Search results for: machine monitoring

Authors: Evgenia Iakovleva, Juha Koivisto, Pasi Karppinen, J. Inkinen, Mikko Alava

Abstract:

Preventing the spread of infectious diseases throughout the worldwide is one of the most important tasks of modern health care. Infectious diseases not only account for one fifth of the deaths in the world, but also cause many pathological complications for the human health. Touch surfaces pose an important vector for the spread of infections by varying microorganisms, including antimicrobial resistant organisms. Further, antimicrobial resistance is reply of bacteria to the overused or inappropriate used of antibiotics everywhere. The biggest challenges in bacterial detection by existing methods are non-direct determination, long time of analysis, the sample preparation, use of chemicals and expensive equipment, and availability of qualified specialists. Therefore, a high-performance, rapid, real-time detection is demanded in rapid practical bacterial detection and to control the epidemiological hazard. Among the known methods for determining bacteria on the surfaces, Hyperspectral methods can be used as direct and rapid methods for microorganism detection on different kind of surfaces based on fluorescence without sampling, sample preparation and chemicals. The aim of this study was to assess the relevance of such systems to remote sensing of surfaces for microorganisms detection to prevent a global spread of infectious diseases. Bacillus subtilis and Escherichia coli with different concentrations (from 0 to 10x8 cell/100µL) were detected with hyperspectral camera using different filters as visible visualization of bacteria and background spots on the steel plate. A method of internal standards was applied for monitoring the correctness of the analysis results. Distances from sample to hyperspectral camera and light source are 25 cm and 40 cm, respectively. Each sample is optically imaged from the surface by hyperspectral imaging system, utilizing a JAI CM-140GE-UV camera. Light source is BeamZ FLATPAR DMX Tri-light, 3W tri-colour LEDs (red, blue and green). Light colors are changed through DMX USB Pro interface. The developed system was calibrated following a standard procedure of setting exposure and focused for light with λ=525 nm. The filter is ThorLabs KuriousTM hyperspectral filter controller with wavelengths from 420 to 720 nm. All data collection, pro-processing and multivariate analysis was performed using LabVIEW and Python software. The studied human eye visible and invisible bacterial stains clustered apart from a reference steel material by clustering analysis using different light sources and filter wavelengths. The calculation of random and systematic errors of the analysis results proved the applicability of the method in real conditions. Validation experiments have been carried out with photometry and ATP swab-test. The lower detection limit of developed method is several orders of magnitude lower than for both validation methods. All parameters of the experiments were the same, except for the light. Hyperspectral imaging method allows to separate not only bacteria and surfaces, but also different types of bacteria, such as Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. Developed method allows skipping the sample preparation and the use of chemicals, unlike all other microbiological methods. The time of analysis with novel hyperspectral system is a few seconds, which is innovative in the field of microbiological tests.

Keywords: Escherichia coli, Bacillus subtilis, hyperspectral imaging, microorganisms detection

Procedia PDF Downloads 203

Authors: Amanda Kavner, Richard Lamb

Abstract:

Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

Procedia PDF Downloads 106

Authors: Vasundhara Arora, Anupam Jhobta, Suresh Thakur, Sanjiv Sharma

Abstract:

Aims and Objectives: Intracranial tuberculosis (TB) is one of the most devastating manifestations of TB and a challenging public health issue of considerable importance and magnitude world over. This study elaborates on the imaging spectrum of neurotuberculosis on magnetic resonance imaging (MRI) in 29 clinically suspected cases from a tertiary care hospital. Materials and Methods: The prospective hospital based evaluation of MR imaging features of neuro-tuberculosis in 29 clinically suspected cases was carried out in Department of Radio-diagnosis, Indira Gandhi Medical Hospital from July 2017 to August 2018. MR Images were obtained on a 1.5 T Magnetom Avanto machine and were analyzed to identify any abnormal meningeal enhancement or parenchymal lesions. Microbiological and Biochemical CSF analysis was performed in radio-logically suspected cases and the results were compared with the imaging data. Clinical follow up of the patients started on anti-tuberculous treatment was done to evaluate the response to treatment and clinical outcome. Results: Age range of patients in the study was between 1 year to 73 years. The mean age of presentation was 11.5 years. No significant difference in the distribution of cerebral tuberculosis was noted among the two genders. Imaging findings of neuro-tuberculosis obtained were varied and non specific ranging from lepto-meningeal enhancement, cerebritis to space occupying lesions such as tuberculomas and tubercular abscesses. Complications presenting as hydrocephalus (n= 7) and infarcts (n=9) was noted in few of these patients. 29 patients showed radiological suspicion of CNS tuberculosis with meningitis alone observed in 11 cases, tuberculomas alone were observed in 4 cases, meningitis with parenchymal tuberculomas in 11 cases. Tubercular abscess and cerebritis were observed in one case each. Tuberculous arachnoiditis was noted in one patient. Gene expert positivity was obtained in 11 out of 29 radiologically suspected patients; none of the patients showed culture positivity. Meningeal form of the disease alone showed higher positivity rate of gene Xpert (n=5) followed by combination of meningeal and parenchymal forms of disease (n=4). The parenchymal manifestation of disease alone showed least positivity rates (n= 3) with gene xpert testing. All 29 patients were started on anti tubercular treatment based on radiological suspicion of the disease with clinical improvement observed in 27 treated patients. Conclusions: In our study, higher incidence of neuro- tuberculosis was noted in paediatric population with predominance of the meningeal form of the disease. Gene Xpert positivity obtained was low due to paucibacillary nature of cerebrospinal fluid (CSF) with even lower positivity of CSF samples in parenchymal form of the manifestation. MRI showed high accuracy in detecting CNS lesions in neuro-tuberculosis. Hence, it can be concluded that MRI plays a crucial role in the diagnosis because of its inherent sensitivity and specificity and is an indispensible imaging modality. It caters to the need of early diagnosis owing to poor sensitivity of microbiological tests more so in the parenchymal manifestation of the disease.

Keywords: neurotuberculosis, tubercular abscess, tuberculoma, tuberculous meningitis

Procedia PDF Downloads 149

Authors: Petros M. Chronopoulos, Evangelos Z. Astreinidis

Abstract:

A major issue of the structural assessment and rehabilitation of existing RC structures is the inadequate lap-splicing of the longitudinal reinforcement. Although prohibited by modern Design Codes, the practice of arranging lap-splices inside the critical regions of RC elements was commonly applied in the past. Today this practice is still the rule, at least for conventional new buildings. Therefore, a lot of relevant research is ongoing in many earthquake prone countries. The rehabilitation of deficient lap-splices of RC elements by means of external confinement is widely accepted as the most efficient technique. If correctly applied, this versatile technique offers a limited increase of flexural capacity and a considerable increase of local ductility and of axial and shear capacities. Moreover, this intervention does not affect the stiffness of the elements and does not affect the dynamic characteristics of the structure. This technique has been extensively discussed and researched contributing to vast accumulation of technical and scientific knowledge that has been reported in relevant books, reports and papers, and included in recent Design Codes and Guides. These references are mostly dealing with modeling and redesign, covering both the enhanced (axial and) shear capacity (due to the additional external closed hoops or jackets) and the increased ductility (due to the confining action, preventing the unzipping of lap-splices and the buckling of continuous reinforcement). An analytical and experimental program devoted to RC members with lap-splices is completed in the Lab. of RC/NTU of Athens/GR. This program aims at the proposal of a rational and safe theoretical model and the calibration of the relevant Design Codes’ provisions. Tests, on forty two (42) full scale specimens, covering mostly beams and columns (not walls), strengthened or not, with adequate or inadequate lap-splices, have been already performed and evaluated. In this paper, the results of twelve (12) specimens under fully reversed cyclic actions are presented and discussed. In eight (8) specimens the lap-splices were inadequate (splicing length of 20 or 30 bar diameters) and they were retrofitted before testing by means of additional external confinement. The two (2) most commonly applied confining materials were used in this study, namely steel and FRPs. More specifically, jackets made of CFRP wraps or light cages made of mild steel were applied. The main parameters of these tests were (i) the degree of confinement (internal and external), and (ii) the length of lap-splices, equal to 20, 30 or 45 bar diameters. These tests were thoroughly instrumented and monitored, by means of conventional (LVDTs, strain gages, etc.) and innovative (optic fibre-Bragg-grating) sensors. This allowed for a thorough investigation of the most influencing design parameter, namely the hoop-stress developed in the confining material. Based on these test results and on comparisons with the provisions of modern Design Codes, it could be argued that shorter (than the normative) lap-splices, commonly found in old structures, could still be effective and safe (at least for lengths more than an absolute minimum), depending on the required ductility, if a properly arranged and adequately detailed external confinement is applied.

