Search results for: technologies of the self

Authors: Hyomin Lee, Jinhyun Lee, Jinyeon Hwang, Younghoon Choi, Byeongseo Son

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Global warming is a world-wide issue. Various carbon capture and storage (CCS) technologies for reducing CO2 concentration in the atmosphere have been increasingly studied. Mineral carbonation is one of promising method for CO2 sequestration. Waste cement generating from aggregate recycling processes of waste concrete is potentially a good raw material containing reactive components for mineral carbonation. The major goal of our long-term project is to developed effective methods for CO2 sequestration using waste cement. In the present study, the carbonation characteristics of hydrated cement were examined by conducting two different direct aqueous carbonation experiments. We also evaluate the influence of NaCl and MgCl2 as additives to increase mineral carbonation efficiency of hydrated cement. Cement paste was made with W:C= 6:4 and stored for 28 days in water bath. The prepared cement paste was pulverized to the size less than 0.15 mm. 15 g of pulverized cement paste and 200 ml of solutions containing additives were reacted in ambient temperature and pressure conditions. 1M NaCl and 0.25 M MgCl2 was selected for additives after leaching test. Two different sources of CO2 was applied for direct aqueous carbonation experiment: 0.64 M NaHCO3 was used for CO2 donor in method 1 and pure CO2 gas (99.9%) was bubbling into reacting solution at the flow rate of 20 ml/min in method 2. The pH and Ca ion concentration were continuously measured with pH/ISE Multiparameter to observe carbonation behaviors. Material characterization of reacted solids was performed by TGA, XRD, SEM/EDS analyses. The carbonation characteristics of hydrated cement were significantly different with additives. Calcite was a dominant calcium carbonate mineral after the two carbonation experiments with no additive and NaCl additive. The significant amount of aragonite and vaterite as well as very fine calcite of poorer crystallinity was formed with MgCl2 additive. CSH (calcium silicate hydrate) in hydrated cement were changed to MSH (magnesium silicate hydrate). This transformation contributed to the high carbonation efficiency. Carbonation experiment with method 1 revealed that that the carbonation of hydrated cement took relatively long time in MgCl2 solution compared to that in NaCl solution and the contents of aragonite and vaterite were increased as increasing reaction time. In order to maximize carbonation efficiency in direct aqueous carbonation with CO2 gas injection (method 2), the control of solution pH was important. The solution pH was decreased with injection of CO2 gas. Therefore, the carbonation efficiency in direct aqueous carbonation was closely related to the stability of calcium carbonate minerals with pH changes. With no additive and NaCl additive, the maximum carbonation was achieved when the solution pH was greater than 11. Calcium carbonate form by mineral carbonation seemed to be re-dissolved as pH decreased below 11 with continuous CO2 gas injection. The type of calcium carbonate mineral formed during carbonation in MgCl2 solution was closely related to the variation of solution pH caused by CO2 gas injection. The amount of aragonite significantly increased with decreasing solution pH, whereas the amount of calcite decreased.

Keywords: CO2 sequestration, Mineral carbonation, Cement and concrete, MgCl2 and NaCl

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Authors: Gianluca Castelnuovo, Giada Pietrabissa, Gian Mauro Manzoni, Margherita Novelli, Emanuele Maria Giusti, Roberto Cattivelli, Enrico Molinari

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Diabesity could be defined as a new global epidemic of obesity and being overweight with many complications and chronic conditions. Such conditions include not only type 2 diabetes, but also cardiovascular diseases, hypertension, dyslipidemia, hypercholesterolemia, cancer, and various psychosocial and psychopathological disorders. The financial direct and indirect burden (considering also the clinical resources involved and the loss of productivity) is a real challenge in many Western health-care systems. Recently the Lancet journal defined diabetes as a 21st-century challenge. In order to promote patient compliance in diabesity treatment reducing costs, evidence-based interventions to improve weight-loss, maintain a healthy weight, and reduce related comorbidities combine different treatment approaches: dietetic, nutritional, physical, behavioral, psychological, and, in some situations, pharmacological and surgical. Moreover, new technologies can provide useful solutions in this multidisciplinary approach, above all in maintaining long-term compliance and adherence in order to ensure clinical efficacy. Psychological therapies with diet and exercise plans could better help patients in achieving weight loss outcomes, both inside hospitals and clinical centers and during out-patient follow-up sessions. In the management of chronic diseases clinical psychology play a key role due to the need of working on psychological conditions of patients, their families and their caregivers. mHealth approach could overcome limitations linked with the traditional, restricted and highly expensive in-patient treatment of many chronic pathologies: one of the best up-to-date application is the management of obesity with type 2 diabetes, where mHealth solutions can provide remote opportunities for enhancing weight reduction and reducing complications from clinical, organizational and economic perspectives. A stepped care mHealth-based approach is an interesting perspective in chronic care management of obesity with type 2 diabetes. One promising future direction could be treating obesity, considered as a chronic multifactorial disease, using a stepped-care approach: -mhealth or traditional based lifestyle psychoeducational and nutritional approach. -health professionals-driven multidisciplinary protocols tailored for each patient. -inpatient approach with the inclusion of drug therapies and other multidisciplinary treatments. -bariatric surgery with psychological and medical follow-up In the chronic care management of globesity mhealth solutions cannot substitute traditional approaches, but they can supplement some steps in clinical psychology and medicine both for obesity prevention and for weight loss management.

Keywords: clinical health psychology, mhealth, obesity, type 2 diabetes, stepped care, chronic care management

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Authors: Hossein Hassani, Ali Rezaei

