Search results for: artificial magnetic conduction

Authors: James Britton, Vijaya Krishna, Manus Biggs, Abhay Pandit

Abstract:

Regeneration of the spinal cord after injury remains a great challenge due to the complexity of this organ. Inflammation and gliosis at the injury site hinder the outgrowth of axons and hence prevent synaptic reconnection and reinnervation. Hyaluronic acid (HA) is the main component of the spinal cord extracellular matrix and plays a vital role in cell proliferation and axonal guidance. In this study, we have synthesized and characterized a photo-cross-linkable HA-tyramine (tyr) hydrogel from a chemical, mechanical, electrical, biological and structural perspective. From our experimentation, we have found that HA-tyr can be synthesized with controllable degrees of tyramine substitution using click chemistry. The complex modulus (G*) of HA-tyr can be tuned to mimic the mechanical properties of the native spinal cord via optimization of the photo-initiator concentration and UV exposure. We have examined the degree of tyramine-tyramine covalent bonding (polymerization) as a function of UV exposure and photo-initiator use via Photo and Nuclear magnetic resonance spectroscopy. Both swelling and enzymatic degradation assays were conducted to examine the resilience of our 3D printed hydrogel constructs in-vitro. Using a femtosecond 780nm laser, the two-photon polymerization of HA-tyr hydrogel in the presence of riboflavin photoinitiator was optimized. A laser power of 50mW and scan speed of 30,000 μm/s produced high-resolution spatial patterning within the hydrogel with sustained mechanical integrity. Using dorsal root ganglion explants, the cytocompatibility of photo-crosslinked HA-tyr was assessed. Using potentiometry, the electrical conductivity of photo-crosslinked HA-tyr was assessed and compared to that of native spinal cord tissue as a function of frequency. In conclusion, we have developed a biocompatible hydrogel that can be used for photolithographic 3D printing to fabricate tissue engineered constructs for neural tissue regeneration applications.

Keywords: 3D printing, hyaluronic acid, photolithography, spinal cord injury

Procedia PDF Downloads 151

Authors: Kapil Khanna, Swee Kuik, Joowon Ban

Abstract:

The current linear economic system is unsustainable due to its dependence on the uncontrolled exploitation of diminishing natural resources. The integration of business innovation and supply chain management has brought about the redesign of business processes through the implementation of a closed-loop approach. The circular economy (CE) offers a sustainable solution to improve business opportunities in the near future by following the principles of rejuvenation and reuse inspired by nature. Those business owners start to rethink and consider using waste as raw material to make new products for consumers. The implementation of CE helps organisations to incorporate new strategic plans for decreasing the use of virgin materials and nature resources. Supply chain partners that are geographically dispersed rely heavily on innovative approaches to support supply chain management. Presently, numerous studies have attempted to establish the concept of supply chain management (SCM) by integrating CE principles, which are commonly denoted as circular SCM. While many scholars have recognised the challenges of transitioning to CE, there is still a lack of consensus on business best practices that can facilitate companies in embracing CE across the supply chain. Hence, this paper strives to scrutinize the SCM practices utilised for CE, identify the obstacles, and recommend best practices that can enhance a company's ability to incorporate CE principles toward business innovation and supply chain performance. Further, the paper proposes future research in the field of using specific technologies such as artificial intelligence, Internet of Things, and blockchain as business innovation tools for supply chain management and CE adoption.

Keywords: business innovation, challenges, circular supply chain, supply chain management, technology

Procedia PDF Downloads 95

Authors: Sujayeva V. A., Karpova I. S., Koslataya O. V., Kolyadko M. G., Russkikh I. I., Vankovich E. A.

Abstract:

Objective: The goal of this study is to develop a method for the prevention of the progression of heart failure (HF) in patients with post-infarction cardiosclerosis who have suffered coronavirus infection. Methods: 135 patients with post-infarction cardiosclerosis were divided into 2 groups: Group I - patients who had suffered COVID-19 - 85 people, and Group II - patients who had not suffered COVID-19 - 50 people. Patients of group I, depending on the level of N-terminal fragment of natriuretic peptide (NTproBNP), were divided into 2 subgroups - subgroup A - with HF - 40 people, subgroup B - without HF - 45 people. All patients underwent a clinical examination, echocardiography, electrocardiotopography in 60 leads, computed angiography of the coronary arteries, heart magnetic resonance imaging, NTproBNP. Results: In the post-Covid period, in patients with post-infarction cardiosclerosis, remodeling of the left ventricle and right parts of the heart, deterioration of the systolic-diastolic function of both ventricles, increased pressure in the pulmonary artery, progression of coronary artery atherosclerosis, and an increase in the size of myocardial fibrosis were revealed. The consequence of these changes was the progression of heart failure. The developed method of medical prevention made it possible to improve the clinical course of coronary artery disease and prevent the progression of chronic heart failure in patients with post-infarction cardiosclerosis. Conclusions: In patients with post-infarction cardiosclerosis who initially had HF, after 1 year, according to laboratory and instrumental data, a slight decrease in its severity was revealed. In patients with post-infarction cardiosclerosis who did not have HF before COVID-19, HF developed 1 year after the coronavirus disease, which may be due to the identified process of myocardial fibrosis, which dictates the need to prevent the development of HF in patients with post-infarction cardiosclerosis, even those who did not initially have HF. The proposed method of medical prevention made it possible to improve the clinical course of coronary artery disease in patients with post-infarction cardiosclerosis after COVID-19, both in persons with and without HF, when included in the study. A method of medical prevention in people with post-infarction cardiosclerosis after COVID-19 infection, including spironolactone, loop diuretics, empagliflozin, sacubitril/valsartan, helped prevent the progression of HF.