Keywords: concrete, fibre-Bragg-grating sensors, lap-splices, retrofitting / rehabilitation

Procedia PDF Downloads 239

Authors: Reggie Chandra

Abstract:

Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

Procedia PDF Downloads 142

Authors: Mayank Gupta, Siba Prasad Samal, Vasu Kakkirala

Abstract:

The internet world and its priorities have changed considerably in the last decade. Browsing on smart phones has increased manifold and is set to explode much more. Users spent considerable time browsing different websites, that gives a great deal of insight into user’s preferences. Instead of plain information classifying different aspects of browsing like Bookmarks, History, and Download Manager into useful categories would improve and enhance the user’s experience. Most of the classification solutions are server side that involves maintaining server and other heavy resources. It has security constraints and maybe misses on contextual data during classification. On device, classification solves many such problems, but the challenge is to achieve accuracy on classification with resource constraints. This on device classification can be much more useful in personalization, reducing dependency on cloud connectivity and better privacy/security. This approach provides more relevant results as compared to current standalone solutions because it uses content rendered by browser which is customized by the content provider based on user’s profile. This paper proposes a Naive Bayes based lightweight classification engine targeted for a resource constraint devices. Our solution integrates with Web Browser that in turn triggers classification algorithm. Whenever a user browses a webpage, this solution extracts DOM Tree data from the browser’s rendering engine. This DOM data is a dynamic, contextual and secure data that can’t be replicated. This proposal extracts different features of the webpage that runs on an algorithm to classify into multiple categories. Naive Bayes based engine is chosen in this solution for its inherent advantages in using limited resources compared to other classification algorithms like Support Vector Machine, Neural Networks, etc. Naive Bayes classification requires small memory footprint and less computation suitable for smartphone environment. This solution has a feature to partition the model into multiple chunks that in turn will facilitate less usage of memory instead of loading a complete model. Classification of the webpages done through integrated engine is faster, more relevant and energy efficient than other standalone on device solution. This classification engine has been tested on Samsung Z3 Tizen hardware. The Engine is integrated into Tizen Browser that uses Chromium Rendering Engine. For this solution, extensive dataset is sourced from dmoztools.net and cleaned. This cleaned dataset has 227.5K webpages which are divided into 8 generic categories ('education', 'games', 'health', 'entertainment', 'news', 'shopping', 'sports', 'travel'). Our browser integrated solution has resulted in 15% less memory usage (due to partition method) and 24% less power consumption in comparison with standalone solution. This solution considered 70% of the dataset for training the data model and the rest 30% dataset for testing. An average accuracy of ~96.3% is achieved across the above mentioned 8 categories. This engine can be further extended for suggesting Dynamic tags and using the classification for differential uses cases to enhance browsing experience.

Keywords: chromium, lightweight engine, mobile computing, Naive Bayes, Tizen, web browser, webpage classification

Procedia PDF Downloads 143

Authors: Agata Migdalska, Joanna Berczynska, Ewa Bieniek, Jacek Sterna

Abstract:

Muscle relaxation is considered important during general anesthesia for spine stabilization. In a presented case peripherally acting muscle relaxant was applied during general anesthesia for spine stabilization surgery. The patient was a dog, 11-years old, 26 kg, male, mix breed. Spine fracture was situated between Th13-L1-L2, probably due to the car accident. Preanesthetic physical examination revealed no sign underlying health issues. The dog was premedicated with midazolam 0.2 mg IM and butorphanol 2.4 mg IM. General anesthesia was induced with propofol IV. After the induction, the dog was intubated with an endotracheal tube and connected to an open-ended rebreathing system and maintained with the use of inhalation anesthesia with isoflurane in oxygen. 0,5 mg/ kg of rocuronium was given IV. Use of muscle relaxant was accompanied by an assessment of the degree of neuromuscular blockade by peripheral nerve stimulator. Electrodes were attached to the skin overlying at the peroneal nerve at the lateral cranial tibia. Four electrical pulses were applied to the nerve over a 2 second period. When satisfying nerve block was detected dog was prepared for the surgery. No further monitoring of the effectiveness of blockade was performed during surgery. Mechanical ventilation was kept during anesthesia. During surgery dog maintain stable, and no anesthesiological complication occur. Intraoperatively surgeon claimed that neuromuscular blockade results in a better approach to the spine and easier muscle manipulation which was helpful in order to see the fracture and replace bone fragments. Finally, euthanasia was performed intraoperatively as a result of vast myelomalacia process of the spinal cord. This prevented examination of the recovering process. Neuromuscular blocking agents act at the neuromuscular junction to provide profound muscle relaxation throughout the body. Muscle blocking agents are neither anesthetic nor analgesic; therefore inappropriately used may cause paralysis in fully conscious and feeling pain patient. They cause paralysis of all skeletal muscles, also diaphragm and intercostal muscles when given in higher doses. Intraoperative management includes maintaining stable physiological conditions, which involves adjusting hemodynamic parameters, ensuring proper ventilation, avoiding variations in temperature, maintain normal blood flow to promote proper oxygen exchange. Neuromuscular blocking agent can cause many side effects like residual paralysis, anaphylactic or anaphylactoid reactions, delayed recovery from anesthesia, histamine release, recurarization. Therefore reverse drug like neostigmine (with glikopyrolat) or edrofonium (with atropine) should be used in case of a life-threatening situation. Another useful drug is sugammadex, although the cost of this drug strongly limits its use. Muscle relaxant improves surgical conditions during spinal surgery, especially in heavily muscled individuals. They are also used to facilitate the replacement of dislocated joints as they improve conditions during fracture reduction. It is important to emphasize that in a patient with muscle weakness neuromuscular blocking agents may result in intraoperative and early postoperative cardiovascular and respiratory complications, as well as prolonged recovery from anesthesia. This should not appear in patients with recent spine fracture or luxation. Therefore it is believed that neuromuscular blockers could be useful during spine stabilization procedures.

Keywords: anesthesia, dog, neuromuscular block, spine surgery

Procedia PDF Downloads 159

Authors: Islam Sherif, Ahmed Ashour, Ahmed Hassan, Hatem Osman

Abstract:

Anterior Cruciate Ligament (ACL) injuries are common among athletes and individuals participating in sports with sudden stops, pivots, and changes in direction. Arthroscopic surgery is the gold standard for ACL reconstruction, aiming to restore knee stability and function. Recent years have witnessed significant advancements in arthroscopic surgery techniques, graft materials, and technological innovations, revolutionizing the field of ACL reconstruction. This presentation delves into the latest advancements in arthroscopic surgery techniques for ACL reconstruction and their potential impact on patient outcomes. Traditionally, autografts from the patellar tendon, hamstring tendon, or quadriceps tendon have been commonly used for ACL reconstruction. However, recent studies have explored the use of allografts, synthetic scaffolds, and tissue-engineered grafts as viable alternatives. This abstract evaluates the benefits and potential drawbacks of each graft type, considering factors such as graft incorporation, strength, and risk of graft failure. Moreover, the application of augmented reality (AR) and virtual reality (VR) technologies in surgical planning and intraoperative navigation has gained traction. AR and VR platforms provide surgeons with detailed 3D anatomical reconstructions of the knee joint, enhancing preoperative visualization and aiding in graft tunnel placement during surgery. We discuss the integration of AR and VR in arthroscopic ACL reconstruction procedures, evaluating their accuracy, cost-effectiveness, and overall impact on surgical outcomes. Beyond graft selection and surgical navigation, patient-specific planning has gained attention in recent research. Advanced imaging techniques, such as MRI-based personalized planning, enable surgeons to tailor ACL reconstruction procedures to each patient's unique anatomy. By accounting for individual variations in the femoral and tibial insertion sites, this personalized approach aims to optimize graft placement and potentially improve postoperative knee kinematics and stability. Furthermore, rehabilitation and postoperative care play a crucial role in the success of ACL reconstruction. This abstract explores novel rehabilitation protocols, emphasizing early mobilization, neuromuscular training, and accelerated recovery strategies. Integrating technology, such as wearable sensors and mobile applications, into postoperative care can facilitate remote monitoring and timely intervention, contributing to enhanced rehabilitation outcomes. In conclusion, this presentation provides an overview of the cutting-edge advancements in arthroscopic surgery techniques for ACL reconstruction. By embracing innovative graft materials, augmented reality, patient-specific planning, and technology-driven rehabilitation, orthopedic surgeons and sports medicine specialists can achieve superior outcomes in ACL injury management. These developments hold great promise for improving the functional outcomes and long-term success rates of ACL reconstruction, benefitting athletes and patients alike.