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Heavy metals are naturally occurring elements that have a high atomic weight and a density at least five times greater than that of water. Their multiple industrial, domestic, agricultural, medical, and technological applications have led to their wide distribution in the environment, raising concerns over their potential effects on human health and the environment. Environmental protection against various pollutants, such as heavy metals formed by industries, mines and modern technologies, is a concern for researchers and industry. In order to assess the contamination of soils the distribution and environmental behavior have been investigated. Jajarm bauxite mine, the most important deposits have been discovered in Iran, which is about 22 million tons of reserve, and is the main mineral of the Diaspora. With a view to estimate the heavy metals ratio of the Jajarm bauxite mine area and to evaluate the pollution level, 50 samples have been collected and have been analyzed for the heavy metals of As, Cd, Cu, Hg, Ni and Pb with the help of Inductively Coupled Plasma-Mass Spectrometer (ICP- MS). In this study, we have dealt with determining evaluation criteria including contamination factor (CF), average concentration (AV), enrichment factor (EF) and geoaccumulation index (GI) to assess the risk of pollution from heavy metals(As, Cd, Cu, Hg, Ni and Pb) in Jajarm bauxite mine. In the samples of the studied, the average of recorded concentration of elements for Arsenic, Cadmium, Copper, Mercury, Nickel and Lead are 18, 0.11, 12, 0.07, 58 and 51 (mg/kg) respectively. The comparison of the heavy metals concentration average and the toxic potential in the samples has shown that an average with respect to the world average of the uncontaminated soil amounts. The average of Pb and As elements shows a higher quantity with respect to the world average quantity. The pollution factor for the study elements has been calculated on the basis of the soil background concentration and has been categorized on the basis of the uncontaminated world soil average with respect to the Hakanson classification. The calculation of the corrected pollutant degree shows the degree of the bulk intermediate pollutant (1.55-2.0) for the average soil sampling of the study area which is on the basis of the background quantity and the world average quantity of the uncontaminated soils. The provided conclusion from calculation of the concentrated factor, for some of the samples show that the average of the lead and arsenic elements stations are more than the background values and the unnatural metal concentration are covered under the study area, That's because the process of mining and mineral extraction. Given conclusion from the calculation of Geoaccumulation index of the soil sampling can explain that the copper, nickel, cadmium, arsenic, lead and mercury elements are Uncontamination. In general, the results indicate that the Jajarm bauxite mine of heavy metal pollution is uncontaminated area and extract the mineral from the mine, not create environmental hazards in the region.

Keywords: enrichment factor, geoaccumulation index, heavy metals, Jajarm bauxite mine, pollution

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Authors: Amor Jnifene, Cedric Cocaud

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One of the most constraining problems facing the development of autonomous vehicles is the limitations of current technologies. Guidance and navigation controllers need to be faster and more robust. Communication data links need to be more reliable and secure. For an Unmanned Aerial Vehicles (UAV) to be useful, and fully autonomous, one important feature that needs to be an integral part of the navigation system is autonomous trajectory planning. The work discussed in this paper presents a method for on-line trajectory planning for UAV’s. This method takes into account various constraints of different types including specific vectors of approach close to target points, multiple objectives, and other constraints related to speed, altitude, and obstacle avoidance. The trajectory produced by the proposed method ensures a smooth transition between different segments, satisfies the minimum curvature imposed by the dynamics of the UAV, and finds the optimum velocity based on available atmospheric conditions. Given a set of objective points and waypoints a skeleton of the trajectory is constructed first by linking all waypoints with straight segments based on the order in which they are encountered in the path. Secondly, vectors of approach (VoA) are assigned to objective waypoints and their preceding transitional waypoint if any. Thirdly, the straight segments are replaced by 3D curvilinear trajectories taking into account the aircraft dynamics. In summary, this work presents a method for on-line 3D trajectory generation (TG) of Unmanned Aerial Vehicles (UAVs). The method takes as inputs a series of waypoints and an optional vector of approach for each of the waypoints. Using a dynamic model based on the performance equations of fixed wing aircrafts, the TG computes a set of 3D parametric curves establishing a course between every pair of waypoints, and assembling these sets of curves to construct a complete trajectory. The algorithm ensures geometric continuity at each connection point between two sets of curves. The geometry of the trajectory is optimized according to the dynamic characteristics of the aircraft such that the result translates into a series of dynamically feasible maneuvers. In summary, this work presents a method for on-line 3D trajectory generation (TG) of Unmanned Aerial Vehicles (UAVs). The method takes as inputs a series of waypoints and an optional vector of approach for each of the waypoints. Using a dynamic model based on the performance equations of fixed wing aircraft, the TG computes a set of 3D parametric curves establishing a course between every pair of waypoints, and assembling these sets of curves to construct a complete trajectory. The algorithm ensures geometric continuity at each connection point between two sets of curves. The geometry of the trajectory is optimized according to the dynamic characteristics of the aircraft such that the result translates into a series of dynamically feasible maneuvers.

Keywords: trajectory planning, unmanned autonomous air vehicle, vector of approach, waypoints

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Authors: Ismene Ithai Bras-Ruiz

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Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

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Authors: Regina Rekuviene, Vykintas Samaitis, Liudas Mažeika, Audrius Jankauskas, Virginija Jankauskaitė, Laura Gegeckienė, Abdolali Sadaghiani, Shaghayegh Saeidiharzand

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Icing brings significant damage to aviation and renewable energy installations. Air-conditioning, refrigeration, wind turbine blades, airplane and helicopter blades often suffer from icing phenomena, which cause severe energy losses and impair aerodynamic performance. The icing process is a complex phenomenon with many different causes and types. Icing mechanisms, distributions, and patterns are still relevant to research topics. The adhesion strength between ice and surfaces differs in different icing environments. This makes the task of anti-icing very challenging. The techniques for various icing environments must satisfy different demands and requirements (e.g., efficient, lightweight, low power consumption, low maintenance and manufacturing costs, reliable operation). It is noticeable that most methods are oriented toward a particular sector and adapting them to or suggesting them for other areas is quite problematic. These methods often use various technologies and have different specifications, sometimes with no clear indication of their efficiency. There are two major groups of anti-icing methods: passive and active. Active techniques have high efficiency but, at the same time, quite high energy consumption and require intervention in the structure’s design. It’s noticeable that vast majority of these methods require specific knowledge and personnel skills. The main effect of passive methods (ice-phobic, superhydrophobic surfaces) is to delay ice formation and growth or reduce the adhesion strength between the ice and the surface. These methods are time-consuming and depend on forecasting. They can be applied on small surfaces only for specific targets, and most are non-biodegradable (except for anti-freezing proteins). There is some quite promising information on ultrasonic ice mitigation methods that employ UGW (Ultrasonic Guided Wave). These methods are have the characteristics of low energy consumption, low cost, lightweight, and easy replacement and maintenance. However, fundamental knowledge of ultrasonic de-icing methodology is still limited. The objective of this work was to identify the ice formation processes and its progress by employing ultrasonic guided wave technique. Throughout this research, the universal set-up for acoustic measurement of ice formation in a real condition (temperature range from +240 C to -230 C) was developed. Ultrasonic measurements were performed by using high frequency 5 MHz transducers in a pitch-catch configuration. The selection of wave modes suitable for detection of ice formation phenomenon on copper metal surface was performed. Interaction between the selected wave modes and ice formation processes was investigated. It was found that selected wave modes are sensitive to temperature changes. It was demonstrated that proposed ultrasonic technique could be successfully used for the detection of ice layer formation on a metal surface.

