino domestic workers address OSH related issues/problems/concerns; and f) the necessary policy amendments/initiatives/programs to address OSH related issues/problems/concerns of Filipino domestic workers. The study conducted a survey using non-probability sampling, two focus group discussions, two group interviews, and fourteen face-to-face interviews. These were further supplemented with an email correspondence to a key informant based in another country. Books, journals, magazines, and relevant websites further substantiated and enriched data of the research. Findings of the study point to the fact that domestic workers have low level of awareness on OSH because of poor information drive, fragmented implementation of the Domestic Workers Act, inactive campaign at the barangay level, weakened advocacy for domestic workers, absence of law on OSH for domestic workers, and generally low safety culture in the country among others. Filipino domestic workers suffer from insufficient rest, long hours of work, heavy workload, occupational stress, poor accommodation, insufficient hours of sleep, deprivation of day off, accidents and injuries such as cuts, burns, slipping, stumbling, electrical grounding, and fire, verbal, physical and sexual abuses, lack of medical assistance, none provision of personal protective equipment (PPE), absence of knowledge on the proper way of lifting, working at heights, and insufficient food provision. They also suffer from psychological problems because of separation from one’s family, limited mobility in the household where they work, injuries and accidents from using advanced home appliances and taking care of pets, low self-esteem, ergonomic problems, the need to adjust to all household members who have various needs and demands, inability to voice their complaints, drudgery of work, and emotional stress. With regard to illness or health problems, they commonly experience leg pains, back pains, and headaches. In the absence of intervention programs like those offered in the formal employment set up, domestic workers resort to praying, turn to family, relatives and friends for social and emotional support, connect with them through social media like Facebook which also serve as a means of entertainment to them, talk to their employer, and just try to be optimistic about their situation. Promoting OSH for domestic workers is very challenging and complicated because of interrelated factors such as cultural, knowledge, attitudinal, relational, social, resource, economic, political, institutional and legal problems. This complexity necessitates using a holistic and integrated approach as this is not a problem requiring simple solutions. With this recognition comes the full understanding that its success involves the action and cooperation of all duty bearers in attaining decent work for domestic workers.

Keywords: decent work, Filipino domestic workers, occupational safety and health, working conditions

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Authors: Kashima Arora, Monika Tomar, Vinay Gupta

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Biosensors have found potential applications ranging from environmental testing and biowarfare agent detection to clinical testing, health care, and cell analysis. This is driven in part by the desire to decrease the cost of health care and to obtain precise information more quickly about the health status of patient by the development of various biosensors, which has become increasingly prevalent in clinical testing and point of care testing for a wide range of biological elements. Uric acid is an important byproduct in human body and a number of pathological disorders are related to its high concentration in human body. In past few years, rapid growth in the development of new materials and improvements in sensing techniques have led to the evolution of advanced biosensors. In this context, metal oxide thin film based matrices due to their bio compatible nature, strong adsorption ability, high isoelectric point (IEP) and abundance in nature have become the materials of choice for recent technological advances in biotechnology. In the past few years, wide band-gap metal oxide semiconductors including ZnO, SnO₂ and CeO₂ have gained much attention as a matrix for immobilization of various biomolecules. Tin oxide (SnO₂), wide band gap semiconductor (Eg =3.87 eV), despite having multifunctional properties for broad range of applications including transparent electronics, gas sensors, acoustic devices, UV photodetectors, etc., it has not been explored much for biosensing purpose. To realize a high performance miniaturized biomolecular electronic device, rf sputtering technique is considered to be the most promising for the reproducible growth of good quality thin films, controlled surface morphology and desired film crystallization with improved electron transfer property. Recently, iron oxide and its composites have been widely used as matrix for biosensing application which exploits the electron communication feature of Fe, for the detection of various analytes using urea, hemoglobin, glucose, phenol, L-lactate, H₂O₂, etc. However, to the authors’ knowledge, no work is being reported on modifying the electronic properties of SnO₂ by implanting with suitable metal (Fe) to induce the redox couple in it and utilizing it for reagentless detection of uric acid. In present study, Fe implanted SnO₂ based matrix has been utilized for reagentless uric acid biosensor. Implantation of Fe into SnO₂ matrix is confirmed by energy-dispersive X-Ray spectroscopy (EDX) analysis. Electrochemical techniques have been used to study the response characteristics of Fe modified SnO₂ matrix before and after uricase immobilization. The developed uric acid biosensor exhibits a high sensitivity to about 0.21 mA/mM and a linear variation in current response over concentration range from 0.05 to 1.0 mM of uric acid besides high shelf life (~20 weeks). The Michaelis-Menten kinetic parameter (Km) is found to be relatively very low (0.23 mM), which indicates high affinity of the fabricated bioelectrode towards uric acid (analyte). Also, the presence of other interferents present in human serum has negligible effect on the performance of biosensor. Hence, obtained results highlight the importance of implanted Fe:SnO₂ thin film as an attractive matrix for realization of reagentless biosensors towards uric acid.

Keywords: Fe implanted tin oxide, reagentless uric acid biosensor, rf sputtering, thin film

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Authors: Nikhilesh Singh, Shishir Gaur, Anitha K. Sharma

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Climate change and anthropogenic stress have severely affected ecosystems all over the globe. Indian rivers are under immense pressure, facing challenges like pollution, encroachment, extreme fluctuation in the flow regime, local ignorance and lack of coordination between stakeholders. To counter all these issues a holistic river rejuvenation plan is needed that tests, innovates and implements sustainable solutions in the river space for sustainable river management. Smart Laboratory for Clean Rivers (SLCR) an Indo-Danish collaboration project, provides a living lab setup that brings all the stakeholders (government agencies, academic and industrial partners and locals) together to engage, learn, co-creating and experiment for a clean and sustainable river that last for ages. Just like every mega project requires piloting, SLCR has opted for a small catchment of the Varuna River, located in the Middle Ganga Basin in India. Considering the integrated approach of river rejuvenation, SLCR embraces various techniques and upgrades for rejuvenation. Likely, maintaining flow in the channel in the lean period, Managed Aquifer Recharge (MAR) is a proven technology. In SLCR, Floa-TEM high-resolution lithological data is used in MAR models to have better decision-making for MAR structures nearby of the river to enhance the river aquifer exchanges. Furthermore, the concerns of quality in the river are a big issue. A city like Varanasi which is located in the last stretch of the river, generates almost 260 MLD of domestic waste in the catchment. The existing STP system is working at full efficiency. Instead of installing a new STP for the future, SLCR is upgrading those STPs with an IoT-based system that optimizes according to the nutrient load and energy consumption. SLCR also advocate nature-based solutions like a reed bed for the drains having less flow. In search of micropollutants, SLCR uses fingerprint analysis involves employing advanced techniques like chromatography and mass spectrometry to create unique chemical profiles. However, rejuvenation attempts cannot be possible without involving the entire catchment. A holistic water management plan that includes storm management, water harvesting structure to efficiently manage the flow of water in the catchment and installation of several buffer zones to restrict pollutants entering into the river. Similarly, carbon (emission and sequestration) is also an important parameter for the catchment. By adopting eco-friendly practices, a ripple effect positively influences the catchment's water dynamics and aids in the revival of river systems. SLCR has adopted 4 villages to make them carbon-neutral and water-positive. Moreover, for the 24×7 monitoring of the river and the catchment, robust IoT devices are going to be installed to observe, river and groundwater quality, groundwater level, river discharge and carbon emission in the catchment and ultimately provide fuel for the data analytics. In its completion, SLCR will provide a river restoration manual, which will strategise the detailed plan and way of implementation for stakeholders. Lastly, the entire process is planned in such a way that will be managed by local administrations and stakeholders equipped with capacity-building activity. This holistic approach makes SLCR unique in the field of river rejuvenation.