Keywords: arthroscopic surgery, ACL, autograft, allograft, graft materials, ACL reconstruction, synthetic scaffolds, tissue-engineered graft, virtual reality, augmented reality, surgical planning, intra-operative navigation

Procedia PDF Downloads 73

Authors: Sandra Regina Cavalcante, Rodolfo A. G. Vilela

Abstract:

Introduction: Work accidents and occupational diseases are a big problem for public health around the world and the main health problem of workers with high social and economic costs. Brazil has shown progress over the last years, with the development of the regulatory system to improve safety and quality of life in the workplace. However, the situation is far from acceptable, because the occurrences remain high and there is a great gap between legislation and reality, generated by the low level of voluntary compliance with the law. Brazilian laws provide procedural legal instruments for both, to compensate the damage caused to the worker's health and to prevent future injuries. In the Judiciary, the prevention idea is in the collective action, effected through Brazilian Class Actions. Inhibitory guardianships may impose both, improvements to the working environment, as well as determine the interruption of activity or a ban on the machine that put workers at risk. Both the Labor Prosecution and trade unions have to stand to promote this type of action, providing payment of compensation for collective moral damage. Objectives: To verify how class actions (known as ‘public civil actions’), regulated in Brazilian legal system to protect diffuse, collective and homogeneous rights, are being used to protect workers' health and safety. Methods: The author identified and evaluated decisions of Brazilian Superior Court of Labor involving collective actions and work accidents. The timeframe chosen was December 2015. The online jurisprudence database was consulted in page available for public consultation on the court website. The categorization of the data was made considering the result (court application was rejected or accepted), the request type, the amount of compensation and the author of the cause, besides knowing the reasoning used by the judges. Results: The High Court issued 21,948 decisions in December 2015, with 1448 judgments (6.6%) about work accidents and only 20 (0.09%) on collective action. After analyzing these 20 decisions, it was found that the judgments granted compensation for collective moral damage (85%) and/or obligation to make, that is, changes to improve prevention and safety (71%). The processes have been filed mainly by the Labor Prosecutor (83%), and also appeared lawsuits filed by unions (17%). The compensation for collective moral damage had average of 250,000 reais (about US$65,000), but it should be noted that there is a great range of values found, also are several situations repaired by this compensation. This is the last instance resource for this kind of lawsuit and all decisions were well founded and received partially the request made for working environment protection. Conclusions: When triggered, the labor court system provides the requested collective protection in class action. The values of convictions arbitrated in collective actions are significant and indicate that it creates social and economic repercussions, stimulating employers to improve the working environment conditions of their companies. It is necessary to intensify the use of collective actions, however, because they are more efficient for prevention than reparatory individual lawsuits, but it has been underutilized, mainly by Unions.

Keywords: Brazilian Class Action, collective action, work accident penalization, workplace accident prevention, workplace protection law

Procedia PDF Downloads 259

Authors: Hazel James P. Agngarayngay, Arnold R. Elepaño

Abstract:

Mango is the third most important export fruit in the Philippines. Despite the expanding mango trade in world market, problems on postharvest losses caused by pests and diseases are still prevalent. Many disease control and pest disinfestation methods have been studied and adopted. Heat treatment is necessary to eliminate pests and diseases to be able to pass the quarantine requirements of importing countries. During heat treatments, temperature and time are critical because fruits can easily be damaged by over-exposure to heat. Modeling the process enables researchers and engineers to study the behaviour of temperature distribution within the fruit over time. Understanding physical processes through modeling and simulation also saves time and resources because of reduced experimentation. This research aimed to simulate the heat transfer mechanism and predict the temperature distribution in ‘Carabao' mangoes during hot water treatment (HWT) and extended hot water treatment (EHWT). The simulation was performed in ANSYS CFD Software, using ANSYS CFX Solver. The simulation process involved model creation, mesh generation, defining the physics of the model, solving the problem, and visualizing the results. Boundary conditions consisted of the convective heat transfer coefficient and a constant free stream temperature. The three-dimensional energy equation for transient conditions was numerically solved to obtain heat flux and transient temperature values. The solver utilized finite volume method of discretization. To validate the simulation, actual data were obtained through experiment. The goodness of fit was evaluated using mean temperature difference (MTD). Also, t-test was used to detect significant differences between the data sets. Results showed that the simulations were able to estimate temperatures accurately with MTD of 0.50 and 0.69 °C for the HWT and EHWT, respectively. This indicates good agreement between the simulated and actual temperature values. The data included in the analysis were taken at different locations of probe punctures within the fruit. Moreover, t-tests showed no significant differences between the two data sets. Maximum heat fluxes obtained at the beginning of the treatments were 394.15 and 262.77 J.s-1 for HWT and EHWT, respectively. These values decreased abruptly at the first 10 seconds and gradual decrease was observed thereafter. Data on heat flux is necessary in the design of heaters. If underestimated, the heating component of a certain machine will not be able to provide enough heat required by certain operations. Otherwise, over-estimation will result in wasting of energy and resources. This study demonstrated that the simulation was able to estimate temperatures accurately. Thus, it can be used to evaluate the influence of various treatment conditions on the temperature-time history in mangoes. When combined with information on insect mortality and quality degradation kinetics, it could predict the efficacy of a particular treatment and guide appropriate selection of treatment conditions. The effect of various parameters on heat transfer rates, such as the boundary and initial conditions as well as the thermal properties of the material, can be systematically studied without performing experiments. Furthermore, the use of ANSYS software in modeling and simulation can be explored in modeling various systems and processes.

Keywords: heat transfer, heat treatment, mango, modeling and simulation

Procedia PDF Downloads 235

Authors: Antonella Falanga, Armando Cartenì

Abstract:

"Big data" refers to extremely vast and complex sets of data, encompassing extraordinarily large and intricate datasets that require specific tools for meaningful analysis and processing. These datasets can stem from diverse origins like sensors, mobile devices, online transactions, social media platforms, and more. The utilization of big data is pivotal, offering the chance to leverage vast information for substantial advantages across diverse fields, thereby enhancing comprehension, decision-making, efficiency, and fostering innovation in various domains. Big data, distinguished by its remarkable attributes of enormous volume, high velocity, diverse variety, and significant value, represent a transformative force reshaping the industry worldwide. Their pervasive impact continues to unlock new possibilities, driving innovation and advancements in technology, decision-making processes, and societal progress in an increasingly data-centric world. The use of these technologies is becoming more widespread, facilitating and accelerating operations that were once much more complicated. In particular, big data impacts across multiple sectors such as business and commerce, healthcare and science, finance, education, geography, agriculture, media and entertainment and also mobility and logistics. Within the transportation sector, which is the focus of this study, big data applications encompass a wide variety, spanning across optimization in vehicle routing, real-time traffic management and monitoring, logistics efficiency, reduction of travel times and congestion, enhancement of the overall transportation systems, but also mitigation of pollutant emissions contributing to environmental sustainability. Meanwhile, in public administration and the development of smart cities, big data aids in improving public services, urban planning, and decision-making processes, leading to more efficient and sustainable urban environments. Access to vast data reservoirs enables deeper insights, revealing hidden patterns and facilitating more precise and timely decision-making. Additionally, advancements in cloud computing and artificial intelligence (AI) have further amplified the potential of big data, enabling more sophisticated and comprehensive analyses. Certainly, utilizing big data presents various advantages but also entails several challenges regarding data privacy and security, ensuring data quality, managing and storing large volumes of data effectively, integrating data from diverse sources, the need for specialized skills to interpret analysis results, ethical considerations in data use, and evaluating costs against benefits. Addressing these difficulties requires well-structured strategies and policies to balance the benefits of big data with privacy, security, and efficient data management concerns. Building upon these premises, the current research investigates the efficacy and influence of big data by conducting an overview of the primary and recent implementations of big data in transportation systems. Overall, this research allows us to conclude that big data better provide to enhance rational decision-making for mobility choices and is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, public transport, sustainable mobility, transport demand, transportation planning

Procedia PDF Downloads 44

Authors: Pedro Llanos, Diego García

Abstract:

Spaceflight is considered the last frontier in terms of science, technology, and engineering. But it is also the next frontier in terms of human physiology and performance. After more than 200,000 years humans have evolved under earth’s gravity and atmospheric conditions, spaceflight poses environmental stresses for which human physiology is not adapted. Hypoxia, accelerations, and radiation are among such stressors, our research involves suborbital flights aiming to develop effective countermeasures in order to assure sustainable human space presence. The physiologic baseline of spaceflight participants is subject to great variability driven by age, gender, fitness, and metabolic reserve. The objective of the present study is to characterize different physiologic variables in a population of STEM practitioners during an aerobatic flight. Cardiovascular and pulmonary responses were determined in Science Astronaut Candidates (SACs) during unusual attitude aerobatic flight indoctrination. Physiologic data recordings from 20 subjects participating in high-G flight training were analyzed. These recordings were registered by wearable sensor-vest that monitored electrocardiographic tracings (ECGs), signs of dysrhythmias or other electric disturbances during all the flight. The same cardiovascular parameters were also collected approximately 10 min pre-flight, during each high-G/unusual attitude maneuver and 10 min after the flights. The ratio (pre-flight/in-flight/post-flight) of the cardiovascular responses was calculated for comparison of inter-individual differences. The resulting tracings depicting the cardiovascular responses of the subjects were compared against the G-loads (Gs) during the aerobatic flights to analyze cardiovascular variability aspects and fluid/pressure shifts due to the high Gs. In-flight ECG revealed cardiac variability patterns associated with rapid Gs onset in terms of reduced heart rate (HR) and some scattered dysrhythmic patterns (15% premature ventricular contractions-type) that were considered as triggered physiological responses to high-G/unusual attitude training and some were considered as instrument artifact. Variation events were observed in subjects during the +Gz and –Gz maneuvers and these may be due to preload and afterload, sudden shift. Our data reveal that aerobatic flight influenced the breathing rate of the subject, due in part by the various levels of energy expenditure due to the increased use of muscle work during these aerobatic maneuvers. Noteworthy was the high heterogeneity in the different physiological responses among a relatively small group of SACs exposed to similar aerobatic flights with similar Gs exposures. The cardiovascular responses clearly demonstrated that SACs were subjected to significant flight stress. Routine ECG monitoring during high-G/unusual attitude flight training is recommended to capture pathology underlying dangerous dysrhythmias in suborbital flight safety. More research is currently being conducted to further facilitate the development of robust medical screening, medical risk assessment approaches, and suborbital flight training in the context of the evolving commercial human suborbital spaceflight industry. A more mature and integrative medical assessment method is required to understand the physiology state and response variability among highly diverse populations of prospective suborbital flight participants.

Keywords: g force, aerobatic maneuvers, suborbital flight, hypoxia, commercial astronauts

Procedia PDF Downloads 108

Authors: Dominique Arrouays, Songchao Chen, Anne C. Richer-De-Forges

Abstract:

Soils are at the crossing of many issues such as food and water security, sustainable energy, climate change mitigation and adaptation, biodiversity protection, human health and well-being. They deliver many ecosystem services that are essential to life on Earth. Therefore, there is a growing demand for soil information on a national and global scale. Unfortunately, many countries do not have detailed soil maps, and, when existing, these maps are generally based on more or less complex and often non-harmonized soil classifications. An estimate of their uncertainty is also often missing. Thus, there are not easy to understand and often not properly used by end-users. Therefore, there is an urgent need to provide end-users with spatially exhaustive grids of essential soil properties, together with an estimate of their uncertainty. One way to achieve this is digital soil mapping (DSM). The concept of DSM relies on the hypothesis that soils and their properties are not randomly distributed, but that they depend on the main soil-forming factors that are climate, organisms, relief, parent material, time (age), and position in space. All these forming factors can be approximated using several exhaustive spatial products such as climatic grids, remote sensing products or vegetation maps, digital elevation models, geological or lithological maps, spatial coordinates of soil information, etc. Thus, DSM generally relies on models calibrated with existing observed soil data (point observations or maps) and so-called “ancillary co-variates” that come from other available spatial products. Then the model is generalized on grids where soil parameters are unknown in order to predict them, and the prediction performances are validated using various methods. With the growing demand for soil information at a national and global scale and the increase of available spatial co-variates national and continental DSM initiatives are continuously increasing. This short review illustrates the main national and continental advances in Europe, the diversity of the approaches and the databases that are used, the validation techniques and the main scientific and other issues. Examples from several countries illustrate the variety of products that were delivered during the last ten years. The scientific production on this topic is continuously increasing and new models and approaches are developed at an incredible speed. Most of the digital soil mapping (DSM) products rely mainly on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs or for existing conventional maps. However, some scientific issues remain to be solved and also political and legal ones related, for instance, to data sharing and to different laws in different countries. Other issues related to communication to end-users and education, especially on the use of uncertainty. Overall, the progress is very important and the willingness of institutes and countries to join their efforts is increasing. Harmonization issues are still remaining, mainly due to differences in classifications or in laboratory standards between countries. However numerous initiatives are ongoing at the EU level and also at the global level. All these progress are scientifically stimulating and also promissing to provide tools to improve and monitor soil quality in countries, EU and at the global level.

Keywords: digital soil mapping, global soil mapping, national and European initiatives, global soil mapping products, mini-review

Procedia PDF Downloads 169

Authors: Rouzbeh Jafari, Joe Nava

Abstract:

This study includes learnings from an engineering practice normally performed on large scale biohydrogen processes. If properly scale-up is done, biohydrogen can be a reliable pathway for biowaste valorization. Most of the studies on biohydrogen process development have used model feedstock to investigate process key performance indicators (KPIs). This study does not intend to compare different technologies with model feedstock. However, it reports economic drivers and technical challenges which help in developing a road map for expanding biohydrogen economy deployment in Canada. BBA is a consulting firm responsible for the design of hydrogen production projects. Through executing these projects, activity has been performed to identify, register and mitigate technical drawbacks of large-scale hydrogen production. Those learnings, in this study, have been applied to the biohydrogen process. Through data collected by a comprehensive literature review, a base case has been considered as a reference, and several case studies have been performed. Critical parameters of the process were identified and through common engineering practice (process design, simulation, cost estimate, and life cycle assessment) impact of these parameters on the commercialization risk matrix and class 5 cost estimations were reported. The process considered in this study is food waste and woody biomass dark fermentation. To propose a reliable road map to develop a sustainable biohydrogen production process impact of critical parameters was studied on the end-to-end process. These parameters were 1) feedstock composition, 2) feedstock pre-treatment, 3) unit operation selection, and 4) multi-product concept. A couple of emerging technologies also were assessed such as photo-fermentation, integrated dark fermentation, and using ultrasound and microwave to break-down feedstock`s complex matrix and increase overall hydrogen yield. To properly report the impact of each parameter KPIs were identified as 1) Hydrogen yield, 2) energy consumption, 3) secondary waste generated, 4) CO2 footprint, 5) Product profile, 6) $/kg-H2 and 5) environmental impact. The feedstock is the main parameter defining the economic viability of biohydrogen production. Through parametric studies, it was found that biohydrogen production favors feedstock with higher carbohydrates. The feedstock composition was varied, by increasing one critical element (such as carbohydrate) and monitoring KPIs evolution. Different cases were studied with diverse feedstock, such as energy crops, wastewater slug, and lignocellulosic waste. The base case process was applied to have reference KPIs values and modifications such as pretreatment and feedstock mix-and-match were implemented to investigate KPIs changes. The complexity of the feedstock is the main bottleneck in the successful commercial deployment of the biohydrogen process as a reliable pathway for waste valorization. Hydrogen yield, reaction kinetics, and performance of key unit operations highly impacted as feedstock composition fluctuates during the lifetime of the process or from one case to another. In this case, concept of multi-product becomes more reliable. In this concept, the process is not designed to produce only one target product such as biohydrogen but will have two or multiple products (biohydrogen and biomethane or biochemicals). This new approach is being investigated by the BBA team and the results will be shared in another scientific contribution.

Keywords: biohydrogen, process scale-up, economic evaluation, commercialization uncertainties, hydrogen economy

Procedia PDF Downloads 84

Authors: Berna Çalışkan

Abstract:

The integrated water management is a colloborative approach to planning that brings together institutions that influence all elements of the water cycle, waterways, watershed characteristics, wetlands, ponds, lakes, floodplain areas, stream channel structure. It encourages collaboration where it will be beneficial and links between water planning and other planning processes that contribute to improving sustainable urban development and liveability. Hydraulic considerations can influence the selection of a highway corridor and the alternate routes within the corridor. widening a roadway, replacing a culvert, or repairing a bridge. Because of this, the type and amount of data needed for planning studies can vary widely depending on such elements as environmental considerations, class of the proposed highway, state of land use development, and individual site conditions. The extraction of drainage networks provide helpful preliminary drainage data from the digital elevation model (DEM). A case study was carried out using the Arc Hydro extension within ArcGIS in the study area. It provides the means for processing and presenting spatially-referenced Stream Model. Study area’s flow routing, stream levels, segmentation, drainage point processing can be obtained using DEM as the 'Input surface raster'. These processes integrate the fields of hydrologic, engineering research, and environmental modeling in a multi-disciplinary program designed to provide decision makers with a science-based understanding, and innovative tools for, the development of interdisciplinary and multi-level approach. This research helps to manage transport project planning and construction phases to analyze the surficial water flow, high-level streams, wetland sites for development of transportation infrastructure planning, implementing, maintenance, monitoring and long-term evaluations to better face the challenges and solutions associated with effective management and enhancement to deal with Low, Medium, High levels of impact. Transport projects are frequently perceived as critical to the ‘success’ of major urban, metropolitan, regional and/or national development because of their potential to affect significant socio-economic and territorial change. In this context, sustaining and development of economic and social activities depend on having sufficient Water Resources Management. The results of our research provides a workflow to build a stream network how can classify suitability map according to stream levels. Transportation projects establish, develop, incorporate and deliver effectively by selecting best location for reducing construction maintenance costs, cost-effective solutions for drainage, landslide, flood control. According to model findings, field study should be done for filling gaps and checking for errors. In future researches, this study can be extended for determining and preventing possible damage of Sensitive Areas and Vulnerable Zones supported with field investigations.