Keywords: ice formation processes, ultrasonic GW, detection of ice formation, ultrasonic testing

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Authors: Giovanna Bimonte, Luigi Senatore, Francesco Saverio Tortoriello, Ilaria Veronesi

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The education process considers capabilities not only to be seen as a means to a certain end but rather as an effective purpose. Sen's capability approach challenges human capital theory, which sees education as an ordinary investment undertaken by individuals. A complex reality requires complex thinking capable of interpreting the dynamics of society's changes to be able to make decisions that can be rational for private, ethical and social contexts. Education is not something removed from the cultural and social context; it exists and is structured within it. In Italy, the "Mathematical High School Project" is a didactic research project is based on additional laboratory courses in extracurricular hours where mathematics intends to bring itself in a dialectical relationship with other disciplines as a cultural bridge between the two cultures, the humanistic and the scientific ones, with interdisciplinary educational modules on themes of strong impact in younger life. This interdisciplinary mathematics presents topics related to the most advanced technologies and contemporary socio-economic frameworks to demonstrate how mathematics is not only a key to reading but also a key to resolving complex problems. The recent developments in mathematics provide the potential for profound and highly beneficial changes in mathematics education at all levels, such as in socio-economic decisions. The research project is built to investigate whether repeated interactions can successfully promote cooperation among students as rational choice and if the skill, the context and the school background can influence the strategies choice and the rationality. A Laboratory on Game Theory as mathematical theory was conducted in the 4th year of the Mathematical High Schools and in an ordinary scientific high school of the Scientific degree program. Students played two simultaneous games of repeated Prisoner's Dilemma with an indefinite horizon, with two different competitors in each round; even though the competitors in each round will remain the same for the duration of the game. The results highlight that most of the students in the two classes used the two games with an immunization strategy against the risk of losing: in one of the games, they started by playing Cooperate, and in the other by the strategy of Compete. In the literature, theoretical models and experiments show that in the case of repeated interactions with the same adversary, the optimal cooperation strategy can be achieved by tit-for-tat mechanisms. In higher education, individual capacities cannot be examined independently, as conceptual framework presupposes a social construction of individuals interacting and competing, making individual and collective choices. The paper will outline all the results of the experimentation and the future development of the research.

Keywords: game theory, interdisciplinarity, mathematics education, mathematical high school

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Authors: Dalal Albudaiwi, Mike Allen, Talal Alhaji, Shahnaz Khadimehzadah

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As a pandemic, COVID-19 has impacted the whole world since November 2019. In other words, every organization, industry, and institution has been negatively affected by the Coronavirus. The uncertainty of how long the pandemic will last caused chaos at all levels. As with any other institution, public libraries were affected and transmitted into online services and resources. As internationally, have been witnessed that some public libraries were well-prepared for such disasters as the pandemic, and therefore, collections, users, services, technologies, staff, and budgets were all influenced. Public libraries’ policies did not mention any plan regarding such a pandemic. Instead, there are several rules in the guidelines about disasters in general, such as natural disasters. In this pandemic situation, libraries have been involved in different uneasy circumstances. However, it has always been apparent to public libraries the role they play in serving their communities in excellent and critical times. It dwells into the traditional role public libraries play in providing information services and sources to satisfy their information-based community needs. Remarkably increasing people’s awareness of the importance of informational enrichment and enhancing society’s skills in dealing with information and information sources. Under critical circumstances, libraries play a different role. It goes beyond the traditional part of information providers to the untraditional role of being a social institution that serves the community with whatever capabilities they have. This study takes two significant directions. The first focuses on investigating how libraries have responded to COVID-19 and how they manage disasters within their organization. The second direction focuses on how libraries help their communities to act during disasters and how to recover from the consequences. The current study examines how libraries prepare for disasters and the role of public libraries during disasters. We will also propose “measures” to be a model that libraries can use to evaluate the effectiveness of their response to disasters. We intend to focus on how libraries responded to this new disaster. Therefore, this study aims to develop a comprehensive policy that includes responding to a crisis such as Covid-19. An analytical lens inside the libraries as an organization and outside the organization walls will be documented based on analyzing disaster-related literature published in the LIS publication. The study employs content analysis (CA) methodology. CA is widely used in the library and information science. The critical contribution of this work is to propose solutions it provides to libraries and planers to prepare crisis management plans/ policies, specifically to face a new global disaster such as the COVID-19 pandemic. Moreover, the study will help library directors to evaluate their strategies and to improve them properly. The significance of this study lies in guiding libraries’ directors to enhance the goals of the libraries to guarantee crucial issues such as: saving time, avoiding loss, saving budget, acting quickly during a crisis, maintaining libraries’ role during pandemics, finding out the best response to disasters, and creating plan/policy as a sample for all libraries.

Keywords: Covid-19, policy, preparedness, public libraries

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Authors: Irene Capecchi, Tommaso Borghini, Iacopo Bernetti

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One of the most common pieces of equipment still used today for pruning, felling, and processing trees is the chainsaw in forestry. However, chainsaw use highlights dangers and one of the highest rates of accidents in both professional and non-professional work. Felling is proportionally the most dangerous phase, both in severity and frequency, because of the risk of being hit by the plant the operator wants to cut down. To avoid this, a correct sequence of chainsaw cuts must be taught concerning the different conditions of the tree. Virtual reality (VR) makes it possible to virtually simulate chainsaw use without danger of injury. The limitations of the existing applications are as follow. The existing platforms are not symmetrical collaborative because the trainee is only in virtual reality, and the trainer can only see the virtual environment on a laptop or PC, and this results in an inefficient teacher-learner relationship. Therefore, most applications only involve the use of a virtual chainsaw, and the trainee thus cannot feel the real weight and inertia of a real chainsaw. Finally, existing applications simulate only a few cases of tree felling. The objectives of this research were to implement and test a symmetrical collaborative training application based on VR and mixed reality (MR) with the overlap between real and virtual chainsaws in MR. The research and training platform was developed for the Meta quest 2 head-mounted display. The research and training platform application is based on the Unity 3D engine, and Present Platform Interaction SDK (PPI-SDK) developed by Meta. PPI-SDK avoids the use of controllers and enables hand tracking and MR. With the combination of these two technologies, it was possible to overlay a virtual chainsaw with a real chainsaw in MR and synchronize their movements in VR. This ensures that the user feels the weight of the actual chainsaw, tightens the muscles, and performs the appropriate movements during the test allowing the user to learn the correct body posture. The chainsaw works only if the right sequence of cuts is made to felling the tree. Contact detection is done by Unity's physics system, which allows the interaction of objects that simulate real-world behavior. Each cut of the chainsaw is defined by a so-called collider, and the felling of the tree can only occur if the colliders are activated in the right order simulating a safe technique felling. In this way, the user can learn how to use the chainsaw safely. The system is also multiplayer, so the student and the instructor can experience VR together in a symmetrical and collaborative way. The platform simulates the following tree-felling situations with safe techniques: cutting the tree tilted forward, cutting the medium-sized tree tilted backward, cutting the large tree tilted backward, sectioning the trunk on the ground, and cutting branches. The application is being evaluated on a sample of university students through a special questionnaire. The results are expected to test both the increase in learning compared to a theoretical lecture and the immersive and telepresence of the platform.