Keywords: elderly, myocardial infarction, COVID-19, prevention

Procedia PDF Downloads 22

Authors: Meysam Naeimi, Zili Li, Roumen Petrov, Rolf Dollevoet, Jilt Sietsma, Jun Wu

Abstract:

The substantial similarity of fatigue mechanism in a new test rig for rolling contact fatigue (RCF) has been investigated. A new reduced-scale test rig is designed to perform controlled RCF tests in wheel-rail materials. The fatigue mechanism of the rig is evaluated in this study using a combined finite element-fatigue prediction approach. The influences of loading conditions on fatigue crack initiation have been studied. Furthermore, the effects of some artificial defects (squat-shape) on fatigue lives are examined. To simulate the vehicle-track interaction by means of the test rig, a three-dimensional finite element (FE) model is built up. The nonlinear material behaviour of the rail steel is modelled in the contact interface. The results of FE simulations are combined with the critical plane concept to determine the material points with the greatest possibility of fatigue failure. Based on the stress-strain responses, by employing of previously postulated criteria for fatigue crack initiation (plastic shakedown and ratchetting), fatigue life analysis is carried out. The results are reported for various loading conditions and different defect sizes. Afterward, the cyclic mechanism of the test rig is evaluated from the operational viewpoint. The results of fatigue life predictions are compared with the expected number of cycles of the test rig by its cyclic nature. Finally, the estimative duration of the experiments until fatigue crack initiation is roughly determined.

Keywords: fatigue, test rig, crack initiation, life, rail, squats

Procedia PDF Downloads 515

Authors: Phusit Phukamchanoad

Abstract:

The research studies the behaviors based on sufficiency economy philosophy at individual and community levels as well as the satisfaction of the urban community leaders by collecting data with purposive sampling technique. For in-depth interviews with 26 urban community leaders, the result shows that the urban community leaders have good knowledge and understanding about sufficiency economy philosophy. Especially in terms of money spending, they must consider the need for living and be economical. The activities in the community or society should not take advantage of the others as well as colleagues. At present, most of the urban community leaders live in a sufficient way. They often spend time with public service, but many families are dealing with debt. Many communities have some political conflict and high family allowances because of living in the urban communities with rapid social and economic changes. However, there are many communities that leaders have applied their wisdom in development for their people by gathering and grouping the professionals to form activities such as making chili sauce, textile organization, making artificial flowers worshipping the sanctity. The most prominent group is the foot massage business in Wat Pracha Rabue Tham. This professional group is supported continuously by the government. One of the factors in terms of satisfaction used for evaluating community leaders is the customary administration in brotherly, interdependent way rather than using the absolute power or controlling power, but using the roles of leader to perform the activities with their people intently, determinedly and having a public mind for people.

Keywords: performance and activities, sufficiency economy, urban communities leader, Dusit district

Procedia PDF Downloads 363

Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

Abstract:

Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty-five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using the neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility, overhead and profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: construction cost factors, neural networks, roadworks, Zambian construction industry

Procedia PDF Downloads 361

Authors: Jéssica F. Rodrigues, Amanda C. Andrade, Sabrina C. Bastos, Sandra B. Coelho, Ana Carla M. Pinheiro

Abstract:

Currently, there is a great demand for the use of natural sweeteners due to the harmful effects of the high sugar and artificial sweeteners consumption on the health. Miracle fruit, which is known for its unique ability to modify the sour taste in sweet taste, has been shown to be a good alternative sweetener. However, it has a high production cost, being important to optimize lower contents to be used. Thus, the aim of this study was to assess the effect of different miracle fruit contents on the temporal (Time-intensity - TI and Temporal Dominance of Sensations - TDS) sensory profile and overall linking of lemonade, to determine the better content to be used as a natural sweetener in sour beverages. TI and TDS results showed that the concentrations of 150 mg, 300 mg and 600 mg miracle fruit were effective in reducing the acidity and promoting the sweet perception in lemonade. Furthermore, the concentrations of 300 mg and 600 mg obtained similar profiles. Through the acceptance test, the concentration of 300 mg miracle fruit was shown to be an efficient substitute for sucrose and sucralose in lemonade, once they had similar hedonic values between ‘I liked it slightly’ and ‘I liked it moderately’. Therefore, 300mg miracle fruit consists in an adequate content to be used as a natural sweetener of lemonade. The results of this work will help the food industry on the efficient application of a new natural sweetener- the Miracle fruit extract in sour beverages, reducing costs and providing a product that meets the consumer desires.