Keywords: water resources management, hydro tool, water protection, transportation

Procedia PDF Downloads 39

Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

Abstract:

Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

Procedia PDF Downloads 58

Authors: Shuo Li, Yannick Coffinier, Chann Lagadec, Fabrizio Cleri, Katsuhiko Nishiguchi, Akira Fujiwara, Soo Hyeon Kim, Nicolas Clément

Abstract:

The need for single cell detection and analysis techniques has increased in the past decades because of the heterogeneity of individual living cells, which increases the complexity of the pathogenesis of malignant tumors. In the search for early cancer detection, high-precision medicine and therapy, the technologies most used today for sensitive detection of target analytes and monitoring the variation of these species are mainly including two types. One is based on the identification of molecular differences at the single-cell level, such as flow cytometry, fluorescence-activated cell sorting, next generation proteomics, lipidomic studies, another is based on capturing or detecting single tumor cells from fresh or fixed primary tumors and metastatic tissues, and rare circulating tumors cells (CTCs) from blood or bone marrow, for example, dielectrophoresis technique, microfluidic based microposts chip, electrochemical (EC) approach. Compared to other methods, EC sensors have the merits of easy operation, high sensitivity, and portability. However, despite various demonstrations of low limits of detection (LOD), including aptamer sensors, arrayed EC sensors for detecting single-cell have not been demonstrated. In this work, a new technique based on 20-nm-thick nanopillars array to support cells and keep them at ideal recognition distance for redox-labeled aptamers grafted on the surface. The key advantages of this technology are not only to suppress the false positive signal arising from the pressure exerted by all (including non-target) cells pushing on the aptamers by downward force but also to stabilize the aptamer at the ideal hairpin configuration thanks to a confinement effect. With the first implementation of this technique, a LOD of 13 cells (with5.4 μL of cell suspension) was estimated. In further, the nanosupported cell technology using redox-labeled aptasensors has been pushed forward and fully integrated into a single-cell electrochemical aptasensor array. To reach this goal, the LOD has been reduced by more than one order of magnitude by suppressing parasitic capacitive electrochemical signals by minimizing the sensor area and localizing the cells. Statistical analysis at the single-cell level is demonstrated for the recognition of cancer cells. The future of this technology is discussed, and the potential for scaling over millions of electrodes, thus pushing further integration at sub-cellular level, is highlighted. Despite several demonstrations of electrochemical devices with LOD of 1 cell/mL, the implementation of single-cell bioelectrochemical sensor arrays has remained elusive due to their challenging implementation at a large scale. Here, the introduced nanopillar array technology combined with redox-labeled aptamers targeting epithelial cell adhesion molecule (EpCAM) is perfectly suited for such implementation. Combining nanopillar arrays with microwells determined for single cell trapping directly on the sensor surface, single target cells are successfully detected and analyzed. This first implementation of a single-cell electrochemical aptasensor array based on Brownian-fluctuating redox species opens new opportunities for large-scale implementation and statistical analysis of early cancer diagnosis and cancer therapy in clinical settings.

Keywords: bioelectrochemistry, aptasensors, single-cell, nanopillars

Procedia PDF Downloads 91

Authors: Neil Singla, Derek Muse, Karen DiDonato, Pamela Palmer

Abstract:

Introduction: Pain is the most common reason people visit emergency rooms. Studies indicate however, that Emergency Department (ED) physicians often do not provide adequate analgesia to their patients as a result of gender and age bias, opiophobia and insufficient knowledge of and formal training in acute pain management. Novel classes of analgesics have recently been introduced, but many patients suffer from acute pain in settings where the availability of intravenous (IV) access may be limited, so there remains a clinical need for rapid-acting, potent analgesics that do not require an invasive route of delivery. A sublingual sufentanil tablet (SST), dispensed using a single-dose applicator, is in development for treatment of moderate-to-severe acute pain in a medically-supervised setting. Objective: The primary objective of this study was to demonstrate the repeat-dose efficacy, safety and tolerability of sufentanil 20 mcg and 30 mcg sublingual tablets compared to placebo for the management of acute pain as determined by the time-weighted sum of pain intensity differences (SPID) to baseline over the 12-hour study period (SPID12). Key secondary efficacy variables included SPID over the first hour (SPID1), Total pain relief over the 12-hour study period (TOTPAR12), time to perceived pain relief (PR) and time to meaningful PR. Safety variables consisted of adverse events (AE), vital signs, oxygen saturation and early termination. Methods: In this Phase 2, double-blind, dose-finding study, an equal number of male and female patients were randomly assigned in a 2:2:1 ratio to SST 20 mcg, SS 30 mcg or placebo, respectively, following bunionectomy. Study drug was dosed as needed, but not more frequently than hourly. Rescue medication was available as needed. The primary endpoint was the Summed Pain Intensity Difference to baseline over 12h (SPIDI2). Safety was assessed by continuous oxygen saturation monitoring and adverse event reporting. Results: 101 patients (51 Male/50 Female) were randomized, 100 received study treatment (intent-to-treat [ITT] population), and 91 completed the study. Reasons for early discontinuation were lack of efficacy (6), adverse events (2) and drug-dosing error (1). Mean age was 42.5 years. For the ITT population, SST 30 mcg was superior to placebo (p=0.003) for the SPID12. SPID12 scores in the active groups were superior for both male (ANOVA overall p-value =0.038) and female (ANOVA overall p-value=0.005) patients. Statistically significant differences in favour of sublingual sufentanil were also observed between the SST 30mcg and placebo group for SPID1(p<0.001), TOTPAR12(p=0.002), time to perceived PR (p=0.023) and time to meaningful PR (p=0.010). Nausea, vomiting and somnolence were more frequent in the sufentanil groups but there were no significant differences between treatment arms for the proportion of patients who prematurely terminated due to AE or inadequate analgesia. Conclusions: Sufentanil tablets dispensed sublingually using a single-dose applicator is in development for treatment of patients with moderate-to-severe acute pain in a medically-supervised setting where immediate IV access is limited. When administered sublingually, sufentanil’s pharmacokinetic profile and non-invasive delivery makes it a useful alternative to IM or IV dosing.

Keywords: acute pain, pain management, sublingual, sufentanil

Procedia PDF Downloads 343

Authors: Javier E. García-Gallo, Nelson J. Fonseca-Ruiz, John F. Duitama-Munoz

Abstract:

Sepsis is a syndrome that occurs with physiological and biochemical abnormalities induced by severe infection and carries a high mortality and morbidity, therefore the severity of its condition must be interpreted quickly. After patient admission in an intensive care unit (ICU), it is necessary to synthesize the large volume of information that is collected from patients in a value that represents the severity of their condition. Traditional severity of illness scores seeks to be applicable to all patient populations, and usually assess in-hospital mortality. However, the use of machine learning techniques and the data of a population that shares a common characteristic could lead to the development of customized mortality prediction scores with better performance. This study presents the development of a score for the one-year mortality prediction of the patients that are admitted to an ICU with a sepsis diagnosis. 5650 ICU admissions extracted from the MIMICIII database were evaluated, divided into two groups: 70% to develop the score and 30% to validate it. Comorbidities, demographics and clinical information of the first 24 hours after the ICU admission were used to develop a mortality prediction score. LASSO (least absolute shrinkage and selection operator) and SGB (Stochastic Gradient Boosting) variable importance methodologies were used to select the set of variables that make up the developed score; each of this variables was dichotomized and a cut-off point that divides the population into two groups with different mean mortalities was found; if the patient is in the group that presents a higher mortality a one is assigned to the particular variable, otherwise a zero is assigned. These binary variables are used in a logistic regression (LR) model, and its coefficients were rounded to the nearest integer. The resulting integers are the point values that make up the score when multiplied with each binary variables and summed. The one-year mortality probability was estimated using the score as the only variable in a LR model. Predictive power of the score, was evaluated using the 1695 admissions of the validation subset obtaining an area under the receiver operating characteristic curve of 0.7528, which outperforms the results obtained with Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS) and Simplified Acute Physiology Score II (SAPSII) scores on the same validation subset. Observed and predicted mortality rates within estimated probabilities deciles were compared graphically and found to be similar, indicating that the risk estimate obtained with the score is close to the observed mortality, it is also observed that the number of events (deaths) is indeed increasing as the outcome go from the decile with the lowest probabilities to the decile with the highest probabilities. Sepsis is a syndrome that carries a high mortality, 43.3% for the patients included in this study; therefore, tools that help clinicians to quickly and accurately predict a worse prognosis are needed. This work demonstrates the importance of customization of mortality prediction scores since the developed score provides better performance than traditional scoring systems.