Keywords: chainsaw, collaborative symmetric virtual reality, mixed reality, operator training

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Authors: Navah Z. Ratzon, Rachel Shichrur

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Background: Many studies have assessed and identified the risk factors of safe driving, but there is relatively little research-based evidence concerning the ability to improve the driving skills of drivers in general and in particular of bus drivers, who are defined as a population at risk. Accidents involving bus drivers can endanger dozens of passengers and cause high direct and indirect damages. Objective: To examine the effectiveness of a metacognitive-functional intervention program for the reduction of risk factors among professional drivers relative to a control group. Methods: The study examined 77 bus drivers working for a large public company in the center of the country, aged 27-69. Twenty-one drivers continued to the intervention stage; four of them dropped out before the end of the intervention. The intervention program we developed was based on previous driving models and the guiding occupational therapy practice framework model in Israel, while adjusting the model to the professional driving in public transportation and its particular risk factors. Treatment focused on raising awareness to safe driving risk factors identified at prescreening (ergonomic, perceptual-cognitive and on-road driving data), with reference to the difficulties that the driver raises and providing coping strategies. The intervention has been customized for each driver and included three sessions of two hours. The effectiveness of the intervention was tested using objective measures: In-Vehicle Data Recorders (IVDR) for monitoring natural driving data, traffic accident data before and after the intervention, and subjective measures (occupational performance questionnaire for bus drivers). Results: Statistical analysis found a significant difference between the degree of change in the rate of IVDR perilous events (t(17)=2.14, p=0.046), before and after the intervention. There was significant difference in the number of accidents per year before and after the intervention in the intervention group (t(17)=2.11, p=0.05), but no significant change in the control group. Subjective ratings of the level of performance and of satisfaction with performance improved in all areas tested following the intervention. The change in the ‘human factors/person’ field, was significant (performance : t=- 2.30, p=0.04; satisfaction with performance : t=-3.18, p=0.009). The change in the ‘driving occupation/tasks’ field, was not significant but showed a tendency toward significance (t=-1.94, p=0.07,). No significant differences were found in driving environment-related variables. Conclusions: The metacognitive-functional intervention significantly improved the objective and subjective measures of safety of bus drivers’ driving. These novel results highlight the potential contribution of occupational therapists, using metacognitive functional treatment, to preventing car accidents among the healthy drivers population and improving the well-being of these drivers. This study also enables familiarity with advanced technologies of IVDR systems and enriches the knowledge of occupational therapists in regards to using a wide variety of driving assessment tools and making the best practice decisions.

Keywords: bus drivers, IVDR, human risk factors, metacognitive-functional intervention

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Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

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Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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Authors: Leonardo Coloma

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The world changes constantly, every second its properties vary due either natural factors or human intervention. As the most intelligent creatures on the planet, human beings have transformed the environment and paradoxically –have allowed ‘mother nature’ to lose species, accelerate the processes of climate change, the deterioration of the ozone layer, among others. The rapid population growth, the procurement, administration and distribution of resources, waste management, and technological advances are some of the factors that boost urban sprawl whose gray stain extends over the territory, facing challenges such as pollution, overpopulation and scarcity of resources. In Ecuador, these problems are added to the social, cultural, economic and political anomalies that have historically affected it. This fact can represent a greater delay when trying to solve global problems, without having paid attention to local inconveniences –smaller ones, but ones that could be the key to project smart solutions on bigger ones. This research aims to highlight the main characteristics of the development models adopted by two Amazonian cities, and analyze the impact of such urban growth on society; to finally define the parameters that would allow the development of an intelligent city in Ecuador, prepared for the challenges of the XXI Century. Contrasts in the climate, temperature, and landscape of Ecuadorian cities are fused with the cultural diversity of its people, generating a multiplicity of nuances of an indecipherable wealth. However, we strive to apply development models that do not recognize that wealth, not understanding them and ignoring that their proposals will vary according to where they are applied. Urban plans seem to take a bit of each of the new theories and proposals of development, which, in the encounter with the informal growth of cities, with those excluded and ‘isolated’ societies, generate absurd morphologies - where the uncertain becomes tangible. The desire to project smart cities is ever growing, but it is important to consider that this concept does not only have to do with the use of information and communication technologies. Its success is achieved when advances in science and technology allow the establishment of a better relationship between people and their context (natural and built). As a research methodology, urban analysis through mappings, diagrams and geographical studies, as well as the identification of sensorial elements when living the city, will make evident the shortcomings of the urban models adopted by certain populations of the Ecuadorian Amazon. Following the vision of previous investigations started since 2014 as part of ‘Centro de Acciones Urbanas,’ the results of this study will encourage the dialogue between the city (as a physical fact) and those who ‘make the city’ (people as its main actors). This research will allow the development of workshops and meetings with different professionals, organizations and individuals in general.