Keywords: acceptance, natural sweetener, temporal dominance of sensations, time-intensity

Procedia PDF Downloads 248

Authors: G. M. Hamada, A. A. Al-Gathe, A. M. Al-Khudafi

Abstract:

Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles.

Keywords: water saturation, Archie’s parameters, artificial intelligence, PSONN, sandstone reservoir

Procedia PDF Downloads 127

Authors: Ayad Salih Sabbar, Amin Chegenizadeh, Hamid Nikraz

Abstract:

Investigation of liquefaction susceptibility of materials that have been used in embankments, slopes, dams, and foundations is very essential. Many catastrophic geo-hazards such as flow slides, declination of foundations, and damage to earth structure are associated with static liquefaction that may occur during abrupt shearing of these materials. Many artificial backfill materials are mixtures of sand with fines and other composition. In order to provide some clarifications and evaluations on the role of fines in static liquefaction behaviour of sand sandy soils, the effect of fines on the liquefaction susceptibility of sand was experimentally examined in the present work over a range of fines content, relative density, and initial confining pressure. The results of an experimental study on various sand-fines mixtures are presented. Undrained static triaxial compression tests were conducted on saturated Perth sand containing 5% bentonite at three different relative densities (10, 50, and 90%), and saturated Perth sand containing both 5% bentonite and slag (2%, 4%, and 6%) at single relative density 10%. Undrained static triaxial tests were performed at three different initial confining pressures (100, 150, and 200 kPa). The brittleness index was used to quantify the liquefaction potential of sand-bentonite-slag mixtures. The results demonstrated that the liquefaction susceptibility of sand-5% bentonite mixture was more than liquefaction susceptibility of clean sandy soil. However, liquefaction potential decreased when both of two fines (bentonite and slag) were used. Liquefaction susceptibility of all mixtures decreased with increasing relative density and initial confining pressure.  

Keywords: liquefaction, bentonite, slag, brittleness index

Procedia PDF Downloads 219

Authors: Jasmin Cowin

Abstract:

Augmented reality digital technologies, big data, and the need for a teacher workforce able to meet the demands of a knowledge-based society are poised to lead to major changes in the field of education. This paper explores applications and educational use cases of augmented reality digital technologies for educational organizations during the Fourth Industrial Revolution. The Fourth Industrial Revolution requires vision, flexibility, and innovative educational conduits by governments and educational institutions to remain competitive in a global economy. Educational organizations will need to focus on teaching in and for a digital age to continue offering academic knowledge relevant to 21st-century markets and changing labor force needs. Implementation of contemporary disciplines will need to be embodied through learners’ active knowledge-making experiences while embracing ubiquitous accessibility. The power of distributed ledger technology promises major streamlining for educational record-keeping, degree conferrals, and authenticity guarantees. Augmented reality digital technologies hold the potential to restructure educational philosophies and their underpinning pedagogies thereby transforming modes of delivery. Structural changes in education and governmental planning are already increasing through intelligent systems and big data. Reimagining and redesigning education on a broad scale is required to plan and implement governmental and institutional changes to harness innovative technologies while moving away from the big schooling machine.

Keywords: fourth industrial revolution, artificial intelligence, big data, education, augmented reality digital technologies, distributed ledger technology

Procedia PDF Downloads 276

Authors: Malihe Ahmadi

Abstract:

Nowadays, stress, tension and neural problems are among the most important concerns of the present age. The environment plays key role on improving mental health and reducing stress of citizens. Establishing balance and appropriate relationship between city and natural environment is of the most important approaches of present century. Type of approach and logical planning for urban green spaces as one of the basic sections of integration with nature, not only plays key role on quality and efficiency of comprehensive urban planning; but also it increases the system of distributing social activities and happiness and lively property of urban environments that leads to permanent urban development. The main purpose of recovering urban identity is considering culture, history and human life style in past. This is a documentary-library research that evaluates the historical properties of Iranian gardens in compliance with climate condition. Results of this research reveal that in addition to following Iranian gardens from common principles of land lot, structure of flowers and plants, water, specific buildings during different ages, the role of climate at different urban areas is among the basics of determining method of designing green spaces and different buildings located at diverse areas i.e. Iranian gardens are a space for merging natural and artificial elements that has inseparable connection with semantic principles and guarantees different functions. Some of the necessities of designing present urban gardens are including: recognition and recreation.