Keywords: intensive care, logistic regression model, mortality prediction, sepsis, severity of illness, stochastic gradient boosting

Procedia PDF Downloads 199

Authors: G. Hubert, S. Aubry

Abstract:

The earth is constantly bombarded by cosmic rays that can be of either galactic or solar origin. Thus, humans are exposed to high levels of galactic radiation due to altitude aircraft. The typical total ambient dose equivalent for a transatlantic flight is about 50 μSv during quiet solar activity. On the contrary, estimations differ by one order of magnitude for the contribution induced by certain solar particle events. Indeed, during Ground Level Enhancements (GLE) event, the Sun can emit particles of sufficient energy and intensity to raise radiation levels on Earth's surface. Analyses of GLE characteristics occurring since 1942 showed that for the worst of them, the dose level is of the order of 1 mSv and more. The largest of these events was observed on February 1956 for which the ambient dose equivalent rate is in the orders of 10 mSv/hr. The extra dose at aircraft altitudes for a flight during this event might have been about 20 mSv, i.e. comparable with the annual limit for aircrew. The most recent GLE, occurred on September 2017 resulting from an X-class solar flare, and it was measured on the surface of both the Earth and Mars using the Radiation Assessment Detector on the Mars Science Laboratory's Curiosity Rover. Recently, Hubert et al. proposed a GLE model included in a particle transport platform (named ATMORAD) describing the extensive air shower characteristics and allowing to assess the ambient dose equivalent. In this approach, the GCR is based on the Force-Field approximation model. The physical description of the Solar Cosmic Ray (i.e. SCR) considers the primary differential rigidity spectrum and the distribution of primary particles at the top of the atmosphere. ATMORAD allows to determine the spectral fluence rate of secondary particles induced by extensive showers, considering altitude range from ground to 45 km. Ambient dose equivalent can be determined using fluence-to-ambient dose equivalent conversion coefficients. The objective of this paper is to analyze the GCR and SCR impacts on ambient dose equivalent considering a high number statistic of world-flight paths. Flight trajectories are based on the Eurocontrol Demand Data Repository (DDR) and consider realistic flight plan with and without regulations or updated with Radar Data from CFMU (Central Flow Management Unit). The final paper will present exhaustive analyses implying solar impacts on ambient dose equivalent level and will propose detailed analyses considering route and airplane characteristics (departure, arrival, continent, airplane type etc.), and the phasing of the solar event. Preliminary results show an important impact of the flight path, particularly the latitude which drives the cutoff rigidity variations. Moreover, dose values vary drastically during GLE events, on the one hand with the route path (latitude, longitude altitude), on the other hand with the phasing of the solar event. Considering the GLE occurred on 23 February 1956, the average ambient dose equivalent evaluated for a flight Paris - New York is around 1.6 mSv, which is relevant to previous works This point highlights the importance of monitoring these solar events and of developing semi-empirical and particle transport method to obtain a reliable calculation of dose levels.

Keywords: cosmic ray, human dose, solar flare, aviation

Procedia PDF Downloads 195

Authors: Raptis Sotirios

Abstract:

Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

Procedia PDF Downloads 210

Authors: Bency Ann Massinello, Nancy Day, Janet Fellini

Abstract:

Background: The use of an appropriate mode of communication among the multidisciplinary care team members regarding coordination of care is an extremely complicated yet important patient safety initiative. Effective communication among the team members(nursing staff, medical staff, respiratory therapists, rehabilitation therapists, patient-family services team…) become essential to develop a culture of trust and collaboration to deliver the highest quality care to patients are their families. The inpatient post-acute pediatrics, where children and their caregivers come for continuity of care, is no exceptions to the increasing use of text messages as a means to communication among clinicians. One such platform is the Vocera Communications (Vocera Smart Mobile App called Vocera Edge) allows the teams to use the application and share sensitive patient information through an encrypted platform using IOS company provided shared and assigned mobile devices. Objective: This paper discusses the quality initiative of implementing the transition from Vocera Smartbage to Vocera Edge Mobile App, technology advantage, use case expansion, and lessons learned about a secure alternative modality that allows sending and receiving secure text messages in a pediatric post-acute setting using an IOS device. This implementation process included all direct care staff, ancillary teams, and administrative teams on the clinical units. Methods: Our institution launched this transition from voice prompted hands-free Vocera Smartbage to Vocera Edge mobile based app for secure care team texting using a big bang approach during the first PDSA cycle. The pre and post implementation data was gathered using a qualitative survey of about 500 multidisciplinary team members to determine the ease of use of the application and its efficiency in care coordination. The technology was further expanded in its use by implementing clinical alerts and alarms notification using middleware integration with patient monitoring (Masimo) and life safety (Nurse call) systems. Additional use of the smart mobile iPhone use include pushing out apps like Lexicomp and Up to Date to have it readily available for users for evident-based practice in medication and disease management. Results: Successful implementation of the communication system in a shared and assigned model with all of the multidisciplinary teams in our pediatric post-acute setting. In just a 3-monthperiod post implementation, we noticed a 14% increase from 7,993 messages in 6 days in December 2020 to 9,116messages in March 2021. This confirmed that all clinical and non-clinical teams were using this mode of communication for coordinating the care for their patients. System generated data analytics used in addition to the pre and post implementation staff survey for process evaluation. Conclusion: A secure texting option using a mobile device is a safe and efficient mode for care team communication and collaboration using technology in real time. This allows for the settings like post-acute pediatric care areas to be in line with the widespread use of mobile apps and technology in our mainstream healthcare.

Keywords: nursing informatics, mobile secure texting, multidisciplinary communication, pediatrics post acute care

Procedia PDF Downloads 182

Authors: Sarah Nauwelaerts, Koen De Cremer, Alfred Bernard, Meredith Verlooy, Kristel Heremans, Natalia Bustos Sierra, Katrien Tersago, Tim Nawrot, Jordy Vercauteren, Christophe Stroobants, Sigrid C. J. De Keersmaecker, Nancy Roosens

Abstract:

Projected climate changes could lead to exacerbation of respiratory disorders associated with reduced air quality. Air pollution and climate changes influence each other through complex interactions. The poor air quality in urban and rural areas includes high levels of particulate matter (PM), ozone (O3) and nitrogen oxides (NOx), representing a major threat to public health and especially for the most vulnerable population strata, and especially young children. In this study, we aim to develop generic standardized policy supporting tools and methods that allow evaluating in future follow-up larger scale epidemiological studies the risks of the combined short-term effects of O3 and PM on the cardiorespiratory system of children. We will use non-invasive indicators of airway damage/inflammation and of genetic or epigenetic variations by using urine or saliva as alternative to blood samples. Therefore, a multi-phase field study will be organized in order to assess the sensitivity and applicability of these tests in large cohorts of children during episodes of air pollution. A first test phase was planned in March 2018, not yet taking into account ‘critical’ pollution periods. Working with non-invasive samples, choosing the right set-up for the field work and the volunteer selection were parameters to consider, as they significantly influence the feasibility of this type of study. During this test phase, the selection of the volunteers was done in collaboration with medical doctors from the Centre for Student Assistance (CLB), by choosing a class of pre-pubertal children of 9-11 years old in a primary school in Flemish Brabant, Belgium. A questionnaire, collecting information on the health and background of children and an informed consent document were drawn up for the parents as well as a simplified cartoon-version of this document for the children. A detailed study protocol was established, giving clear information on the study objectives, the recruitment, the sample types, the medical examinations to be performed, the strategy to ensure anonymity, and finally on the sample processing. Furthermore, the protocol describes how this field study will be conducted in relation with the prevision and monitoring of air pollutants for the future phases. Potential protein, genetic and epigenetic biomarkers reflecting the respiratory function and the levels of air pollution will be measured in the collected samples using unconventional technologies. The test phase results will be used to address the most important bottlenecks before proceeding to the following phases of the study where the combined effect of O3 and PM during pollution peaks will be examined. This feasibility study will allow identifying possible bottlenecks and providing missing scientific knowledge, necessary for the preparation, implementation and evaluation of federal policies/strategies, based on the most appropriate epidemiological studies on the health effects of air pollution. The research leading to these results has been funded by the Belgian Science Policy Office through contract No.: BR/165/PI/PMOLLUGENIX-V2.