Keywords: sustainable management, holistic approach, living lab, integrated river management

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Authors: Aleksandra Chałupnik, Zuzanna Chilimoniuk, Joanna Borowik, Aleksandra Borkowska, Anna Torres

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Background: Precocious puberty is the occurrence of secondary sexual characteristics in girls before the age of 8. The diverse etiology of premature puberty is crucial to determine whether it is true precocious puberty, depending on the activation of the hypothalamic-pituitary-gonadal axis, or pseudo-precocious, which is independent of the activation of this axis. Whatever the cause, premature action of the sex hormones leads to the common symptoms of various forms of puberty. These include the development of sexual characteristics, acne, acceleration of growth rate and acceleration of skeletal maturation. Due to the possible genetic basis of the disorders, an interdisciplinary search for the cause is needed. Case report: The case report concerns a patient of a pediatric gynecology clinic who, at the age of two years, developed advanced thelarhe (M3) and started recurrent vaginal bleeding. In August 2019, gonadotropin suppression initially and after LHRH stimulation and high estradiol levels were reported at the Endocrinology Department. Imaging examinations showed a cyst in the right ovary projection. The bone age was six years. The entire clinical picture indicated pseudo- (peripheral) precocious in the course of ovarian autonomic cyst. In the follow-up ultrasound performed in September, the image of the cyst was stationary and normalization of estradiol levels and clinical symptoms was noted. In December 2019, cyst regression and normal gonadotropin and estradiol concentrations were found. In June 2020, white mucus tinged with blood on the underwear, without any other disturbing symptoms, was observed for several days. Two consecutive USG examinations carried out in the same month confirmed the change in the right ovary, the diameter of which was 25 mm with a very high level of estradiol. Germinal tumor markers were normal. On the Tanner scale, the patient scored M2P1. The labia and hymen had puberty features. The correct vaginal entrance was visible. Another active vaginal bleeding occurred in the first week of July 2020. The considered laparoscopic treatment was abandoned due to the lack of oncological indications. Treatment with Tamoxifen was recommended in July 2020. In the initiating period of treatment, no maturation progression, and even reduction of symptoms, no acceleration of growth and a marked reduction in the size of the cysts were noted. There was no bleeding. After the size of the cyst and hormonal activity increased again, the treatment was changed to Anastrozole, the effect of which led to a reduction in the size of the cyst. Conclusions: The entire clinical picture indicates alleged (peripheral) puberty. Premature puberty in girls, which is manifested as enlarged mammary glands with high levels of estrogens secreted by autonomic ovarian cysts and prepubertal levels of gonadotropins, may indicate McCune-Albright syndrome. Vaginal bleeding may also occur in this syndrome. Cancellation of surgical treatment of the cyst made it impossible to perform a molecular test that would allow to confirm the diagnosis. Taking into account the fact that cysts are often one of the first symptoms of McCune-Albrigt syndrome, it is important to remember about multidisciplinary care for the patient and careful search for skin and bone changes or other hormonal disorders.

Keywords: McCune Albrigth's syndrome, ovarian cyst, pediatric gynaecology, precocious puberty

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Authors: Corentin Jankowiak, Aggelos Zacharopoulos, Caterina Brandoni

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In a context of increasing stress put on the electricity network by the decarbonization of many sectors, energy storage is likely to be the key mitigating element, by acting as a buffer between production and demand. In particular, the highest potential for storage is when connected closer to the loads. Yet, low voltage storage struggles to penetrate the market at a large scale due to the novelty and complexity of the solution, and the competitive advantage of fossil fuel-based technologies regarding regulations. Strong and reliable numerical simulations are required to show the benefits of storage located near loads and promote its development. The present study was restrained from excluding aggregated control of storage: it is assumed that the storage units operate independently to one another without exchanging information – as is currently mostly the case. A computationally light battery model is presented in detail and validated by direct comparison with a domestic battery operating in real conditions. This model is then used to develop Peak-Shaving (PS) control strategies as it is the decentralized service from which beneficial impacts are most likely to emerge. The aggregation of flatter, peak- shaved consumption profiles is likely to lead to flatter and arbitraged profile at higher voltage layers. Furthermore, voltage fluctuations can be expected to decrease if spikes of individual consumption are reduced. The crucial part to achieve PS lies in the charging pattern: peaks depend on the switching on and off of appliances in the dwelling by the occupants and are therefore impossible to predict accurately. A performant PS strategy must, therefore, include a smart charge recovery algorithm that can ensure enough energy is present in the battery in case it is needed without generating new peaks by charging the unit. Three categories of PS algorithms are introduced in detail. First, using a constant threshold or power rate for charge recovery, followed by algorithms using the State Of Charge (SOC) as a decision variable. Finally, using a load forecast – of which the impact of the accuracy is discussed – to generate PS. A performance metrics was defined in order to quantitatively evaluate their operating regarding peak reduction, total energy consumption, and self-consumption of domestic photovoltaic generation. The algorithms were tested on load profiles with a 1-minute granularity over a 1-year period, and their performance was assessed regarding these metrics. The results show that constant charging threshold or power are far from optimal: a certain value is not likely to fit the variability of a residential profile. As could be expected, forecast-based algorithms show the highest performance. However, these depend on the accuracy of the forecast. On the other hand, SOC based algorithms also present satisfying performance, making them a strong alternative when the reliable forecast is not available.

Keywords: decentralised control, domestic integrated batteries, electricity network performance, peak-shaving algorithm

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Authors: Kantha Siddhanth Gujjari, Shaani Singhal, Robert Andrew Danks, Adrian Praeger

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Background: Trigeminal neuralgia (TN) is a heterogenous pain syndrome characterised by short paroxysms of lancinating facial pain in the distribution of the trigeminal nerve, often triggered by usually innocuous stimuli. TN has a low prevalence of less than 0.1%, of which 80% to 90% is caused by compression of the trigeminal nerve from an adjacent artery or vein. The root entry zone of the trigeminal nerve is most sensitive to neurovascular conflict (NVC), causing dysmyelination. Whilst microvascular decompression (MVD) is an effective treatment for TN with NVC, all patients do not achieve long-term pain relief. Pre-operative scoring systems by Panczykowski and Hardaway have been proposed but have not been externally validated. These pre-operative scoring systems are composite scores calculated according to a subtype of TN, presence and degree of neurovascular conflict, and response to medical treatments. There is discordance in the assessment of NVC identified on pre-operative magnetic resonance imaging (MRI) between neurosurgeons and radiologists. To our best knowledge, the prognostic impact for MVD of this difference of interpretation has not previously been investigated in the form of a composite scoring system such as those suggested by Panczykowski and Hardaway. Aims: This study aims to identify prognostic factors and externally validate the proposed scoring systems by Panczykowski and Hardaway for TN. A secondary aim is to investigate the prognostic difference between a neurosurgeon's interpretation of NVC on MRI compared with a radiologist’s. Methods: This retrospective cohort study included 95 patients who underwent de novo MVD in a single neurosurgical unit in Melbourne. Data was recorded from patients’ hospital records and neurosurgeon’s correspondence from perioperative clinic reviews. Patient demographics, type of TN, distribution of TN, response to carbamazepine, neurosurgeon, and radiologist interpretation of NVC on MRI, were clearly described prospectively and preoperatively in the correspondence. Scoring systems published by Panczykowski et al. and Hardaway et al. were used to determine composite scores, which were compared with the recurrence of TN recorded during follow-up over 1-year. Categorical data analysed using Pearson chi-square testing. Independent numerical and nominal data analysed with logistical regression. Results: Logistical regression showed that a Panczykowski composite score of greater than 3 points was associated with a higher likelihood of pain-free outcome 1-year post-MVD with an OR 1.81 (95%CI 1.41-2.61, p=0.032). The composite score using neurosurgeon’s impression of NVC had an OR 2.96 (95%CI 2.28-3.31, p=0.048). A Hardaway composite score of greater than 2 points was associated with a higher likelihood of pain-free outcome 1 year post-MVD with an OR 3.41 (95%CI 2.58-4.37, p=0.028). The composite score using neurosurgeon’s impression of NVC had an OR 3.96 (95%CI 3.01-4.65, p=0.042). Conclusion: Composite scores developed by Panczykowski and Hardaway were validated for the prediction of response to MVD in TN. A composite score based on the neurosurgeon’s interpretation of NVC on MRI, when compared with the radiologist’s had a greater correlation with pain-free outcomes 1 year post-MVD.