Keywords: historical gardens, climate, properties of Iranian gardens, Iran

Procedia PDF Downloads 395

Authors: Di Luo, Maria Buchberger, Anja Pickhard

Abstract:

Objectives: The purpose of this study was to assess and compare the diagnostic accuracy of four common morphological approaches, including sonography, computed tomography (CT), positron emission tomography/computed tomography (PET/CT), and magnetic resonance imaging (MRI) for the evaluation of cervical lymph node metastases in head and neck squamous cell carcinoma (HNSCC) patients. Material and Methods: Included in this retrospective study were 26 patients diagnosed with HNSCC between 2010 and 2011 who all underwent sonography, CT, PET/CT, and MRI imaging before neck dissection. Morphological data were compared to the corresponding histopathological results. Statistical analysis was performed with SPSS statistic software (version 26.0), calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for detection of cervical lymph node metastases. Results: The 5-year survival rate of the patient collective was 55.5%.Risk factors for survival included initial primary tumor stage, initial lymph node stage, initial metastasis status, and therapeutic approaches. Cox regression showed initial metastasis status(HR 8.671, 95%CI 1.316-57.123, p=0.025) and therapeutic approaches(HR 6.699, 95%CI 1.746-25.700, p=0.006)to be independent predictive risk factors for survival. Sensitivity was highest for MRI (96% compared to 85% for sonography and 89% for CT and PET/CT). Specificity was comparable with 95 % for CT and 98 % for sonography and PET/CT, but only 68% for MRI. While the MRI showed the least PPV (34%) compared to all other methods (85% for sonography,75% for CT, and 86% for PET/CT), the NPV was comparable in all methods(98-99%). The overall accuracy of cervical lymph node metastases detection was comparable for sonography, CT, and PET/CT with 96%,97%,94%, respectively, while MRI had only 72% accuracy. Conclusion: Since the initial status of metastasis is an independent predictive risk factor for patients’ survival, efficient detection is crucial to plan adequate therapeutic approaches. Sonography, CT, and PET/CT have better diagnostic accuracy than MRI for the evaluation of cervical lymph node metastases in HNSCC patients.

Keywords: cervical lymph node metastases, diagnostic accuracy, head and neck squamous carcinoma, risk factors, survival

Procedia PDF Downloads 132

Authors: Nawal Yacoub Halim Abdelmasih

Abstract:

The intersection between development and human rights has been the factor of scholarly debate for a long term. therefore, some of standards, which enlarge from the proper to development to the human rights-based totally method to development, had been adopted to apprehend the dynamics among the two standards. no matter these attempts, the exact relationship among improvement and human rights has not been completely determined but. however, the inevitable interdependence between the two notions and the idea that improvement efforts ought to be undertaken with the aid of giving due regard to human rights ensures has won momentum in recent years. then again, the emergence of sustainable development as a extensively common technique in development dreams and policies makes this unsettled convergence even extra complicated. The vicinity of sustainable improvement in human rights regulation discourse and the function of the latter in making sure the sustainability of development applications name for a scientific observe. as a result, this newsletter seeks to discover the relationship among development and human rights, particularly focusing at the location given to sustainable development principles in international human proper regulation. it'll similarly quest whether or not there is a proper to sustainable improvement diagnosed therein. as a result, the item asserts that the ideas of sustainable improvement are immediately or circuitously diagnosed in diverse human rights contraptions, which affords an affirmative response to the question raised hereinabove. This paintings, therefore, will make expeditions via international and regional human rights devices in addition to case legal guidelines and interpretative hints of human rights bodies to show this speculation.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

Procedia PDF Downloads 29

Authors: Naim Izet Kajtazi

Abstract:

Context: The third ventricle colloid cyst (CC) is a benign growth usually located in the third ventricle and can cause various neurological symptoms, including sudden death. Modern surgical interventions may still result in a wide range of complications and cerebral venous thrombosis (CVT) is among them. Process: A 38-year-old female with an existing diagnosis of diabetes mellitus (DM) and hypothyroidism and a six-month history of headaches, blurred vision, and vomiting presented to our clinic three days after the headaches became excessively severe. Neurological examination on admission revealed bilateral papilledema without any associated focal neurological deficits. Brain computed tomography (CT) and magnetic resonance imaging (MRI) confirmed the presence of a third ventricle colloid cyst and associated non-communicating hydrocephalus involving the lateral ventricles. As a result, the patient underwent emergency bilateral external ventricular drainage (EVD) insertion followed by a third ventricular CC excision under neuronavigation through a right frontal craniotomy. Twelve days post-operatively, the patient developed further headaches, followed by a generalized tonic-clonic seizure that led to no postictal neurological deficits. Nonetheless, computed tomography venography of the brain revealed extensive thrombosis of the superior sagittal sinus, inferior sagittal sinus, right sigmoid sinus, and right internal jugular vein. A newly diagnosed CVT was treated with intravenous heparin. The patient was discharged with warfarin, which was discontinued after 12 months. Ten years after her illness, she remained stable and free from any neurological deficits but still suffered from mild chronic headaches. Outcome: Ten years after her illness, she remained stable and free from any neurological deficits but still suffered from mild chronic headaches. Relevance: A preoperative venous study should be performed in all cases to gain a better understanding of the venous anatomy. We advocate meticulous microsurgical techniques to protect the venous system surrounding the foramen of Monro and reduce the amount of retraction during surgery.