Keywords: air pollution, biomarkers, children, field study, feasibility study, non-invasive

Procedia PDF Downloads 157

Authors: Georgios A. Fragkiadakis, Antonia Psaroudaki, Theodora Mouratidou, Eirini Sfakianaki

Abstract:

Research: Within the project "Actions for the optimal utilization of the potential of carob in the Region of Crete" which is financed-supervised by the Region, with collaboration of Crete University and Hellenic Mediterranean University, a SWOT (strengths, weaknesses, opportunities, threats) survey was carried out, to evaluate the prospects of carob in human nutrition, in Crete. Results and conclusions: 1). Strengths: There exists a local production of carob for human consumption, based on international reports, and local-product reports. The data on products in the market (over 100 brands of carob food), indicates a sufficiency of carob materials offered in Crete. The variety of carob food products retailed in Crete indicates a strong demand-production-consumption trend. There is a stable number (core) of businesses that invest significantly (Creta carob, Cretan mills, etc.). The great majority of the relevant food stores (bakery, confectionary etc.) do offer carob products. The presence of carob products produced in Crete is strong on the internet (over 20 main professionally designed websites). The promotion of the carob food-products is based on their variety and on a few historical elements connected with the Cretan diet. 2). Weaknesses: The international prices for carob seed affect the sector; the seed had an international price of €20 per kg in 2021-22 and fell to €8 in 2022, causing losses to carob traders. The local producers do not sort the carobs they deliver for processing, causing 30-40% losses of the product in the industry. The occasional high price triggers the collection of degraded raw material; large losses may emerge due to the action of insects. There are many carob trees whose fruits are not collected, e.g. in Apokoronas, Chania. The nutritional and commercial value of the wild carob fruits is very low. Carob trees-production is recorded by Greek statistical services as "other cultures" in combination with prickly pear i.e., creating difficulties in retrieving data. The percentage of carob used for human nutrition, in contrast to animal feeding, is not known. The exact imports of carob are not closely monitored. We have no data on the recycling of carob by-products in Crete. 3). Opportunities: The development of a culture of respect for carob trade may improve professional relations in the sector. Monitoring carob market and connecting production with retailing-industry needs may allow better market-stability. Raw material evaluation procedures may be implemented to maintain carob value-chain. The state agricultural services may be further involved in carob-health protection. The education of farmers on carob cultivation/management, can improve the quality of the product. The selection of local productive varieties, may improve the sustainability of the culture. Connecting the consumption of carob with health-food products, may create added value in the sector. The presence and extent of wild carob threes in Crete, represents, potentially, a target for grafting. 4). Threats: The annual fluctuation of carob yield challenges the programming of local food industry activities. Carob is a forest species also - there is danger of wrong classification of crops as forest areas, where land ownership is not clear.

Keywords: human nutrition, carob food, SWOT analysis, crete, greece

Procedia PDF Downloads 71

Authors: Ana Paula Almeida Castaldelli Maciel, Gabriela Medeiros, Amanda de Souza Machado, Maria Clara Pilatti, Ralpho Rinaldo dos Reis, Silvio Cesar Sampaio

Abstract:

Sustainable agricultural systems are crucial to ensuring global food security and the long-term production of nutritious food. Comprehensive soil and water management practices, including nutrient management, balanced fertilizer use, and appropriate waste management, are essential for sustainable agriculture. Swine wastewater (SWW) treatment has become a significant focus due to environmental concerns related to heavy metals, antibiotics, resistant pathogens, and nutrients. In South America, small farms use soil to dispose of animal waste, a practice that is expected to increase with global pork production. The potential of SWW as a nutrient source is promising, contributing to global food security, nutrient cycling, and mineral fertilizer reduction. Short- and long-term studies evaluated the effects of SWW on soil and plant parameters, such as nutrients, heavy metals, organic matter (OM), cation exchange capacity (CEC), and pH. Although promising results have been observed in short- and medium-term applications, long-term applications require more attention due to heavy metal concentrations. Organic soil amendment strategies, due to their economic and ecological benefits, are commonly used to reduce the bioavailability of heavy metals. However, the rate of degradation and initial levels of OM must be monitored to avoid changes in soil pH and release of metals. The study aimed to evaluate the long-term effects of SWW application on soil fertility parameters, focusing on calcium (Ca), magnesium (Mg), and potassium (K), in addition to CEC and OM. Experiments were conducted at the Universidade Estadual do Oeste do Paraná, Brazil, using 24 drainage lysimeters for nine years, with different application rates of SWW and mineral fertilization. Principal Component Analysis (PCA) was then conducted to summarize the composite variables, known as principal components (PC), and limit the dimensionality to be evaluated. The retained PCs were then correlated with the original variables to identify the level of association between each variable and each PC. Data were interpreted using Analysis of Variance - ANOVA for general linear models (GLM). As OM was not measured in the 2007 soybean experiment, it was assessed separately from PCA to avoid loss of information. PCA and ANOVA indicated that crop type, SWW, and mineral fertilization significantly influenced soil nutrient levels. Soybeans presented higher concentrations of Ca, Mg, and CEC. The application of SWW influenced K levels, with higher concentrations observed in SWW from biodigesters and higher doses of swine manure. Variability in nutrient concentrations in SWW due to factors such as animal age and feed composition makes standard recommendations challenging. OM levels increased in SWW-treated soils, improving soil fertility and structure. In conclusion, the application of SWW can increase soil fertility and crop productivity, reducing environmental risks. However, careful management and long-term monitoring are essential to optimize benefits and minimize adverse effects.

Keywords: contamination, water research, biodigester, nutrients

Procedia PDF Downloads 33

Authors: Kaushikk Iyer, Richard M Hall, David Keeling

Abstract:

Hardware-in-the-loop (HIL) simulation is an advanced technique for developing and testing complex real-time control systems. This paper presents the benefits of HIL simulation and how it can be implemented and used effectively to develop, test, and validate advanced control algorithms used in a spinal joint Wear simulator for the Tribological testing of spinal disc prostheses. spinal wear simulator is technologically the most advanced machine currently employed For the in-vitro testing of newly developed spinal Discimplants. However, the existing control techniques, such as a simple position control Does not allow the simulator to test non-sinusoidal waveforms. Thus, there is a need for better and advanced control methods that can be developed and tested Rigorouslybut safely before deploying it into the real simulator. A benchtop HILsetupis was created for experimentation, controller verification, and validation purposes, allowing different control strategies to be tested rapidly in a safe environment. The HIL simulation aspect in this setup attempts to replicate similar spinal motion and loading conditions. The spinal joint wear simulator containsa four-Barlinkpowered by electromechanical actuators. LabVIEW software is used to design a kinematic model of the spinal wear Simulator to Validatehow each link contributes towards the final motion of the implant under test. As a result, the implant articulates with an angular motion specified in the international standards, ISO-18192-1, that define fixed, simplified, and sinusoid motion and load profiles for wear testing of cervical disc implants. Using a PID controller, a velocity-based position control algorithm was developed to interface with the benchtop setup that performs HIL simulation. In addition to PID, a fuzzy logic controller (FLC) was also developed that acts as a supervisory controller. FLC provides intelligence to the PID controller by By automatically tuning the controller for profiles that vary in amplitude, shape, and frequency. This combination of the fuzzy-PID controller is novel to the wear testing application for spinal simulators and demonstrated superior performance against PIDwhen tested for a spectrum of frequency. Kaushikk Iyer is a Ph.D. Student at the University of Leeds and an employee at Key Engineering Solutions, Leeds, United Kingdom, (e-mail: [email protected], phone: +44 740 541 5502). Richard M Hall is with the University of Leeds, the United Kingdom as a professor in the Mechanical Engineering Department (e-mail: [email protected]). David Keeling is the managing director of Key Engineering Solutions, Leeds, United Kingdom (e-mail: [email protected]). Results obtained are successfully validated against the load and motion tolerances specified by the ISO18192-1 standard and fall within limits, that is, ±0.5° at the maxima and minima of the motion and ±2 % of the complete cycle for phasing. The simulation results prove the efficacy of the test setup using HIL simulation to verify and validate the accuracy and robustness of the prospective controller before its deployment into the spinal wear simulator. This method of testing controllers enables a wide range of possibilities to test advanced control algorithms that can potentially test even profiles of patients performing various dailyliving activities.