Keywords: Latin American cities, smart cities, urban development, urban morphology, urban sprawl

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Authors: A. V. Samokhin, A. A. Fadeev, M. A. Sinaiskii, N. V. Alekseev, A. V. Kolesnikov

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Composite materials where metal matrix is reinforced by ceramic or metal particles are of great interest for use in the manufacturing of electrical contacts. Significant improvement of the composite physical and mechanical properties as well as increase of the performance parameters of composite-based products can be achieved if the nanoscale structure in the composite materials is obtained by using nanosized powders as starting components. The results of nanosized composite powders synthesis (Ag-SnO2, W-Cu and Cu-C) in the DC thermal plasma flows are presented in this paper. The investigations included the following processes: - Recondensation of micron powder mixture Ag + SnO2 in a nitrogen plasma; - The reduction of the oxide powders mixture (WO3 + CuO) in a hydrogen-nitrogen plasma; - Decomposition of the copper formate and copper acetate powders in nitrogen plasma. The calculations of equilibrium compositions of multicomponent systems Ag-Sn-O-N, W-Cu-O-H-N and Cu-O-C-H-N in the temperature range of 400-5000 K were carried to estimate basic process characteristics. Experimental studies of the processes were performed using a plasma reactor with a confined jet flow. The plasma jet net power was in the range of 2 - 13 kW, and the feedstock flow rate was up to 0.35 kg/h. The obtained powders were characterized by TEM, HR-TEM, SEM, EDS, ED-XRF, XRD, BET and QEA methods. Nanocomposite Ag-SnO2 (12 wt. %). Processing of the initial powder mixture (Ag-SnO2) in nitrogen thermal plasma stream allowed to produce nanopowders with a specific surface area up to 24 m2/g, consisting predominantly of particles with size less than 100 nm. According to XRD results, tin was present in the obtained products as SnO2 phase, and also as intermetallic phases AgxSn. Nanocomposite W-Cu (20 wt .%). Reduction of (WO3+CuO) mixture in the hydrogen-nitrogen plasma provides W-Cu nanopowder with particle sizes in the range of 10-150 nm. The particles have mainly spherical shape and structure tungsten core - copper shell. The thickness of the shell is about several nanometers, the shell is composed of copper and its oxides (Cu2O, CuO). The nanopowders had 1.5 wt. % oxygen impurity. Heat treatment in a hydrogen atmosphere allows to reduce the oxygen content to less than 0.1 wt. %. Nanocomposite Cu-C. Copper nanopowders were found as products of the starting copper compounds decomposition. The nanopowders primarily had a spherical shape with a particle size of less than 100 nm. The main phase was copper, with small amount of Cu2O and CuO oxides. Copper formate decomposition products had a specific surface area 2.5-7 m2/g and contained 0.15 - 4 wt. % carbon; and copper acetate decomposition products had the specific surface area 5-35 m2/g, and carbon content of 0.3 - 5 wt. %. Compacting of nanocomposites (sintering in hydrogen for Ag-SnO2 and electric spark sintering (SPS) for W-Cu) showed that the samples having a relative density of 97-98 % can be obtained with a submicron structure. The studies indicate the possibility of using high-intensity plasma processes to create new technologies to produce nanocomposite materials for electric contacts.

Keywords: electrical contact, material, nanocomposite, plasma, synthesis

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Authors: Itzel Amaro-Estrada, Eduardo Vergara-Rivera, Virginia Juarez-Flores, Mayra Cobaxin-Cardenas, Rosa Estela Quiroz, Jesus F. Preciado, Sergio Rodriguez-Camarillo

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Bovine anaplasmosis is an infectious, tick-borne disease caused mainly by Anaplasma marginale; typical signs include anemia, fever, abortion, weight loss, decreased milk production, jaundice, and potentially death. Sick bovine can recover when antibiotics are administered; however, it usually remains as carrier for life, being a risk of infection for susceptible cattle. Anaplasma marginale is an obligate intracellular Gram-negative bacterium with genetic composition highly diverse among geographical isolates. There are currently no vaccines fully effective against bovine anaplasmosis; therefore, the economic losses due to disease are present. Vaccine formulation became a hard task for several pathogens as Anaplasma marginale, but peptide-based vaccines are an interesting proposal way to induce specific responses. Phage-displayed peptide libraries have been proved one of the most powerful technologies for identifying specific ligands. Screening of these peptides libraries is also a tool for studying interactions between proteins or peptides. Thus, it has allowed the identification of ligands recognized by polyclonal antiserums, and it has been successful for the identification of relevant epitopes in chronic diseases and toxicological conditions. Protective immune response to bovine anaplasmosis includes high levels of immunoglobulins subclass G2 (IgG2) but not subclass IgG1. Therefore, IgG2 from the serum of protected bovine can be useful to identify ligands, which can be part of an immunogen for cattle. In this work, phage display random peptide library Ph.D. ™ -12 was incubating with IgG2 or blood sera of immunized bovines against A. marginale as targets. After three rounds of biopanning, several candidates were selected for additional analysis. Subsequently, their reactivity with sera immunized against A. marginale, as well as with positive and negative sera to A. marginale was evaluated by immunoassays. A collection of recognized peptides tested by ELISA was generated. More than three hundred phage-peptides were separately evaluated against molecules which were used during panning. At least ten different peptides sequences were determined from their nucleotide composition. In this approach, three phage-peptides were selected by their binding and affinity properties. In the case of the development of vaccines or diagnostic reagents, it is important to evaluate the immunogenic and antigenic properties of the peptides. Immunogenic in vitro and in vivo behavior of peptides will be assayed as synthetic and as phage-peptide for to determinate their vaccine potential. Acknowledgment: This work was supported by grant SEP-CONACYT 252577 given to I. Amaro-Estrada.

Keywords: bovine anaplasmosis, peptides, phage display, veterinary vaccines

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Authors: Vladimir Messerle, Alexandr Ustimenko, Oleg Lavrichshev