Keywords: CVT, seizures, third ventricle colloid cyst, MRI of brain

Procedia PDF Downloads 71

Authors: Wojciech Górecki, Anna Sowiżdżał, Grzegorz Machowski, Tomasz Maćkowski, Bartosz Papiernik, Michał Stefaniuk

Abstract:

The article shows results of the project which aims at evaluating possibilities of effective development and exploitation of natural gas from argillaceous series of the Autochthonous Miocene in the Carpathian Foredeep. To achieve the objective, the research team develop a world-trend based but unique methodology of processing and interpretation, adjusted to data, local variations and petroleum characteristics of the area. In order to determine the zones in which maximum volumes of hydrocarbons might have been generated and preserved as shale gas reservoirs, as well as to identify the most preferable well sites where largest gas accumulations are anticipated a number of task were accomplished. Evaluation of petrophysical properties and hydrocarbon saturation of the Miocene complex is based on laboratory measurements as well as interpretation of well-logs and archival data. The studies apply mercury porosimetry (MICP), micro CT and nuclear magnetic resonance imaging (using the Rock Core Analyzer). For prospective location (e.g. central part of Carpathian Foredeep – Brzesko-Wojnicz area) reprocessing and reinterpretation of detailed seismic survey data with the use of integrated geophysical investigations has been made. Construction of quantitative, structural and parametric models for selected areas of the Carpathian Foredeep is performed on the basis of integrated, detailed 3D computer models. Modeling are carried on with the Schlumberger’s Petrel software. Finally, prospective zones are spatially contoured in a form of regional 3D grid, which will be framework for generation modelling and comprehensive parametric mapping, allowing for spatial identification of the most prospective zones of unconventional gas accumulation in the Carpathian Foredeep. Preliminary results of research works indicate a potentially prospective area for occurrence of unconventional gas accumulations in the Polish part of Carpathian Foredeep.

Keywords: autochthonous Miocene, Carpathian foredeep, Poland, shale gas

Procedia PDF Downloads 226

Authors: Rohit Gupta, Ravinder Agarwal

Abstract:

Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: classifiers, feature selection, locomotion, sEMG

Procedia PDF Downloads 291

Authors: Muhammad Umair, Yasir Nawab, Khubab Shaker, Muhammad Maqsood, Adeel Zulfiqar, Danish Mahmood Baitab

Abstract:

Fabric is a flexible woven material consisting of a network of natural or artificial fibers often referred to as thread or yarn. Today fabrics are produced by weaving, braiding, knitting, tufting and non-woven. Weaving is a method of fabric production in which warp and weft yarns are interlaced perpendicular to each other. There is infinite number of ways for the interlacing of warp and weft yarn. Each way produces a different fabric structure. The yarns parallel to the machine direction are called warp yarns and the yarns perpendicular to the machine direction are called weft or filling yarns. Air jet weaving is the modern method of weft insertion and considered as high speed loom. The twist loss in air jet during weft insertion affects the strength. The aim of this study was to investigate the effect of twist change in weft yarn during air-jet weft insertion. A total number of 8 samples were produced using 1/1 plain and 3/1 twill weave design with two fabric widths having same loom settings. Two different types of yarns like cotton and PC blend were used. The effect of material type, weave design and fabric width on twist change of weft yarn was measured and discussed. Twist change in the different types of weft yarn and weave design was measured and compared the twist change in the weft yarn with the yarn before weft yarn insertion and twist loss is measured. Wider fabric leads to higher twist loss in the yarn.

Keywords: air jet loom, twist per inch, twist loss, weft yarn

Procedia PDF Downloads 401

Authors: Ozgu Hafizoglu

Abstract:

Analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with attributional, deep structural, casual relations that are essential to learning, to innovation in artificial worlds, and to discovery in science. Cognitive Model of Analogy (CMA) leads and creates information pattern transfer within and between domains and disciplines in science. This paper demonstrates the Cognitive Model of Analogy (CMA) as an evolutionary approach to scientific research. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions. In this paper, the model of analogical reasoning is created based on brain cells, their fractal, and operational forms within the system itself. Visualization techniques are used to show correspondences. Distinct phases of the problem-solving processes are divided thusly: encoding, mapping, inference, and response. The system is revealed relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain cells: glial cells, axons, axon terminals, and neurons, relative to matching conditions of analogical reasoning and relational information. It’s found that encoding, mapping, inference, and response processes in four-term analogical reasoning are corresponding with the fractal and operational forms of brain cells: glial, axons, and neurons.