Keywords: de novo microvascular decompression, neurovascular conflict, prognosis, trigeminal neuralgia

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Authors: Mozhdeh Shirinzadeh, Richard Stroetmann

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Orthotropic steel deck bridge consists of a deck plate, longitudinal stiffeners under the deck plate, cross beams and the main longitudinal girders. Due to the several advantages, Orthotropic Steel Deck (OSD) systems have been utilized in many bridges worldwide. The significant feature of this structural system is its high load-bearing capacity while having relatively low dead weight. In addition, cost efficiency and the ability of rapid field erection have made the orthotropic steel deck a popular type of bridge worldwide. However, OSD bridges are highly susceptible to fatigue damage. A large number of welded joints can be regarded as the main weakness of this system. This problem is, in particular, evident in the bridges which were built before 1994 when the fatigue design criteria had not been introduced in the bridge design codes. Recently, an Orthotropic-composite slab (OCS) for road bridges has been experimentally and numerically evaluated and developed at Technische Universität Dresden as a part of AIF-FOSTA research project P1265. The results of the project have provided a solid foundation for the design and analysis of Orthotropic-composite decks with dowel strips as a durable alternative to conventional steel or reinforced concrete decks. In continuation, while using the achievements of that project, the application of a modified Ortho-composite deck for an existing typical OSD bridge is investigated. Composite action is obtained by using rows of dowel strips in a clothoid (CL) shape. Regarding Eurocode criteria for different fatigue detail categories of an OSD bridge, the effect of the proposed modification approach is assessed. Moreover, a numerical parametric study is carried out utilizing finite element software to determine the impact of different variables, such as the size and arrangement of dowel strips, the application of transverse or longitudinal rows of dowel strips, and local wheel loads. For the verification of the simulation technique, experimental results of a segment of an OCS deck are used conducted in project P1265. Fatigue assessment is performed based on the last draft of Eurocode 1993-2 (2024) for the most probable detail categories (Hot-Spots) that have been reported in the previous statistical studies. Then, an analytical comparison is provided between the typical orthotropic steel deck and the modified Ortho-composite deck bridge in terms of fatigue issues and durability. The load-bearing capacity of the bridge, the critical deflections, and the composite behavior are also evaluated and compared. Results give a comprehensive overview of the efficiency of the rehabilitation method considering the required design service life of the bridge. Moreover, the proposed approach is assessed with regard to the construction method, details and practical aspects, as well as the economic point of view.

Keywords: composite action, fatigue, finite element method, steel deck, bridge

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Authors: Nicola Milani, Carla Bellissimo, Davide Pogliana, Davide Panzin, Luca Garlaschelli, Giulia Facchinetti, Claudia Casson, Luca Marazzina, Andrea Sartori, Simone Rivaroli, Jeff Konin

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The patient is a 26 year-old male amateur football player from Milan, Italy; (81kg; 185cm; BMI 23.6 kg/m²). He sustained a non-contact anterior cruciate ligament tear to his right knee in June 2021. In September 2021, his right knee ligament was reconstructed using a semitendinosus graft. The injury occurred during a football match on natural grass with typical shoes on a warm day (32 degrees celsius). Playing as a defender he sustained the injury during a change of direction, where the foot was fixated on the grass. He felt pain and was unable to continue playing the match. The surgeon approved his rehabilitation to begin two weeks post-operative. The initial physiotherapist assessment determined performing two training sessions per day within the first three months. In the first three weeks, the pain was 4/10 on Numerical Rating Scale (NRS), no swelling, a range of motion was 0-110°, with difficulty fully extending his knee and minimal quadriceps activation. Crutches were discontinued at four weeks with improved walking. Active exercise, electrostimulator, physical therapy, massages, osteopathy, and passive motion were initiated. At week 6, he completed his first functional movement screen; the score was 16/21 with no pain and no swelling. At week 8, the isokinetic test showed a 23% differential deficit between the two legs in maximum strength (at 90°/s). At week 10, he improved to 15% of injury-induced deficit which suggested he was ready to start running. At week 12, the athlete sustained his first threshold test. At week 16, he performed his first return to sports movement assessment, which revealed a 10% stronger difference between the legs. At week 16, he had his second threshold test. At week 17, his first on-field test revealed a 5% differential deficit between the two legs in the hop test. At week 18, isokinetic test demonstrates that the uninjured leg was 7% stronger than the recovering leg in maximum strength (at 90°/s). At week 20, his second on-field test revealed a 2% difference in hop test; at week 21, his third isokinetic test demonstrated a difference of 5% in maximum strength (at 90°/s). At week 21, he performed his second return to sports movement assessment which revealed a 2% difference between the limbs. Since it was the end of the championship, the team asked him to partake in the playoffs; moreover the player was very motivated to participate in the playoffs also because he was the captain of the team. Together with the player and the team, we decided to let him play even though we were aware of a heightened risk of injury than what is reported in the literature because of two factors: biological recovery times and the results of the tests we performed. In the decision making process about the athlete’s recovery time, it is important to balance the information available from the literature with the desires of the patient to avoid frustration.

Keywords: ACL, football, rehabilitation, return to play

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Authors: Elena Iacob, Marie-Louise Ionescu, Elena Ionescu, Carmen Elena Tebrencu, Oana Teodora Ciuperca

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Infrared spectroscopy (FT-IR) is an advanced technique frequently used to authenticate both raw materials and final products using their specific fingerprints and to determine plant extracts biomarkers based on their functional groups. In recent years the market for Hypericum has grown rapidly and also has grown the cases of adultery/replacement, especially for Hypericum perforatum L.specie. Presence/absence of same biomarkers provides preliminary identification of Hypericum species in safe use in the manufacture of food supplements. The main objective of the work was to characterize the main biomarkers of Hypericum perforatum L. (St. John's wort) and identify this species in herbal food supplements after specific FT-IR fingerprint. An experimental program has been designed in order to test: (1) raw material (St. John's wort); (2)intermediate raw materials (St. John's wort dry extract ); (3) the finished products: tablets based on powders, on extracts, on powder and extract, hydroalcoholic solution from herbal mixture based on St. John's wort. The analyze using FTIR infrared spectroscopy were obtained raw materials, intermediates and finished products spectra, respectively absorption bands corresponding and similar with aliphatic and aromatic structures; examination was done individually and through comparison between Hypericum perforatum L. plant species and finished product The tests were done in correlation with phytochemical markers for authenticating the specie Hypericum perforatum L.: hyperoside, rutin, quercetin, isoquercetin, luteolin, apigenin, hypericin, hyperforin, chlorogenic acid. Samples were analyzed using a Shimatzu FTIR spectrometer and the infrared spectrum of each sample was recorded in the MIR region, from 4000 to 1000 cm-1 and then the fingerprint region was selected for data analysis. The following functional groups were identified -stretching vibrations suggests existing groups in the compounds of interest (flavones–rutin, hyperoside, polyphenolcarboxilic acids - chlorogenic acid, naphtodianthrones- hypericin): oxidril groups (OH) free alcohol type: rutin, hyperoside, chlorogenic acid; C = O bond from structures with free carbonyl groups of aldehyde, ketone, carboxylic, ester: hypericin; C = O structure with the free carbonyl of the aldehyde groups, ketone, carboxylic acid, esteric/C = O free bonds present in chlorogenic acid; C = C bonds of the aromatic ring (condensed aromatic hydrocarbons, heterocyclic compounds) present in all compounds of interest; OH phenolic groups: present in all compounds of interest, C-O-C groups from glycoside structures: rutin, hyperoside, chlorogenic acid. The experimental results show that: (I)The six fingerprint region analysis indicated the presence of specific functional groups: (1) 1000 - 1130 cm-1 (C-O–C of glycoside structures); (2) 1200-1380 cm-1 (carbonyl C-O or O-H phenolic); (3) 1400-1450 cm-1 (C=C aromatic); (4) 1600- 1730 cm-1 (C=O carbonyl); (5) 2850 - 2930 cm-1 (–CH3, -CH2-, =CH-); (6) 338-3920 cm-1 (OH free alcohol type); (II)Comparative FT-IR spectral analysis indicate the authenticity of the finished products ( tablets) in terms of Hypericum perforatum L. content; (III)The infrared spectroscopy is an adequate technique for identification and authentication of the medicinal herbs , intermediate raw material and in the food supplements less in the form of solutions where the results are not conclusive.

Keywords: Authentication, FT-IR fingerprint, Herbal supplements, Hypericum perforatum L.