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This work presents plasma technology for solid fuel ignition and combustion. Plasma activation promotes more effective and environmentally friendly low-rank coal ignition and combustion. To realise this technology at coal fired power plants plasma-fuel systems (PFS) were developed. PFS improve efficiency of power coals combustion and decrease harmful emission. PFS is pulverized coal burner equipped with arc plasma torch. Plasma torch is the main element of the PFS. Plasma forming gas is air. It is blown through the electrodes forming plasma flame. Temperature of this flame is varied from 5000 to 6000 K. Plasma torch power is varied from 100 to 350 kW and geometrical sizes are the following: the height is 0.4-0.5 m and diameter is 0.2-0.25 m. The base of the PFS technology is plasma thermochemical preparation of coal for burning. It consists of heating of the pulverized coal and air mixture by arc plasma up to temperature of coal volatiles release and char carbon partial gasification. In the PFS coal-air mixture is deficient in oxygen and carbon is oxidised mainly to carbon monoxide. As a result, at the PFS exit a highly reactive mixture is formed of combustible gases and partially burned char particles, together with products of combustion, while the temperature of the gaseous mixture is around 1300 K. Further mixing with the air promotes intensive ignition and complete combustion of the prepared fuel. PFS have been tested for boilers start up and pulverized coal flame stabilization in different countries at power boilers of 75 to 950 t/h steam productivity. They were equipped with different types of pulverized coal burners (direct flow, muffle and swirl burners). At PFS testing power coals of all ranks (lignite, bituminous, anthracite and their mixtures) were incinerated. Volatile content of them was from 4 to 50%, ash varied from 15 to 48% and heat of combustion was from 1600 to 6000 kcal/kg. To show the advantages of the plasma technology before conventional technologies of coal combustion numerical investigation of plasma ignition, gasification and thermochemical preparation of a pulverized coal for incineration in an experimental furnace with heat capacity of 3 MW was fulfilled. Two computer-codes were used for the research. The computer simulation experiments were conducted for low-rank bituminous coal of 44% ash content. The boiler operation has been studied at the conventional mode of combustion and with arc plasma activation of coal combustion. The experiments and computer simulation showed ecological efficiency of the plasma technology. When a plasma torch operates in the regime of plasma stabilization of pulverized coal flame, NOX emission is reduced twice and amount of unburned carbon is reduced four times. Acknowledgement: This work was supported by Ministry of Education and Science of the Republic of Kazakhstan and Ministry of Education and Science of the Russian Federation (Agreement on grant No. 14.613.21.0005, project RFMEFI61314X0005).

Keywords: coal, ignition, plasma-fuel system, plasma torch, thermal power plant

Procedia PDF Downloads 278

Authors: Alessandro Evangelista, Vito M. Manghisi, Michele Gattullo, Enricoandrea Laviola

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In this work, we propose a custom visualization technique for Out-Of-View-Objects in Virtual and Augmented Reality applications using Head Mounted Displays. In the last two decades, Augmented Reality (AR) and Virtual Reality (VR) technologies experienced a remarkable growth of applications for navigation, interaction, and collaboration in different types of environments, real or virtual. Both environments can be potentially very complex, as they can include many virtual objects located in different places. Given the natural limitation of the human Field of View (about 210° horizontal and 150° vertical), humans cannot perceive objects outside this angular range. Moreover, despite recent technological advances in AR e VR Head-Mounted Displays (HMDs), these devices still suffer from a limited Field of View, especially regarding Optical See-Through displays, thus greatly amplifying the challenge of visualizing out-of-view objects. This problem is not negligible when the user needs to be aware of the number and the position of the out-of-view objects in the environment. For instance, during a maintenance operation on a construction site where virtual objects serve to improve the dangers' awareness. Providing such information can enhance the comprehension of the scene, enable fast navigation and focused search, and improve users' safety. In our research, we investigated how to represent out-of-view-objects in HMD User Interfaces (UI). Inspired by commercial video games such as Call of Duty Modern Warfare, we designed a customized Compass. By exploiting the Unity 3D graphics engine, we implemented our custom solution that can be used both in AR and VR environments. The Compass Bar consists of a graduated bar (in degrees) at the top center of the UI. The values of the bar range from -180 (far left) to +180 (far right), the zero is placed in front of the user. Two vertical lines on the bar show the amplitude of the user's field of view. Every virtual object within the scene is represented onto the compass bar as a specific color-coded proxy icon (a circular ring with a colored dot at its center). To provide the user with information about the distance, we implemented a specific algorithm that increases the size of the inner dot as the user approaches the virtual object (i.e., when the user reaches the object, the dot fills the ring). This visualization technique for out-of-view objects has some advantages. It allows users to be quickly aware of the number and the position of the virtual objects in the environment. For instance, if the compass bar displays the proxy icon at about +90, users will immediately know that the virtual object is to their right and so on. Furthermore, by having qualitative information about the distance, users can optimize their speed, thus gaining effectiveness in their work. Given the small size and position of the Compass Bar, our solution also helps lessening the occlusion problem thus increasing user acceptance and engagement. As soon as the lockdown measures will allow, we will carry out user-tests comparing this solution with other state-of-the-art existing ones such as 3D Radar, SidebARs and EyeSee360.

Keywords: augmented reality, situation awareness, virtual reality, visualization design

Procedia PDF Downloads 129

Authors: Bhojan Dhakal, Naba Raj Devkota, Chet Raj Upreti, Maheshwar Sapkota

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A study was conducted to find out the production potential, nutrient composition, and the variability of the most commonly available fodder trees along with the varying altitude to help optimize the dry matter requirement during winter lean period. The study was carried out from March to June, 2012 in Lho and Prok Village Development Committee of Manaslu Conservation Area (MCA), located in Gorkha district of Nepal. The other objective of the research was to learn the impact of climate change on livestock production linking it with feed availability. The study was conducted in two parts: social and biological. Accordingly, a households (HHs) survey was conducted to collect primary data from 70 HHs, focusing on the perception of respondents on impacts of climatic variability on the feeding management. The next part consisted of understanding yield potential and nutrient composition of the four most commonly available fodder trees (M. azedirach, M. alba, F. roxburghii, F. nemoralis), within two altitudes range: (1500-2000 masl and 2000-2500 masl) by using a RCB design in 2*4 factorial combination of treatments, each replicated four times. Results revealed that majority of the farmers perceived the change in climatic phenomenon more severely within the past five years. Farmers were using different adaptation technologies such as collection of forage from jungle, reducing unproductive animals, fodder trees utilization, and crop by product feeding at feed scarcity period. Ranking of the different fodder trees on the basis of indigenous knowledge and experiences revealed that F. roxburghii was the best-preferred fodder tree species (index value 0.72) in terms overall preferability whereas M. azedirach had highest growth and productivity (index value 0.77), F. roxburghii had highest adoptability (index value 0.69) and palatability (index value 0.69) as well. Similarly, fresh yield and dry matter yield of the each fodder trees was significant (P < 0.01) between the altitude and within species. Fodder trees yield analysis revealed that the highest dry matter (DM) yield (28 kg/tree) was obtained for F. roxburghii but that remained statistically similar (P > 0.05) to the other treatment. On the other hand, most of the parameters: ether extract (EE), acid detergent lignin (ADL), acid detergent fibre (ADF), cell wall digestibility (CWD), relative digestibility (RD), digestible nutrient (TDN), and Calcium (Ca) among the treatments were highly significant (P < 0.01). This indicates the scope of introducing productive and nutritive fodder trees species even at the high altitude to help reduce fodder scarcity problem during winter. The finding also revealed the scope of promoting all available local fodder trees species as crude protein content of these species were similar.