Keywords: analogy, analogical reasoning, cognitive model, brain and glials

Procedia PDF Downloads 184

Authors: Sergio Celaschi, Henrique Canavarro de Alencar, Claaudecir Biazoli

Abstract:

Effluent quality is of the highest priority for compliance with the permit limits of environmental protection agencies and ensures the protection of their local water system. Of the pollutants monitored, the biochemical oxygen demand (BOD) posed one of the greatest challenges. This work presents a solution for wastewater treatment plants - WWTP’s ability to react to different situations and meet treatment goals. Delayed BOD5 results from the lab take 7 to 8 analysis days, hindered the WWTP’s ability to react to different situations and meet treatment goals. Reducing BOD turnaround time from days to hours is our quest. Such a solution is based on a system of two BOD bioreactors associated with Digital Twin (DT) and Machine Learning (ML) methodologies via an Internet of Things (IoT) platform to monitor and control a WWTP to support decision making. DT is a virtual and dynamic replica of a production process. DT requires the ability to collect and store real-time sensor data related to the operating environment. Furthermore, it integrates and organizes the data on a digital platform and applies analytical models allowing a deeper understanding of the real process to catch sooner anomalies. In our system of continuous time monitoring of the BOD suppressed by the effluent treatment process, the DT algorithm for analyzing the data uses ML on a chemical kinetic parameterized model. The continuous BOD monitoring system, capable of providing results in a fraction of the time required by BOD5 analysis, is composed of two thermally isolated batch bioreactors. Each bioreactor contains input/output access to wastewater sample (influent and effluent), hydraulic conduction tubes, pumps, and valves for batch sample and dilution water, air supply for dissolved oxygen (DO) saturation, cooler/heater for sample thermal stability, optical ODO sensor based on fluorescence quenching, pH, ORP, temperature, and atmospheric pressure sensors, local PLC/CPU for TCP/IP data transmission interface. The dynamic BOD system monitoring range covers 2 mg/L < BOD < 2,000 mg/L. In addition to the BOD monitoring system, there are many other operational WWTP sensors. The CPU data is transmitted/received to/from the digital platform, which in turn performs analyses at periodic intervals, aiming to feed the learning process. BOD bulletins and their credibility intervals are made available in 12-hour intervals to web users. The chemical kinetics ML algorithm is composed of a coupled system of four first-order ordinary differential equations for the molar masses of DO, organic material present in the sample, biomass, and products (CO₂ and H₂O) of the reaction. This system is solved numerically linked to its initial conditions: DO (saturated) and initial products of the kinetic oxidation process; CO₂ = H₂0 = 0. The initial values for organic matter and biomass are estimated by the method of minimization of the mean square deviations. A real case of continuous monitoring of BOD wastewater effluent quality is being conducted by deploying an IoT application on a large wastewater purification system located in S. Paulo, Brazil.

Keywords: effluent treatment, biochemical oxygen demand, continuous monitoring, IoT, machine learning

Procedia PDF Downloads 72

Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

Abstract:

Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

Procedia PDF Downloads 133

Authors: Safia Asif, Saadia Bano

Abstract:

In a world with rapidly depleting resources, using artificial lighting during daytime is an act of human ignorance. Imitated light is the major source of energy consumption in public buildings. Despite, the fact that substantial working hours of these buildings usually persist in natural daylight time; there is a trend of isolated, un-fenestrated and a-contextual interiors majorly dependent on active energy sources. On the contrary, if direct and un-controlled sunlight is allowed inside the building, it will create visual and thermal discomfort. Controlled daylighting with appropriate design mechanisms is one of the important aspects of achieving thermal and visual comfort. The natural sunlight can be utilized intelligently with the help of architectural thermal controlling mechanisms to achieve a healthy and productive environment. This paper is an attempt to investigate and analyze the importance of daylighting with reference to energy efficiency and thermal comfort. For this purpose, three public buildings including two educational institutions and one general post office are selected, as case-studies in the context of Karachi, Pakistan. Various parameters of visual and thermal comfort are analyzed which includes orientation, ceiling heights, overall building profile along with daylight controlling mechanisms in terms of penetration, distribution, protection, and control. In the later part of the research, a questionnaire survey is also conducted to evaluate the user experience in terms of adequate daylighting and thermal comfort.

Keywords: daylight, public buildings, sustainable architecture, visual and thermal comfort

Procedia PDF Downloads 209

Authors: Yehjune Heo

Abstract:

Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: anti-spoofing, CNN, fingerprint recognition, GAN

Procedia PDF Downloads 183

Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

Abstract:

As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

Procedia PDF Downloads 110

Authors: Hugo Daneluzzo, Christelle Rabbat, Alan Jean-Marie

Abstract:

Water electrolysis is one of the most advanced technologies for producing hydrogen and can be easily combined with electricity from different sources. Under the influence of electric current, water molecules can be split into oxygen and hydrogen. The production of hydrogen by water electrolysis favors the integration of renewable energy sources into the energy mix by compensating for their intermittence through the storage of the energy produced when production exceeds demand and its release during off-peak production periods. Among the various electrolysis technologies, anion exchange membrane (AEM) electrolyser cells are emerging as a reliable technology for water electrolysis. Modeling and simulation are effective tools to save time, money, and effort during the optimization of operating conditions and the investigation of the design. The modeling and simulation become even more important when dealing with multiphysics dynamic systems. One of those systems is the AEM electrolysis cell involving complex physico-chemical reactions. Once developed, models may be utilized to comprehend the mechanisms to control and detect flaws in the systems. Several modeling methods have been initiated by scientists. These methods can be separated into two main approaches, namely equation-based modeling and graph-based modeling. The former approach is less user-friendly and difficult to update as it is based on ordinary or partial differential equations to represent the systems. However, the latter approach is more user-friendly and allows a clear representation of physical phenomena. In this case, the system is depicted by connecting subsystems, so-called blocks, through ports based on their physical interactions, hence being suitable for multiphysics systems. Among the graphical modelling methods, the bond graph is receiving increasing attention as being domain-independent and relying on the energy exchange between the components of the system. At present, few studies have investigated the modelling of AEM systems. A mathematical model and a bond graph model were used in previous studies to model the electrolysis cell performance. In this study, experimental data from literature were simulated using OpenModelica using bond graphs and mathematical approaches. The polarization curves at different operating conditions obtained by both approaches were compared with experimental ones. It was stated that both models predicted satisfactorily the polarization curves with error margins lower than 2% for equation-based models and lower than 5% for the bond graph model. The activation polarization of hydrogen evolution reactions (HER) and oxygen evolution reactions (OER) were behind the voltage loss in the AEM electrolyzer, whereas ion conduction through the membrane resulted in the ohmic loss. Therefore, highly active electro-catalysts are required for both HER and OER while high-conductivity AEMs are needed for effectively lowering the ohmic losses. The bond graph simulation of the polarisation curve for operating conditions at various temperatures has illustrated that voltage increases with temperature owing to the technology of the membrane. Simulation of the polarisation curve can be tested virtually, hence resulting in reduced cost and time involved due to experimental testing and improved design optimization. Further improvements can be made by implementing the bond graph model in a real power-to-gas-to-power scenario.

Keywords: hydrogen production, anion-exchange membrane, electrolyzer, mathematical modeling, multiphysics modeling

Procedia PDF Downloads 89

Authors: Christodoulou Christos, Politis Anastasios

Abstract:

The utilization of Wireless Body Area Network (WBAN) is experiencing a notable surge in popularity as a result of its widespread implementation in the field of smart health. WBANs utilize small sensors implanted within the human body to monitor and record physiological indicators. These sensors transmit the collected data to hospitals and healthcare facilities through designated access points. Bio-sensors exhibit a diverse array of shapes and sizes, and their deployment can be tailored to the condition of the individual. Multiple sensors may be strategically placed within, on, or around the human body to effectively observe, record, and transmit essential physiological indicators. These measurements serve as a basis for subsequent analysis, evaluation, and therapeutic interventions. In conjunction with physical health concerns, numerous smartwatches are engineered to employ artificial intelligence techniques for the purpose of detecting mental health conditions such as depression and anxiety. The utilization of smartwatches serves as a secure and cost-effective solution for monitoring mental health. Physiological signals are widely regarded as a highly dependable method for the recognition of emotions due to the inherent inability of individuals to deliberately influence them over extended periods of time. The techniques that WBANs employ to recognize emotions are thoroughly examined in this article.

Keywords: emotion recognition, wireless body area network, WBAN, ERC, wearable devices, psychological signals, emotion, smart-watch, prediction

Procedia PDF Downloads 49

Authors: Geum Yeon Sim, Tyler Pigott, Julio Vasquez

Abstract:

A 38-year-old obese female with no apparent past medical history presented with left knee pain. Six months ago, she sustained a left knee dislocation in a motor vehicle accident that was managed with a skin graft over the left lower extremity without any reconstructive surgery. She developed persistent pain and stiffness in her left knee that worsened with walking and stair climbing. Examination revealed healed extensive skin graft over the left lower extremity, including the left knee. Palpation showed moderate tenderness along the superior border of the patella, exquisite tenderness over MCL, and mild tenderness on the tibial tuberosity. There was normal sensation, reflexes, and strength in her lower extremities. There was limited active and passive range of motion of her left knee during flexion. There was instability noted upon the valgus stress test of the left knee. Left knee magnetic resonance imaging showed high-grade (grade 2-3) injury of the proximal superficial fibers of the MCL and diffuse thickening and signal abnormality of the cruciate ligaments, as well as edema-like subchondral marrow signal change in the anterolateral aspect of the lateral femoral condyle weight-bearing surface. There was also notable extensive scarring and edema of the skin, subcutaneous soft tissues, and musculature surrounding the knee. The patient was managed with left knee immobilization for five months, which was complicated by limited knee flexion. Physical therapy consisting of quadriceps, hamstrings, gastrocnemius stretching and strengthening, range of motion exercises, scar/soft tissue mobilization, and gait training was given with marked improvement in pain and range of motion. The patient experienced a further reduction in pain as well as an improvement in function with home exercises consisting of continued strengthening and stretching.