Keywords: Fuzzy-PID controller, hardware-in-the-loop (HIL), real-time simulation, spinal wear simulator

Procedia PDF Downloads 154

Authors: Hakimeh Masoudigavgani

Abstract:

Since manufacturing firms deplete non-renewable resources and pollute air, soil, and water in greatly unsustainable manner, industrial activities or production of products are considered to be a key contributor to adverse environmental impacts. Hence, management strategies and approaches that involve an effective supply chain decision process in a manufacturing sector could be extremely significant to the application of environmental initiatives. Green manufacturing (GM) is one of these strategies which minimises negative effects on the environment through reducing greenhouse gas emissions, waste, and the consumption of energy and natural resources. This paper aims to explore what greening methods and mechanisms could be applied in the manufacturing supply chain and what are the outcomes of adopting these methods in terms of abating environmental burdens? The study is an interpretive research with an exploratory approach, using thematic analysis by coding text, breaking down and grouping the content of collected literature into various themes and categories. It is found that green supply chain could be attained through execution of some pre-production strategies including green building, eco-design, and green procurement as well as a number of in-production and post-production strategies involving green manufacturing and green logistics. To achieve an effective GM, the pre-production strategies are suggested to be employed. This paper defines GM as (1) the analysis of the ecological impacts generated by practices, products, production processes, and operational functions, and (2) the implementation of greening methods to reduce damaging influences of them on the natural environment. Analysis means assessing, monitoring, and auditing of practices in order to measure and pinpoint their harmful impacts. Moreover, greening methods involved within GM (arranged in order from the least to the most level of environmental compliance and techniques) consist of: •product stewardship (e.g. less use of toxic, non-renewable, and hazardous materials in the manufacture of the product; and stewardship of the environmental problems with regard to the product in all production, use, and end-of-life stages); •process stewardship (e.g. controlling carbon emission, energy and resources usage, transportation method, and disposal; reengineering polluting processes; recycling waste materials generated in production); •lean and clean production practices (e.g. elimination of waste, materials replacement, materials reduction, resource-efficient consumption, energy-efficient usage, emission reduction, managerial assessment, waste re-use); •use of eco-industrial parks (e.g. a shared warehouse, shared logistics management system, energy co-generation plant, effluent treatment). However, the focus of this paper is only on methods related to the in-production phase and needs further research on both pre-production and post-production environmental innovations. The outlined methods in this investigation may possibly be taken into account by policy/decision makers. Additionally, the proposed future research direction and identified gaps can be filled by scholars and researchers. The paper compares and contrasts a variety of viewpoints and enhances the body of knowledge by building a definition for GM through synthesising literature and categorising the strategic concept of greening methods, drivers, barriers, and successful implementing tactics.

Keywords: green manufacturing (GM), product stewardship, process stewardship, clean production, eco-industrial parks (EIPs)

Procedia PDF Downloads 566

Authors: Anita Conti, Riccardo Ossanna, Lindsey A. Quintero, Giamaica Conti, Andrea Sbarbati

Abstract:

Ischemia/reperfusion (IR) injury induces muscle fiber atrophy and skeletal muscle fiber death with subsequently functionality loss. The heterogeneous pool of cells, especially mesenchymal stem cells, contained in the stromal vascular fraction (SVF) of adipose tissue could promote muscle fiber regeneration. To prevent SVF dispersion, it has been proposed the use of injectable biopolymers that work as cells carrier. A significant element of the extracellular matrix is hyaluronic acid (HA), which has been widely used in regenerative medicine as a cell scaffold given its biocompatibility, degradability, and the possibility of chemical functionalization. Connective tissue micro-fragments enriched with SVF obtained from mechanical disaggregation of adipose tissue were evaluated for IR muscle injury regeneration using low molecular weight HA as a scaffold. IR induction. Hindlimb ischemia was induced in 9 athymic nude mice through the clamping of the right quadriceps using a plastic band. Reperfusion was induced by cutting the plastic band after 3 hours of ischemic period. Contralateral (left) muscular tissue was used as healthy control. Treatment. Twenty-four hours after the IR induction, animals (n=3) were intramuscularly injected with 100 µl of SVF mixed with HA (SVF-HA). Animals treated with 100 µl of HA (n=3) and 100 µl saline solution (n=3) were used as control. Treatment monitoring. All animals were in vivo monitored by magnetic resonance imaging (MRI) at 5, 7, 14 and 18 days post-injury (dpi). High-resolution morphological T2 weighed, quantitative T2 map and Dynamic Contrast-Enhanced (DCE) images were acquired in order to assess the regenerative potential of SVF-HA treatment. Ex vivo evaluation. After 18 days from IR induction, animals were sacrificed, and the muscles were harvested for histological examination. At 5 dpi T2 high-resolution MR images clearly reveal the presence of an extensive edematous area due to IR damage for all groups identifiable as an increase of signal intensity (SI) of muscular and surrounding tissue. At 7 dpi, animals of the SVF-HA group showed a reduction of SI, and the T2relaxation time of muscle tissue of the HA-SVF group was 29±0.5ms, comparable with the T2relaxation time of contralateral muscular tissue (30±0.7ms). These suggest a reduction of edematous overflow and swelling. The T2relaxation time at 7dpi of HA and saline groups were 84±2ms and 90±5ms, respectively, which remained elevated during the rest of the study. The evaluation of vascular regeneration showed similar results. Indeed, DCE-MRI analysis revealed a complete recovery of muscular tissue perfusion after 14 dpi for the SVF-HA group, while for the saline and HA group, controls remained in a damaged state. Finally, the histological examination of SVF-HA treated animals exhibited well-defined and organized fibers morphology with a lateralized nucleus, similar to contralateral healthy muscular tissue. On the contrary, HA and saline-treated animals presented inflammatory infiltrates, with HA slightly improving the diameter of the fibers and less degenerated tissue. Our findings show that connective tissue micro-fragments enriched with SVF induce higher muscle homeostasis and perfusion restoration in contrast to control groups.

Keywords: ischemia/reperfusion injury, regenerative medicine, resonance imaging, stromal vascular fraction

Procedia PDF Downloads 107

Authors: Ding Liangxiao

Abstract:

The technological advancements in Micro-Electro-Mechanical Systems (MEMS) have significantly contributed to the development of new, flexible capacitive pressure sensors,which are pivotal in transforming wearable and medical device technologies. This study employs the sophisticated simulation tools available in COMSOL Multiphysics® to develop and analyze a MEMS-based sensor with a tri-layered design. This sensor comprises top and bottom electrodes made from gold (Au), noted for their excellent conductivity, a middle dielectric layer made from a composite of Silver Nanowires (AgNWs) embedded in Thermoplastic Polyurethane (TPU), and a flexible, durable substrate of Polydimethylsiloxane (PDMS). This research was directed towards understanding how changes in the physical characteristics of the AgNWs/TPU dielectric layer—specifically, its thickness and surface area—impact the sensor's operational efficacy. We assessed several key electrical properties: capacitance, electric potential, and membrane displacement under varied pressure conditions. These investigations are crucial for enhancing the sensor's sensitivity and ensuring its adaptability across diverse applications, including health monitoring systems and dynamic user interface technologies. To ensure the reliability of our simulations, we applied the Effective Medium Theory to calculate the dielectric constant of the AgNWs/TPU composite accurately. This approach is essential for predicting how the composite material will perform under different environmental and operational stresses, thus facilitating the optimization of the sensor design for enhanced performance and longevity. Moreover, we explored the potential benefits of innovative three-dimensional structures for the dielectric layer compared to traditional flat designs. Our hypothesis was that 3D configurations might improve the stress distribution and optimize the electrical field interactions within the sensor, thereby boosting its sensitivity and accuracy. Our simulation protocol includes comprehensive performance testing under simulated environmental conditions, such as temperature fluctuations and mechanical pressures, which mirror the actual operational conditions. These tests are crucial for assessing the sensor's robustness and its ability to function reliably over extended periods, ensuring high reliability and accuracy in complex real-world environments. In our current research, although a full dynamic simulation analysis of the three-dimensional structures has not yet been conducted, preliminary explorations through three-dimensional modeling have indicated the potential for mechanical and electrical performance improvements over traditional planar designs. These initial observations emphasize the potential advantages and importance of incorporating advanced three-dimensional modeling techniques in the development of Micro-Electro-Mechanical Systems (MEMS)sensors, offering new directions for the design and functional optimization of future sensors. Overall, this study not only highlights the powerful capabilities of COMSOL Multiphysics® for modeling sophisticated electronic devices but also underscores the potential of innovative MEMS technology in advancing the development of more effective, reliable, and adaptable sensor solutions for a broad spectrum of technological applications.

Keywords: MEMS, flexible sensors, COMSOL Multiphysics, AgNWs/TPU, PDMS, 3D modeling, sensor durability

Procedia PDF Downloads 20

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

Procedia PDF Downloads 43