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Authors: Ayesha Selwyn, Annapoorni Chandrashekar, Kumar Ashwarya, Nishant Baghel

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Project-based learning (PBL) activities are typically implemented in technology-enabled schools by highly trained teachers. In rural India, students have limited access to technology and quality education. Implementing typical PBL activities is challenging. This study details how Pratham Education Foundation’s Hybrid Learning model was used to implement two PBL activities related to music in 900 remote Indian villages with 46,000 students aged 10-14. The activities were completed by 69% of groups that submitted a total of 15,000 videos (completed projects). Pratham’s H-Learning model reaches 100,000 students aged 3-14 in 900 Indian villages. The community-driven model engages students in 20,000 self-organized groups outside of school. The students are guided by 6,000 youth volunteers and 100 facilitators. The students partake in learning activities across subjects with the support of community stakeholders and offline digital content on shared Android tablets. A training and implementation toolkit for PBL activities is designed by subject experts. This toolkit is essential in ensuring efficient implementation of activities as facilitators aren’t highly skilled and have limited access to training resources. The toolkit details the activity at three levels of student engagement - enrollment, participation, and completion. The subject experts train project leaders and facilitators who train youth volunteers. Volunteers need to be trained on how to execute the activity and guide students. The training is focused on building the volunteers’ capacity to enable students to solve problems, rather than developing the volunteers’ subject-related knowledge. This structure ensures that continuous intervention of subject matter experts isn’t required, and the onus of judging creativity skills is put on community members. 46,000 students in the H-Learning program were engaged in two PBL activities related to Music from April-June 2019. For one activity, students had to conduct a “musical survey” in their village by designing a survey and shooting and editing a video. This activity aimed to develop students’ information retrieval, data gathering, teamwork, communication, project management, and creativity skills. It also aimed to identify talent and document local folk music. The second activity, “Pratham Idol”, was a singing competition. Students participated in performing, producing, and editing videos. This activity aimed to develop students’ teamwork and creative skills and give students a creative outlet. Students showcased their completed projects at village fairs wherein a panel of community members evaluated the videos. The shortlisted videos from all villages were further evaluated by experts who identified students and adults to participate in advanced music workshops. The H-Learning framework enables students in low resource settings to engage in PBL and develop relevant skills by leveraging community support and using video creation as a tool. In rural India, students do not have access to high-quality education or infrastructure. Therefore designing activities that can be implemented by community members after limited training is essential. The subject experts have minimal intervention once the activity is initiated, which significantly reduces the cost of implementation and allows the activity to be implemented at a massive scale.

Keywords: community supported learning, project-based learning, self-organized learning, education technology

Procedia PDF Downloads 163

Authors: Anita Bajpai, Sarah Duan, Ashlee Erskine, Shehzein Khan, Raymond Kramer

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Introduction: This research focuses on exploring the beneficial effects if any, of Vitamin-D in reducing the need for supplemental oxygen among hospitalized COVID-19 patients. Two questions are investigated – Q1)Doeshaving a healthy level of baselineVitamin-D 25-OH (≥ 30ng/ml) help,andQ2) does administering Vitamin-D therapy after-the-factduring inpatient hospitalization help? Methods/Study Design: This is a comprehensive, retrospective, observational study of all inpatients at RUHS from March through December 2020 who tested positive for COVID-19 based on real-time reverse transcriptase–polymerase chain reaction assay of nasal and pharyngeal swabs and rapid assay antigen test. To address Q1, we looked atall N1=182 patients whose baseline plasma Vitamin-D 25-OH was known and who needed supplemental oxygen. Of this, a total of 121 patients had a healthy Vitamin-D level of ≥30 ng/mlwhile the remaining 61 patients had low or borderline (≤ 29.9ng/ml)level. Similarly, for Q2, we looked at a total of N2=893 patients who were given supplemental oxygen, of which713 were not given Vitamin-D and 180 were given Vitamin-D therapy. The numerical value of the maximum amount of oxygen flow rate(dependent variable) administered was recorded for each patient. The mean values and associated standard deviations for each group were calculated. Thesetwo sets of independent data served as the basis for independent, two-sample t-Test statistical analysis. To be accommodative of any reasonable benefitof Vitamin-D, ap-value of 0.10(α< 10%) was set as the cutoff point for statistical significance. Results: Given the large sample sizes, the calculated statistical power for both our studies exceeded the customary norm of 80% or better (β< 0.2). For Q1, the mean value for maximumoxygen flow rate for the group with healthybaseline level of Vitamin-D was 8.6 L/min vs.12.6L/min for those with low or borderline levels, yielding a p-value of 0.07 (p < 0.10) with the conclusion that those with a healthy level of baseline Vitamin-D needed statistically significant lower levels of supplemental oxygen. ForQ2, the mean value for a maximum oxygen flow rate for those not administered Vitamin-Dwas 12.5 L/min vs.12.8L/min for those given Vitamin-D, yielding a p-valueof 0.87 (p > 0.10). We thereforeconcludedthat there was no statistically significant difference in the use of oxygen therapy between those who were or were not administered Vitamin-D after-the-fact in the hospital. Discussion/Conclusion: We found that patients who had healthy levels of Vitamin-D at baseline needed statistically significant lower levels of supplemental oxygen. Vitamin-D is well documented, including in a recent article in the Lancet, for its anti-inflammatory role as an adjuvant in the regulation of cytokines and immune cells. Interestingly, we found no statistically significant advantage for giving Vitamin-D to hospitalized patients. It may be a case of “too little too late”. A randomized clinical trial reported in JAMA also did not find any reduction in hospital stay of patients given Vitamin-D. Such conclusions come with a caveat that any delayed marginal benefits may not have materialized promptly in the presence of a significant inflammatory condition. Since Vitamin-D is a low-cost, low-risk option, it may still be useful on an inpatient basis until more definitive findings are established.

Keywords: COVID-19, vitamin-D, supplemental oxygen, vitamin-D in primary care

Procedia PDF Downloads 135

Authors: Jiujie Cai, Fengxia LI, Haibo Wang

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After years of research, offshore oil and gas development now are shifted to unconventional reservoirs, where multi-stage hydraulic fracturing technology has been widely used. However, the simulation of complex hydraulic fractures in tight reservoirs is faced with geological and engineering difficulties, such as large burial depths, sand-shale interbeds, and complex stress barriers. The objective of this work is to simulate the hydraulic fracture propagation in the tight sandstone matrix of the marine-continental transitional reservoirs, where the Shanxi Formation in Tianhuan syncline of the Dongsheng gas field was used as the research target. The characteristic parameters of the vertical rock samples with rich beddings were clarified through rock mechanics experiments. The influence of rock mechanical parameters, vertical stress difference of pay-zone and bedding layer, and fracturing parameters (such as injection rates, fracturing fluid viscosity, and number of perforation clusters within single stage) on fracture initiation and propagation were investigated. In this paper, a 3-D fracture propagation model was built to investigate the complex fracture propagation morphology by boundary element method, considering the strength of bonding surface between layers, vertical stress difference and fracturing parameters (such as injection rates, fluid volume and viscosity). The research results indicate that on the condition of vertical stress difference (3 MPa), the fracture height can break through and enter the upper interlayer when the thickness of the overlying bedding layer is 6-9 m, considering effect of the weak bonding surface between layers. The fracture propagates within the pay zone when overlying interlayer is greater than 13 m. Difference in fluid volume distribution between clusters could be more than 20% when the stress difference of each cluster in the segment exceeds 2MPa. Fracture cluster in high stress zones cannot initiate when the stress difference in the segment exceeds 5MPa. The simulation results of fracture height are much higher if the effect of weak bonding surface between layers is not involved. By increasing the injection rates, increasing fracturing fluid viscosity, and reducing the number of clusters within single stage can promote the fracture height propagation through layers. Optimizing the perforation position and reducing the number of perforations can promote the uniform expansion of fractures. Typical curves of fracture height estimation were established for the tight sandstone of the Lower Permian Shanxi Formation. The model results have good consistency with micro-seismic monitoring results of hydraulic fracturing in Well 1HF.

Keywords: fracture propagation, boundary element method, fracture height, offshore oil and gas, marine-continental transitional reservoirs, rock mechanics experiment

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Authors: Jelvin Tom Sebastian, Vinod Yeldho Baby

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In space, electronic devices are constantly attacked with radiation, which causes certain parts to fail or behave in unpredictable ways. To advance the thermal controllability for microsatellites, we need a new approach and thermal control system that is smaller than that on conventional satellites and that demand no electric power. Heat exchange inside the microsatellites is not that easy as conventional satellites due to the smaller size. With slight mass gain and no electric power, accommodating heat using phase change materials (PCMs) is a strong candidate for solving micro satellites' thermal difficulty. In other words, PCMs can absorb or produce heat in the form of latent heat, changing their phase and minimalizing the temperature fluctuation around the phase change point. The main restriction for these systems is thermal conductivity weakness of common PCMs. As PCM is having low thermal conductivity, it increases the melting and solidification time, which is not suitable for specific application like electronic cooling. In order to increase the thermal conductivity nanoparticles are introduced. Adding the nanoparticles in base PCM increases the thermal conductivity. Increase in weight concentration increases the thermal conductivity. This paper numerically investigates the thermal energy storage panel with nanoparticle enhanced phase change material. Silver nanostructure have increased the thermal properties of the base PCM, eicosane. Different weight concentration (1, 2, 3.5, 5, 6.5, 8, 10%) of silver enhanced phase change material was considered. Both steady state and transient analysis was performed to compare the characteristics of nanoparticle enhanced phase material at different heat loads. Results showed that in steady state, the temperature near the front panel reduced and temperature on NePCM panel increased as the weight concentration increased. With the increase in thermal conductivity more heat was absorbed into the NePCM panel. In transient analysis, it was found that the effect of nanoparticle concentration on maximum temperature of the system was reduced as the melting point of the material reduced with increase in weight concentration. But for the heat load of maximum 20W, the model with NePCM did not attain the melting point temperature. Therefore it showed that the model with NePCM is capable of holding more heat load. In order to study the heat load capacity double the load is given, maximum of 40W was given as first half of the cycle and the other is given constant OW. Higher temperature was obtained comparing the other heat load. The panel maintained a constant temperature for a long duration according to the NePCM melting point. In both the analysis, the uniformity of temperature of the TESP was shown. Using Ag-NePCM it allows maintaining a constant peak temperature near the melting point. Therefore, by altering the weight concentration of the Ag-NePCM it is possible to create an optimum operating temperature required for the effective working of the electronics components.