Keywords: ligamentous injury, trauma, rehabilitation, knee pain

Procedia PDF Downloads 106

Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

Abstract:

During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

Procedia PDF Downloads 130

Authors: Sila Akhan, Muge Toygar, Murat Sayan, Simge Fidan

Abstract:

Chronic viral hepatitis B, C, and D can cause hepatocellular carcinoma (HCC), cirrhosis and death. The proper treatment reduce the risk of development of HCC importantly, but not to zero point. Materials and Methods: We analysed retrospectively our chronic viral hepatitis B, C and D patients who attended to our Infectious Diseases policlinic between 2004-2018. From 589 biopsy-proven chronic hepatitis patients 3 have hepatocellular carcinoma on our follow up. First case is 74 years old patient. His HCV infection diagnosis was made 8 years ago. First treatment was pegylated interferon plus ribavirin only 28 weeks, because of HCV RNA breakthrough under treatment. In 2013 he was retreated with telaprevir, pegylated interferon plus ribavirin 24 weeks. But at the end of the therapy HCV RNA was found 1.290.000 IU/mL. He has abdominal ultrasonography (US) controls and alpha-fetoprotein (AFP) at 6 months intervals. All seemed normal until 2015 then he has an abdominal magnetic resonance imaging (MRI) and found HCC by chance. His treatment began in Oncology Clinic after verified with biopsy of HCC. And then sofosbuvir/ledipasvir was given to him for HCV 24 weeks. Sustained virologic response (SVR) was obtained. He is on cure for HCV infection and under control of Oncology for HCC. Second patient is 36 years old man. He knows his HBV infection since 2008. HBsAg and HBeAg positive; HDV RNA negative. Liver biopsy revealed grade:4, stage 3-4 according modified Knodell scoring system. In 2010 tenofovir treatment was began. His abdominal US and AFP were normal. His controls took place at 6 months intervals and HBV DNA negative, US, and AFP were normal until 2016 continuously. AFP found 37 above the normal range and then HCC was found in MRI. Third patient is 57 years old man. As hepatitis B infection was first diagnosed; he has cirrhosis and was began tenofovir as treatment. In short time he has HCC despite normal AFP values. Conclusion: In Mediterranian countries including Turkey naturally occurring pre-S/S variants are more than 75% of all chronic hepatitis B patients. This variants may contribute to the development of progressive liver damage and hepatocarcinogenesis. HCV-induced development of HCC is a gradual process and is affected by the duration of disease and viral genotype. All the chronic viral hepatitis patients should be followed up in 6 months intervals not only with US and AFP for HCC. Despite they have proper treatment there is always the risk development of HCC. Chronic hepatitis patients cannot be dropped from follow up even treated well.

Keywords: HCC, HCV, HBV, DAA

Procedia PDF Downloads 134

Authors: Nathaniel Carpenter

Abstract:

Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

Procedia PDF Downloads 109

Authors: Deepak Kakde, Steve Howdle, Derek Irvine, Cameron Alexander

Abstract:

The poor aqueous solubility is one of the major obstacles in the formulation development of many drugs. Around 70% of drugs are poorly soluble in aqueous media. In the last few decades, micelles have emerged as one of the major tools for solubilization of hydrophobic drugs. Micelles are nanosized structures (10-100nm) obtained by self-assembly of amphiphilic molecules into the water. The hydrophobic part of the micelle forms core which is surrounded by a hydrophilic outer shell called corona. These core-shell structures have been used as a drug delivery vehicle for many years. Although, the utility of micelles have been reduced due to the lack of sustainable materials. In the present study, a novel methoxy poly(ethylene glycol)-b-poly(ε-decalactone) (mPEG-b-PεDL) copolymer was synthesized by ring opening polymerization (ROP) of renewable ε-decalactone (ε-DL) monomers on methoxy poly(ethylene glycol) (mPEG) initiator using 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) as a organocatalyst. All the reactions were conducted in bulk to avoid the use of toxic organic solvents. The copolymer was characterized by nuclear magnetic resonance spectroscopy (NMR), gel permeation chromatography (GPC) and differential scanning calorimetry (DSC).The mPEG-b-PεDL block copolymeric micelles containing indomethacin (IND) were prepared by nanoprecipitation method and evaluated as drug delivery vehicle. The size of the micelles was less than 40nm with narrow polydispersity pattern. TEM image showed uniform distribution of spherical micelles defined by clear surface boundary. The indomethacin loading was 7.4% for copolymer with molecular weight of 13000 and drug/polymer weight ratio of 4/50. The higher drug/polymer ratio decreased the drug loading. The drug release study in PBS (pH7.4) showed a sustained release of drug over a period of 24hr. In conclusion, we have developed a new sustainable polymeric material for IND delivery by combining the green synthetic approach with the use of renewable monomer for sustainable development of polymeric nanomedicine.

Keywords: dopolymer, ε-decalactone, indomethacin, micelles

Procedia PDF Downloads 294