Keywords: fodder trees, yield potential, climate change, nutrient composition

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Authors: Ramanamurthi Botlagunta

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Return migrate on is a process; it’s not a new phenomenal. People are migrating since civilization started. In the case of Indian Diaspora, peoples migrated before the Independence of India. Even after the independence. There are various reasons for the migration. According to the characteristics of the migrants, geographical, political, and economic factors there are many changes occur in the mode of migration. In India currently almost 25 million peoples are outside of the country. But all of them not able to get the immigrants status in their respective host society due to the nature of individual perception and the immigration policies of the host countries. They came back to homeland after spending days/months/years. They are known as the return migrants. Returning migrants are 'persons returning to their country of citizenship after having been international migrants, whether short term or long-term'. Increasingly, migration is seen very differently from what was once believed to be a one-way phenomenon. The renewed interest of return migration can be seen through two aspects one is that growing importance of temporary migration programmers in other countries and other one is that potential role of migrants in developing their home countries. Conceptualized return migration in several ways: occasional return, seasonal return, temporary return, permanent return, and circular return. The reasons for the return migration are retirement, failure to assimilate in the host country, problems with acculturation in the destination country, being unsuccessful in the emigrating country, acquiring the desired wealth, innovate and to serve as change agents in the birth country. With the advent of globalization and the rapid development of transportation systems and communication technologies, this is a process by which immigrants forge and sustain simultaneous multi-stranded social relations that link together their societies of origin and settlement. We can find that Current theories of transnational migration are greatly focused on the economic impacts on the home countries, while social, cultural and political impacts have recently started gaining momentum. This, however, has been changing as globalization is radically transforming the way people move around the world. One of the reasons for the return migration is that lack of proportionate representation of Asian immigrants in positions of authority and decision-making can be a result of challenges confronted in cultural and structural assimilation. The present study mainly focuses socioeconomic and demographic profile of return migration of Indians from other countries in general and particularly on Andhra Pradesh the people who are returning from other countries. Migration is that lack of proportionate representation of Asian immigrants in positions of authority and decision-making can be a result of challenges confronted in cultural and structural assimilation. The present study mainly focuses socioeconomic and demographic profile of return migration of Indians from other countries in general and particularly on Andhra Pradesh the people who are returning from other countries.

Keywords: migration, return migration, globalization, development, socio- economic, Asian immigrants, UN, Andhra Pradesh

Procedia PDF Downloads 376

Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

Procedia PDF Downloads 269

Authors: Aline V. Arroteia, Gustavo G. Do Amaral, Simone Z. Kikuti, Norberto C. S. Moura, Silvio B. Melhado

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Despite the significant advances made by the construction industry in recent years, the crystalized absence of integration between the design and construction phases is still an evident and costly problem in building construction. Globally, the construction industry has sought to adopt collaborative practices through new technologies to mitigate impacts of this fragmented process and to optimize its production. In this new technological business environment, professionals are required to develop new methodologies based on the notion of collaboration and integration of information throughout the building lifecycle. This scenario also represents the industry’s reality in developing nations, and the increasing need for overall efficiency has demanded new educational alternatives at the undergraduate and post-graduate levels. In countries like Brazil, it is the common understanding that Architecture, Engineering and Building Construction educational programs are being required to review the traditional design pedagogical processes to promote a comprehensive notion about integration and simultaneity between the phases of the project. In this context, the coherent inclusion of computation design to all segments of the educational programs of construction related professionals represents a significant research topic that, in fact, can affect the industry practice. Thus, the main objective of the present study was to comparatively measure the effectiveness of the Building Information Modeling courses offered by the University of Sao Paulo, the most important academic institution in Brazil, at the Schools of Architecture and Civil Engineering and the courses offered in well recognized BIM research institutions, such as the School of Design in the College of Architecture of the Georgia Institute of Technology, USA, to evaluate the dissemination of BIM knowledge amongst students in post graduate level. The qualitative research methodology was developed based on the analysis of the program and activities proposed by two BIM courses offered in each of the above-mentioned institutions, which were used as case studies. The data collection instruments were a student questionnaire, semi-structured interviews, participatory evaluation and pedagogical practices. The found results have detected a broad heterogeneity of the students regarding their professional experience, hours dedicated to training, and especially in relation to their general knowledge of BIM technology and its applications. The research observed that BIM is mostly understood as an operational tool and not as methodological project development approach, relevant to the whole building life cycle. The present research offers in its conclusion an assessment about the importance of the incorporation of BIM, with efficiency and in its totality, as a teaching method in undergraduate and graduate courses in the Brazilian architecture, engineering and building construction schools.

Keywords: building information modeling (BIM), BIM education, BIM process, design teaching

Procedia PDF Downloads 157

Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: Anishur Rahman, Maria Areias, Diogo Simões, Ana Figeuiredo, Filipa Figueiredo, João Nunes

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The circular economy (CE) has gained increased awareness among academics, businesses, and decision-makers as it stimulates resource circularity in the production and consumption systems. A large epistemological study has explored the principles of CE, but scant attention eagerly focused on analysing how CE is evaluated, consented to, and enforced using economic metabolism data and business intelligent framework. Economic metabolism involves the ongoing exchange of materials and energy within and across socio-economic systems and requires the assessment of vast amounts of data to provide quantitative analysis related to effective resource management. Limited concern, the present work has focused on the regional flows pilot region from Portugal. By addressing this gap, this study aims to promote eco-innovation and sustainability in the regions of Intermunicipal Communities Região de Coimbra, Viseu Dão Lafões and Beiras e Serra da Estrela, using this data to find precise synergies in terms of material flows and give companies a competitive advantage in form of valuable waste destinations, access to new resources and new markets, cost reduction and risk sharing benefits. In our work, emphasis on applying artificial intelligence (AI) and, more specifically, on implementing state-of-the-art deep learning algorithms is placed, contributing to construction a business intelligent approach. With the emergence of new approaches generally highlighted under the sub-heading of AI and machine learning (ML), the methods for statistical analysis of complex and uncertain production systems are facing significant changes. Therefore, various definitions of AI and its differences from traditional statistics are presented, and furthermore, ML is introduced to identify its place in data science and the differences in topics such as big data analytics and in production problems that using AI and ML are identified. A lifecycle-based approach is then taken to analyse the use of different methods in each phase to identify the most useful technologies and unifying attributes of AI in manufacturing. Most of macroeconomic metabolisms models are mainly direct to contexts of large metropolis, neglecting rural territories, so within this project, a dynamic decision support model coupled with artificial intelligence tools and information platforms will be developed, focused on the reality of these transition zones between the rural and urban. Thus, a real decision support tool is under development, which will surpass the scientific developments carried out to date and will allow to overcome imitations related to the availability and reliability of data.