Keywords: carbon-fiber-reinforced polymer, micro/nano-satellite, nanoparticle phase change material, thermal energy storage

Procedia PDF Downloads 192

Authors: Teodora Vukovic, Christoph Hottiger, Noah Bubenhofer

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The development of VIAN-DH aims at bridging two linguistic approaches: conversation analysis/interactional linguistics (IL), so far a dominantly qualitative field, and computational/corpus linguistics and its quantitative and automated methods. Contemporary IL investigates the systematic organization of conversations and interactions composed of speech, gaze, gestures, and body positioning, among others. These highly integrated multimodal behaviour is analysed based on video data aimed at uncovering so called “multimodal gestalts”, patterns of linguistic and embodied conduct that reoccur in specific sequential positions employed for specific purposes. Multimodal analyses (and other disciplines using videos) are so far dependent on time and resource intensive processes of manual transcription of each component from video materials. Automating these tasks requires advanced programming skills, which is often not in the scope of IL. Moreover, the use of different tools makes the integration and analysis of different formats challenging. Consequently, IL research often deals with relatively small samples of annotated data which are suitable for qualitative analysis but not enough for making generalized empirical claims derived quantitatively. VIAN-DH aims to create a workspace where many annotation layers required for the multimodal analysis of videos can be created, processed, and correlated in one platform. VIAN-DH will provide a graphical interface that operates state-of-the-art tools for automating parts of the data processing. The integration of tools that already exist in computational linguistics and computer vision, facilitates data processing for researchers lacking programming skills, speeds up the overall research process, and enables the processing of large amounts of data. The main features to be introduced are automatic speech recognition for the transcription of language, automatic image recognition for extraction of gestures and other visual cues, as well as grammatical annotation for adding morphological and syntactic information to the verbal content. In the ongoing instance of VIAN-DH, we focus on gesture extraction (pointing gestures, in particular), making use of existing models created for sign language and adapting them for this specific purpose. In order to view and search the data, VIAN-DH will provide a unified format and enable the import of the main existing formats of annotated video data and the export to other formats used in the field, while integrating different data source formats in a way that they can be combined in research. VIAN-DH will adapt querying methods from corpus linguistics to enable parallel search of many annotation levels, combining token-level and chronological search for various types of data. VIAN-DH strives to bring crucial and potentially revolutionary innovation to the field of IL, (that can also extend to other fields using video materials). It will allow the processing of large amounts of data automatically and, the implementation of quantitative analyses, combining it with the qualitative approach. It will facilitate the investigation of correlations between linguistic patterns (lexical or grammatical) with conversational aspects (turn-taking or gestures). Users will be able to automatically transcribe and annotate visual, spoken and grammatical information from videos, and to correlate those different levels and perform queries and analyses.

Keywords: multimodal analysis, corpus linguistics, computational linguistics, image recognition, speech recognition

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Authors: Anne Schattel, Mitja Echim, Christof Büskens

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Trajectory planning for deep space missions has become a recent topic of great interest. Flying to space objects like asteroids provides two main challenges. One is to find rare earth elements, the other to gain scientific knowledge of the origin of the world. Due to the enormous spatial distances such explorer missions have to be performed unmanned and autonomously. The mathematical field of optimization and optimal control can be used to realize autonomous missions while protecting recourses and making them safer. The resulting algorithms may be applied to other, earth-bound applications like e.g. deep sea navigation and autonomous driving as well. The project KaNaRiA ('Kognitionsbasierte, autonome Navigation am Beispiel des Ressourcenabbaus im All') investigates the possibilities of cognitive autonomous navigation on the example of an asteroid mining mission, including the cruise phase and approach as well as the asteroid rendezvous, landing and surface exploration. To verify and test all methods an interactive, real-time capable simulation using virtual reality is developed under KaNaRiA. This paper focuses on the specific challenge of the guidance during the cruise phase of the spacecraft, i.e. trajectory optimization and optimal control, including first solutions and results. In principle there exist two ways to solve optimal control problems (OCPs), the so called indirect and direct methods. The indirect methods are being studied since several decades and their usage needs advanced skills regarding optimal control theory. The main idea of direct approaches, also known as transcription techniques, is to transform the infinite-dimensional OCP into a finite-dimensional non-linear optimization problem (NLP) via discretization of states and controls. These direct methods are applied in this paper. The resulting high dimensional NLP with constraints can be solved efficiently by special NLP methods, e.g. sequential quadratic programming (SQP) or interior point methods (IP). The movement of the spacecraft due to gravitational influences of the sun and other planets, as well as the thrust commands, is described through ordinary differential equations (ODEs). The competitive mission aims like short flight times and low energy consumption are considered by using a multi-criteria objective function. The resulting non-linear high-dimensional optimization problems are solved by using the software package WORHP ('We Optimize Really Huge Problems'), a software routine combining SQP at an outer level and IP to solve underlying quadratic subproblems. An application-adapted model of impulsive thrusting, as well as a model of an electrically powered spacecraft propulsion system, is introduced. Different priorities and possibilities of a space mission regarding energy cost and flight time duration are investigated by choosing different weighting factors for the multi-criteria objective function. Varying mission trajectories are analyzed and compared, both aiming at different destination asteroids and using different propulsion systems. For the transcription, the robust method of full discretization is used. The results strengthen the need for trajectory optimization as a foundation for autonomous decision making during deep space missions. Simultaneously they show the enormous increase in possibilities for flight maneuvers by being able to consider different and opposite mission objectives.

Keywords: deep space navigation, guidance, multi-objective, non-linear optimization, optimal control, trajectory planning.

Procedia PDF Downloads 398

Authors: Elhadi Nasr, Ibrahim Ramadan

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Biostratigraphical analyses of well sections from the Maragh Low in the Eastern Sirt Basin has allowed high resolution correlations to be undertaken. Full integration of this data with available palaeoenvironmental, lithological, gravity, seismic, aeromagnetic, igneous, radiometric and wireline log information and a geochemical analysis of source rock quality and distribution has led to a more detailed understanding of the geological and the structural history of this area. Pre Sirt Unconformity two superimposed rifting cycles have been identified. The oldest is represented by the Amal Group of sediments and is of Late Carboniferous, Kasimovian / Gzelian to Middle Triassic, Anisian age. Unconformably overlying is a younger rift cycle which is represented the Sarir Group of sediments and is of Early Cretaceous, late Neocomian to Aptian in age. Overlying the Sirt Unconformity is the marine Late Cretaceous section. An assessment of pyrolysis results and a palynofacies analysis has allowed hydrocarbon source facies and quality to be determined. There are a number of hydrocarbon source rock horizons in the Maragh Low, these are sometimes vertically stacked and they are of fair to excellent quality. The oldest identified source rock is the Triassic Shale, this unit is unconformably overlain by sandstones belonging to the Sarir Group and conformably overlies a Triassic Siltstone unit. Palynological dating of the Triassic Shale unit indicates a Middle Triassic, Anisian age. The Triassic Shale is interpreted to have been deposited in a lacustrine palaeoenvironment. This particularly is evidenced by the dark, fine grained, organic rich nature of the sediment and is supported by palynofacies analysis and by the recovery of fish fossils. Geochemical analysis of the Triassic Shale indicates total organic carbon varying between 1.37 and 3.53. S2 pyrolysate yields vary between 2.15 mg/g and 6.61 mg/g and hydrogen indices vary between 156.91 and 278.91. The source quality of the Triassic Shale varies from being of fair to very good / rich. Linked to thermal maturity it is now a very good source for light oil and gas. It was once a very good to rich oil source. The Early Barremian Shale was also deposited in a lacustrine palaeoenvironment. Recovered palynomorphs indicate an Early Cretaceous, late Neocomian to early Barremian age. The Early Barremian Shale is conformably underlain and overlain by sandstone units belonging to the Sarir Group of sediments which are also of Early Cretaceous age. Geochemical analysis of the Early Barremian Shale indicates that it is a good oil source and was originally very good. Total organic carbon varies between 3.59% and 7%. S2 varies between 6.30 mg/g and 10.39 mg/g and the hydrogen indices vary between 148.4 and 175.5. A Late Barremian Shale unit of this age has also been identified in the central Maragh Low. Geochemical analyses indicate that total organic carbon varies between 1.05 and 2.38%, S2 pyrolysate between 1.6 and 5.34 mg/g and the hydrogen index between 152.4 and 224.4. It is a good oil source rock which is now mature. In addition to the non marine hydrocarbon source rocks pre Sirt Unconformity, three formations in the overlying Late Cretaceous section also provide hydrocarbon quality source rocks. Interbedded shales within the Rachmat Formation of Late Cretaceous, early Campanian age have total organic carbon ranging between, 0.7 and 1.47%, S2 pyrolysate varying between 1.37 and 4.00 mg/g and hydrogen indices varying between 195.7 and 272.1. The indication is that this unit would provide a fair gas source to a good oil source. Geochemical analyses of the overlying Tagrifet Limestone indicate that total organic carbon varies between 0.26% and 1.01%. S2 pyrolysate varies between 1.21 and 2.16 mg/g and hydrogen indices vary between 195.7 and 465.4. For the overlying Sirt Shale Formation of Late Cretaceous, late Campanian age, total organic carbon varies between 1.04% and 1.51%, S2 pyrolysate varies between 4.65 mg/g and 6.99 mg/g and the hydrogen indices vary between 151 and 462.9. The study has proven that both the Sirt Shale Formation and the Tagrifet Limestone are good to very good and rich sources for oil in the Maragh Low. High resolution biostratigraphical interpretations have been integrated and calibrated with thermal maturity determinations (Vitrinite Reflectance (%Ro), Spore Colour Index (SCI) and Tmax (ºC) and the determined present day geothermal gradient of 25ºC / Km for the Maragh Low. Interpretation of generated basin modelling profiles allows a detailed prediction of timing of maturation development of these source horizons and leads to a determination of amounts of missing section at major unconformities. From the results the top of the oil window (0.72% Ro) is picked as high as 10,700’ and the base of the oil window (1.35% Ro) assuming a linear trend and by projection is picked as low as 18,000’ in the Maragh Low. For the Triassic Shale the early phase of oil generation was in the Late Palaeocene / Early to Middle Eocene and the main phase of oil generation was in the Middle to Late Eocene. The Early Barremian Shale reached the main phase of oil generation in the Early Oligocene with late generation being reached in the Middle Miocene. For the Rakb Group section (Rachmat Formation, Tagrifet Limestone and Sirt Shale Formation) the early phase of oil generation started in the Late Eocene with the main phase of generation being between the Early Oligocene and the Early Miocene. From studying maturity profiles and from regional considerations it can be predicted that up to 500’ of sediment may have been deposited and eroded by the Sirt Unconformity in the central Maragh Low while up to 2000’ of sediment may have been deposited and then eroded to the south of the trough.

Keywords: Geochemical analysis of the source rocks from wells in Eastern Sirt Basin.

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

Procedia PDF Downloads 134

Authors: Junkal Gutierrez, Hernane S. Barud, Sidney J. L. Ribeiro, Agnieszka Tercjak

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Nowadays the interest on green nanocomposites and on the development of more environmental friendly products has been increased. Bacterial cellulose has been recently investigated as an attractive environmentally friendly material for the preparation of low-cost nanocomposites. The formation of cellulose by laboratory bacterial cultures is an interesting and attractive biomimetic access to obtain pure cellulose with excellent properties. Additionally, properties as molar mass, molar mass distribution, and the supramolecular structure could be control using different bacterial strain, culture mediums and conditions, including the incorporation of different additives. This kind of cellulose is a natural nanomaterial, and therefore, it has a high surface-to-volume ratio which is highly advantageous in composites production. Such property combined with good biocompatibility, high tensile strength, and high crystallinity makes bacterial cellulose a potential material for applications in different fields. The aim of this investigation work was the fabrication of novel hybrid inorganic-organic composites based on bacterial cellulose, cultivated in our laboratory, as a template. This kind of biohybrid nanocomposites gathers together excellent properties of bacterial cellulose with the ones displayed by typical inorganic nanoparticles like optical, magnetic and electrical properties, luminescence, ionic conductivity and selectivity, as well as chemical or biochemical activity. In addition, the functionalization of cellulose with inorganic materials opens new pathways for the fabrication of novel multifunctional hybrid materials with promising properties for a wide range of applications namely electronic paper, flexible displays, solar cells, sensors, among others. In this work, different pathways for fabrication of multifunctional biohybrid nanopapers with tunable properties based on BC modified with amphiphilic poly(ethylene oxide-b-propylene oxide-b-ethylene oxide) (EPE) block copolymer, sol-gel synthesized nanoparticles (titanium, vanadium and a mixture of both oxides) and functionalized iron oxide nanoparticles will be presented. In situ (biosynthesized) and ex situ (at post-production level) approaches were successfully used to modify BC membranes. Bacterial cellulose based biocomposites modified with different EPE block copolymer contents were developed by in situ technique. Thus, BC growth conditions were manipulated to fabricate EPE/BC nanocomposite during the biosynthesis. Additionally, hybrid inorganic/organic nanocomposites based on BC membranes and inorganic nanoparticles were designed via ex-situ method, by immersion of never-dried BC membranes into different nanoparticle solutions. On the one hand, sol-gel synthesized nanoparticles (titanium, vanadium and a mixture of both oxides) and on the other hand superparamagnetic iron oxide nanoparticles (SPION), Fe2O3-PEO solution. The morphology of designed novel bionanocomposites hybrid materials was investigated by atomic force microscopy (AFM) and scanning electron microscopy (SEM). In order to characterized obtained materials from the point of view of future applications different techniques were employed. On the one hand, optical properties were analyzed by UV-vis spectroscopy and spectrofluorimetry and on the other hand electrical properties were studied at nano and macroscale using electric force microscopy (EFM), tunneling atomic force microscopy (TUNA) and Keithley semiconductor analyzer, respectively. Magnetic properties were measured by means of magnetic force microscopy (MFM). Additionally, mechanical properties were also analyzed.

Keywords: bacterial cellulose, block copolymer, advanced characterization techniques, nanoparticles

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Authors: M. Pienaar, J. W. Odendaal, J. C. Smit, J. Joubert

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Simulations are commonly used to predict the bistatic radar cross section (RCS) of military targets since characterization measurements can be expensive and time consuming. It is thus important to accurately predict the bistatic RCS of targets. Computational electromagnetic (CEM) methods can be used for bistatic RCS prediction. CEM methods are divided into full-wave and asymptotic methods. Full-wave methods are numerical approximations to the exact solution of Maxwell’s equations. These methods are very accurate but are computationally very intensive and time consuming. Asymptotic techniques make simplifying assumptions in solving Maxwell's equations and are thus less accurate but require less computational resources and time. Asymptotic techniques can thus be very valuable for the prediction of bistatic RCS of electrically large targets, due to the decreased computational requirements. This study extends previous work by validating the accuracy of asymptotic techniques to predict bistatic RCS through comparison with full-wave simulations as well as measurements. Validation is done with canonical structures as well as complex realistic aircraft models instead of only looking at a complex slicy structure. The slicy structure is a combination of canonical structures, including cylinders, corner reflectors and cubes. Validation is done over large bistatic angles and at different polarizations. Bistatic RCS measurements were conducted in a compact range, at the University of Pretoria, South Africa. The measurements were performed at different polarizations from 2 GHz to 6 GHz. Fixed bistatic angles of β = 30.8°, 45° and 90° were used. The measurements were calibrated with an active calibration target. The EM simulation tool FEKO was used to generate simulated results. The full-wave multi-level fast multipole method (MLFMM) simulated results together with the measured data were used as reference for validation. The accuracy of physical optics (PO) and geometrical optics (GO) was investigated. Differences relating to amplitude, lobing structure and null positions were observed between the asymptotic, full-wave and measured data. PO and GO were more accurate at angles close to the specular scattering directions and the accuracy seemed to decrease as the bistatic angle increased. At large bistatic angles PO did not perform well due to the shadow regions not being treated appropriately. PO also did not perform well for canonical structures where multi-bounce was the main scattering mechanism. PO and GO do not account for diffraction but these inaccuracies tended to decrease as the electrical size of objects increased. It was evident that both asymptotic techniques do not properly account for bistatic structural shadowing. Specular scattering was calculated accurately even if targets did not meet the electrically large criteria. It was evident that the bistatic RCS prediction performance of PO and GO depends on incident angle, frequency, target shape and observation angle. The improved computational efficiency of the asymptotic solvers yields a major advantage over full-wave solvers and measurements; however, there is still much room for improvement of the accuracy of these asymptotic techniques.

Keywords: asymptotic techniques, bistatic RCS, geometrical optics, physical optics

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Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

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To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

Procedia PDF Downloads 133

Authors: De-Shin Liu, Po-Chun Wen, Zhen-Wei Zhuang, Hsueh-Chih Liu, Pei-Chen Huang

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Wafer probing tests play an important role in semiconductor manufacturing procedures in accordance with the yield and reliability requirement of the wafer after the backend-of-the-line process. Accordingly, the stable physical and electrical contact between the probe and the tested wafer during wafer probing is regarded as an essential issue in identifying the known good die. The probe card can be integrated with multiple probe needles, which are classified as vertical, cantilever and micro-electro-mechanical systems type probe selections. Among all potential probe types, the vertical probe has several advantages as compared with other probe types, including maintainability, high probe density and feasibility for high-speed wafer testing. In the present study, the mechanical response of the wafer probing test with the vertical cobra probe on 720 μm thick silicon (Si) substrate with a 1.4 μm thick aluminum (Al) pad is investigated by the experiment and transient dynamic simulation approach. Because the deformation mechanism of the vertical cobra probe is determined by both bending and buckling mechanisms, the stable correlation between contact forces and overdrive (OD) length must be carefully verified. Moreover, the decent OD length with corresponding contact force contributed to piercing the native oxide layer of the Al pad and preventing the probing test-induced damage on the interconnect system. Accordingly, the scratch depth of the Al pad under various OD lengths is estimated by the atomic force microscope (AFM) and simulation work. In the wafer probing test configuration, the contact phenomenon between the probe needle and the tested object introduced large deformation and twisting of mesh gridding, causing the subsequent numerical divergence issue. For this reason, the arbitrary Lagrangian-Eulerian method is utilized in the present simulation work to conquer the aforementioned issue. The analytic results revealed a slight difference when the OD is considered as 40 μm, and the simulated is almost identical to the measured scratch depths of the Al pad under higher OD lengths up to 70 μm. This phenomenon can be attributed to the unstable contact of the probe at low OD length with the scratch depth below 30% of Al pad thickness, and the contact status will be being stable when the scratch depth over 30% of pad thickness. The splash of the Al pad is observed by the AFM, and the splashed Al debris accumulates on a specific side; this phenomenon is successfully simulated in the transient dynamic simulation. Thus, the preferred testing OD lengths are found as 45 μm to 70 μm, and the corresponding scratch depths on the Al pad are represented as 31.4% and 47.1% of Al pad thickness, respectively. The investigation approach demonstrated in this study contributed to analyzing the mechanical response of wafer probing test configuration under large strain conditions and assessed the geometric designs and material selections of probe needles to meet the requirement of high resolution and high-speed wafer-level probing test for thinned wafer application.

Keywords: wafer probing test, vertical probe, probe mark, mechanical response, FEA simulation

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: B. Neethu, Diptesh Das

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The present study investigates the performance of a semi-active controller using magneto-rheological dampers (MR) for seismic response reduction of a multi-span bridge. The application of structural control to the structures during earthquake excitation involves numerous challenges such as proper formulation and selection of the control strategy, mathematical modeling of the system, uncertainty in system parameters and noisy measurements. These problems, however, need to be tackled in order to design and develop controllers which will efficiently perform in such complex systems. A control algorithm, which can accommodate un-certainty and imprecision compared to all the other algorithms mentioned so far, due to its inherent robustness and ability to cope with the parameter uncertainties and imprecisions, is the sliding mode algorithm. A sliding mode control algorithm is adopted in the present study due to its inherent stability and distinguished robustness to system parameter variation and external disturbances. In general a semi-active control scheme using an MR damper requires two nested controllers: (i) an overall system controller, which derives the control force required to be applied to the structure and (ii) an MR damper voltage controller which determines the voltage required to be supplied to the damper in order to generate the desired control force. In the present study a sliding mode algorithm is used to determine the desired optimal force. The function of the voltage controller is to command the damper to produce the desired force. The clipped optimal algorithm is used to find the command voltage supplied to the MR damper which is regulated by a semi active control law based on sliding mode algorithm. The main objective of the study is to propose a robust semi active control which can effectively control the responses of the bridge under real earthquake ground motions. Lumped mass model of the bridge is developed and time history analysis is carried out by solving the governing equations of motion in the state space form. The effectiveness of MR dampers is studied by analytical simulations by subjecting the bridge to real earthquake records. In this regard, it may also be noted that the performance of controllers depends, to a great extent, on the characteristics of the input ground motions. Therefore, in order to study the robustness of the controller in the present study, the performance of the controllers have been investigated for fourteen different earthquake ground motion records. The earthquakes are chosen in such a way that all possible characteristic variations can be accommodated. Out of these fourteen earthquakes, seven are near-field and seven are far-field. Also, these earthquakes are divided into different frequency contents, viz, low-frequency, medium-frequency, and high-frequency earthquakes. The responses of the controlled bridge are compared with the responses of the corresponding uncontrolled bridge (i.e., the bridge without any control devices). The results of the numerical study show that the sliding mode based semi-active control strategy can substantially reduce the seismic responses of the bridge showing a stable and robust performance for all the earthquakes.

Keywords: bridge, semi active control, sliding mode control, MR damper

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Authors: Ashley L. Freeman, Jessica D. Watkins

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TeleEmergency Medicine (TeleEM) is an innovative approach leveraging virtual technology to deliver specialized emergency medical care across diverse healthcare settings, including internal acute care and critical access hospitals, remote patient monitoring, and nurse triage escalation, in addition to external emergency departments, skilled nursing facilities, and community health centers. TeleEM represents a significant advancement in the delivery of emergency medical care, providing healthcare professionals the capability to deliver expertise that closely mirrors in-person emergency medicine, exceeding geographical boundaries. Through qualitative research, the extension of timely, high-quality care has proven to address the critical needs of patients in remote and underserved areas. TeleEM’s service design allows for the expansion of existing services and the establishment of new ones in diverse geographic locations. This ensures that healthcare institutions can readily scale and adapt services to evolving community requirements by leveraging on-demand (non-scheduled) telemedicine visits through the deployment of multiple video solutions. In terms of financial management, TeleEM currently employs billing suppression and subscription models to enhance accessibility for a wide range of healthcare facilities. Plans are in motion to transition to a billing system routing charges through a third-party vendor, further enhancing financial management flexibility. To address state licensure concerns, a patient location verification process has been integrated through legal counsel and compliance authorities' guidance. The TeleEM workflow is designed to terminate if the patient is not physically located within licensed regions at the time of the virtual connection, alleviating legal uncertainties. A distinctive and pivotal feature of TeleEM is the introduction of the TeleEmergency Medicine Care Team Assistant (TeleCTA) role. TeleCTAs collaborate closely with TeleEM Physicians, leading to enhanced service activation, streamlined coordination, and workflow and data efficiencies. In the last year, more than 800 TeleEM sessions have been conducted, of which 680 were initiated by internal acute care and critical access hospitals, as evidenced by quantitative research. Without this service, many of these cases would have necessitated patient transfers. Barriers to success were examined through thorough medical record review and data analysis, which identified inaccuracies in documentation leading to activation delays, limitations in billing capabilities, and data distortion, as well as the intricacies of managing varying workflows and device setups. TeleEM represents a transformative advancement in emergency medical care that nurtures collaboration and innovation. Not only has advanced the delivery of emergency medicine care virtual technology through focus group participation with key stakeholders, rigorous attention to legal and financial considerations, and the implementation of robust documentation tools and the TeleCTA role, but it’s also set the stage for overcoming geographic limitations. TeleEM assumes a notable position in the field of telemedicine by enhancing patient outcomes and expanding access to emergency medical care while mitigating licensure risks and ensuring compliant billing.

Keywords: emergency medicine, TeleEM, rural healthcare, telemedicine

Procedia PDF Downloads 61

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

Procedia PDF Downloads 60

Authors: Benjamin Arazi

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Blockchain is the technology behind cryptocurrencies like Bitcoin. It revolutionizes the meaning of trust in the sense of offering total reliability without relying on any central entity that controls or supervises the system. The Wall Street Journal states: “Blockchain Marks the Next Step in the Internet’s Evolution”. Blockchain was listed as #1 in Linkedin – The Learning Blog “most in-demand hard skills needed in 2020”. As stated there: “Blockchain’s novel way to store, validate, authorize, and move data across the internet has evolved to securely store and send any digital asset”. GSMA, a leading Telco organization of mobile communications operators, declared that “Blockchain has the potential to be for value what the Internet has been for information”. Motivated by these seminal observations, this paper presents the foundations of a Blockchain-based “Internet of Assets” academic program that joins under one roof leading application areas that are characterized by the transfer of assets over communication lines. Two such areas, which are pillars of our economy, are Fintech – Financial Technology and mobile communications services. The next application in line is Healthcare. These challenges are met based on available extensive professional literature. Blockchain-based assets communication is based on extending the principle of Bitcoin, starting with the basic question: If digital money that travels across the universe can ‘prove its own validity’, can this principle be applied to digital content. A groundbreaking positive answer here led to the concept of “smart contract” and consequently to DLT - Distributed Ledger Technology, where the word ‘distributed’ relates to the non-existence of reliable central entities or trusted third parties. The terms Blockchain and DLT are frequently used interchangeably in various application areas. The World Bank Group compiled comprehensive reports, analyzing the contribution of DLT/Blockchain to Fintech. The European Central Bank and Bank of Japan are engaged in Project Stella, “Balancing confidentiality and auditability in a distributed ledger environment”. 130 DLT/Blockchain focused Fintech startups are now operating in Switzerland. Blockchain impact on mobile communications services is treated in detail by leading organizations. The TM Forum is a global industry association in the telecom industry, with over 850 member companies, mainly mobile operators, that generate US$2 trillion in revenue and serve five billion customers across 180 countries. From their perspective: “Blockchain is considered one of the digital economy’s most disruptive technologies”. Samples of Blockchain contributions to Fintech (taken from a World Bank document): Decentralization and disintermediation; Greater transparency and easier auditability; Automation & programmability; Immutability & verifiability; Gains in speed and efficiency; Cost reductions; Enhanced cyber security resilience. Samples of Blockchain contributions to the Telco industry. Establishing identity verification; Record of transactions for easy cost settlement; Automatic triggering of roaming contract which enables near-instantaneous charging and reduction in roaming fraud; Decentralized roaming agreements; Settling accounts per costs incurred in accordance with agreement tariffs. This clearly demonstrates an academic education structure where fundamental technologies are studied in classes together with these two application areas. Advanced courses, treating specific implementations then follow separately. All are under the roof of “Internet of Assets”.

Keywords: blockchain, education, financial technology, mobile telecommunications services

Procedia PDF Downloads 163

Authors: R. R. Galiev, I. I. Lykov, A. E. Shitikov, I. A. Bilenko

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Dual-comb interferometry is based on the mixing of two optical frequency combs with slightly different lines spacing which results in the mapping of the optical spectrum into the radio-frequency domain for future digitizing and numerical processing. The dual-comb approach enables diverse applications, including metrology, fast high-precision spectroscopy, and distance range. Ordinary frequency-modulated continuous-wave (FMCW) laser-based Light Identification Detection and Ranging systems (LIDARs) suffer from two main disadvantages: slow and unreliable mechanical, spatial scan and a rather wide linewidth of conventional lasers, which limits speed measurement resolution. Dual-comb distance measurements with Allan deviations down to 12 nanometers at averaging times of 13 microseconds, along with ultrafast ranging at acquisition rates of 100 megahertz, allowing for an in-flight sampling of gun projectiles moving at 150 meters per second, was previously demonstrated. Nevertheless, pump lasers with EDFA amplifiers made the device bulky and expensive. An alternative approach is a direct coupling of the laser to a reference microring cavity. Backscattering can tune the laser to the eigenfrequency of the cavity via the so-called self-injection locked (SIL) effect. Moreover, the nonlinearity of the cavity allows a solitonic frequency comb generation in the very same cavity. In this work, we developed a fully integrated, power-efficient, electrically driven dual-micro comb source based on the semiconductor lasers SIL to high-quality integrated Si3N4 microresonators. We managed to obtain robust 1400-1700 nm combs generation with a 150 GHz or 1 THz lines spacing and measure less than a 1 kHz Lorentzian withs of stable, MHz spaced beat notes in a GHz band using two separated chips, each pumped by its own, self-injection locked laser. A deep investigation of the SIL dynamic allows us to find out the turn-key operation regime even for affordable Fabry-Perot multifrequency lasers used as a pump. It is important that such lasers are usually more powerful than DFB ones, which were also tested in our experiments. In order to test the advantages of the proposed techniques, we experimentally measured a minimum detectable speed of a reflective object. It has been shown that the narrow line of the laser locked to the microresonator provides markedly better velocity accuracy, showing velocity resolution down to 16 nm/s, while the no-SIL diode laser only allowed 160 nm/s with good accuracy. The results obtained are in agreement with the estimations and open up ways to develop LIDARs based on compact and cheap lasers. Our implementation uses affordable components, including semiconductor laser diodes and commercially available silicon nitride photonic circuits with microresonators.

Keywords: dual-comb spectroscopy, LIDAR, optical microresonator, self-injection locking

Procedia PDF Downloads 55

Authors: Ho-Ting Wu, Kai-Wei Ke, Bo-Yu Huang, Liang-Lin Yan, Chun-Ting Lin

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With the advances in advanced technology, wireless sensor network (WSN) has become one of the most promising candidates to implement the wireless industrial internet of things (IIOT) architecture. However, the legacy IEEE 802.15.4 based WSN technology such as Zigbee system cannot meet the stringent QoS requirement of low powered, real-time, and highly reliable transmission imposed by the IIOT environment. Recently, the IEEE society developed IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) access mode to serve this purpose. Furthermore, the IETF 6TiSCH working group has proposed standards to integrate IEEE 802.15.4e with IPv6 protocol smoothly to form a complete protocol stack for IIOT. In this work, we develop key network technologies for IEEE 802.15.4e based wireless IIoT architecture, focusing on practical design and system implementation. We realize the OpenWSN-based wireless IIOT system. The system architecture is divided into three main parts: web server, network manager, and sensor nodes. The web server provides user interface, allowing the user to view the status of sensor nodes and instruct sensor nodes to follow commands via user-friendly browser. The network manager is responsible for the establishment, maintenance, and management of scheduling and topology information. It executes centralized scheduling algorithm, sends the scheduling table to each node, as well as manages the sensing tasks of each device. Sensor nodes complete the assigned tasks and sends the sensed data. Furthermore, to prevent scheduling error due to packet loss, a schedule inspection mechanism is implemented to verify the correctness of the schedule table. In addition, when network topology changes, the system will act to generate a new schedule table based on the changed topology for ensuring the proper operation of the system. To enhance the system performance of such system, we further propose dynamic bandwidth allocation and distributed scheduling mechanisms. The developed distributed scheduling mechanism enables each individual sensor node to build, maintain and manage the dedicated link bandwidth with its parent and children nodes based on locally observed information by exchanging the Add/Delete commands via two processes. The first process, termed as the schedule initialization process, allows each sensor node pair to identify the available idle slots to allocate the basic dedicated transmission bandwidth. The second process, termed as the schedule adjustment process, enables each sensor node pair to adjust their allocated bandwidth dynamically according to the measured traffic loading. Such technology can sufficiently satisfy the dynamic bandwidth requirement in the frequently changing environments. Last but not least, we propose a packet retransmission scheme to enhance the system performance of the centralized scheduling algorithm when the packet delivery rate (PDR) is low. We propose a multi-frame retransmission mechanism to allow every single network node to resend each packet for at least the predefined number of times. The multi frame architecture is built according to the number of layers of the network topology. Performance results via simulation reveal that such retransmission scheme is able to provide sufficient high transmission reliability while maintaining low packet transmission latency. Therefore, the QoS requirement of IIoT can be achieved.

Keywords: IEEE 802.15.4e, industrial internet of things (IIOT), scheduling mechanisms, wireless sensor networks (WSN)

Procedia PDF Downloads 142