Keywords: circular economy, artificial intelligence, economic metabolisms, machine learning

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Ashir Ahmed

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Peacekeeping is a collective responsibility of governments, law enforcement agencies, communities, families, and individuals. Moreover, the complex nature of peacekeeping activities requires a holistic and collaborative approach where various community sectors work together to form collective strategies that are likely to be more effective than strategies designed and delivered in isolation. Similarly, it is important to learn from past programs to evaluate the initiatives that have worked well and the areas that need further improvement. Review of recent peacekeeping initiatives suggests that there have been tremendous efforts and resources put in place to deal with the emerging threat of terrorism, radicalization and violent extremism through number of de-radicalization programs. Despite various attempts in designing and delivering successful programs for deradicalization, the threat of people being radicalized is growing more than ever before. This research reviews the prominent de-radicalization programs to draw an understanding of their strengths and weaknesses. Some of the weaknesses in the existing programs include. Inaccessibility: Limited resources, geographical location of potential participants (for offline programs), inaccessibility or inability to use various technologies (for online programs) makes it difficult for people to participate in de-radicalization programs. Timeliness: People might need to wait for a program on a set date/time to get the required information and to get their questions answered. This is particularly true for offline programs. Lack of trust: The privacy issues and lack of trust between participants and program organizers are another hurdle in the success of de-radicalization programs. The fear of sharing participants information with organizations (such as law enforcement agencies) without their consent led them not to participate in these programs. Generalizability: Majority of these programs are very generic in nature and do not cater the specific needs of an individual. Participants in these programs may feel that the contents are irrelevant to their individual situations and hence feel disconnected with purpose of the programs. To address the above-mentioned weaknesses, this research developed a framework that recommends some improvements in de-radicalization programs. One of the recommendations is to offer 24/7, secure, private and online hotline (also referred as helpline) for the people who have any question, concern or situation to discuss with someone who is qualified (a counsellor) to deal with people who are vulnerable to be radicalized. To make these hotline services viable and sustainable, the existing organizations offering support for depression, anxiety or suicidal ideation could additionally host these services. These helplines should be available via phone, the internet, social media and in-person. Since these services will be embedded within existing and well-known services, they would likely to get more visibility and promotion. The anonymous and secure conversation between a person and a counsellor would ensure that a person can discuss the issues without being afraid of information sharing with any third party – without his/her consent. The next stage of this project would include the operationalization of the framework by collaborating with other organizations to host de-radicalization hotlines and would assess the effectiveness of such initiatives.

Keywords: de-radicalization, framework, hotlines, peacekeeping

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Authors: Teimuraz Manjapharashvili, Ketevani Manjaparashvili

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Fuzzy logic is a modeling method for complex or ill-defined systems and is a relatively new mathematical approach. Its basis is to consider overlapping cases of parameter values and define operations to manipulate these cases. Fuzzy logic can successfully create operative automatic management or appropriate advisory systems. Fuzzy logic techniques in various operational control technologies have grown rapidly in the last few years. Fuzzy logic is used in many areas of human technological activity. In recent years, Fuzzy logic has proven its great potential, especially in the automation of industrial process control, where it allows the form of a control design based on the experience of experts and the results of experiments. The engineering of chemical technological processes uses fuzzy logic in optimal management, and it is also used in process control, including the operational control of continuous cycle chemical industrial, technological processes, where special features appear due to the continuous cycle and correct management acquires special importance. This paper discusses how intelligent systems can be developed, in particular, how Fuzzy logic can be used to build knowledge-based expert systems in chemical process engineering. The implemented projects reveal that the use of Fuzzy logic in technological process control has already given us better solutions than standard control techniques. Fuzzy logic makes it possible to develop an advisory system for decision-making based on the historical experience of the managing operator and experienced experts. The present paper deals with operational control and management systems of continuous cycle chemical technological processes, including advisory systems. Because of the continuous cycle, many features are introduced in them compared to the operational control of other chemical technological processes. Among them, there is a greater risk of transitioning to emergency mode; the return from emergency mode to normal mode must be done very quickly due to the impossibility of stopping the technological process due to the release of defective products during this period (i.e., receiving a loss), accordingly, due to the need for high qualification of the operator managing the process, etc. For these reasons, operational control systems of continuous cycle chemical technological processes have been specifically discussed, as they are different systems. Special features of such systems in control and management were brought out, which determine the characteristics of the construction of control and management systems. To verify the findings, the development of an advisory decision-making information system for operational control of a lime kiln using Fuzzy logic, based on the creation of a relevant expert-targeted knowledge base, was discussed. The control system has been implemented in a real lime production plant with a lime burn kiln, which has shown that suitable and intelligent automation improves operational management, reduces the risks of releasing defective products, and, therefore, reduces costs. The special advisory system was successfully used in the said plant both for the improvement of operational management and, if necessary, for the training of new operators due to the lack of an appropriate training institution.

Keywords: chemical process control systems, continuous cycle industrial technological processes, fuzzy logic, lime kiln

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Authors: Biswaranjan D. Mohapatra, Ipsha Hota, Swarna P. Mantry, Nibedita Behera, Kumar S. K. Varadwaj

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Designing highly active and low-cost oxygen evolution (2H₂O → 4H⁺ + 4e⁻ + O₂) electrocatalyst is one of the most active areas of advanced energy research. Some precious metal-based electrocatalysts, such as IrO₂ and RuO₂, have shown excellent performance for oxygen evolution reaction (OER); however, they suffer from high-cost and low abundance which limits their applications. Recently, layered double hydroxides (LDHs), composed of layers of divalent and trivalent transition metal cations coordinated to hydroxide anions, have gathered attention as an alternative OER catalyst. However, LDHs are insulators and coupled with carbon materials for the electrocatalytic applications. Graphene covalently doped with nitrogen has been demonstrated to be an excellent electrocatalyst for energy conversion technologies such as; oxygen reduction reaction (ORR), oxygen evolution reaction (OER) & hydrogen evolution reaction (HER). However, they operate at high overpotentials, significantly above the thermodynamic standard potentials. Recently, we reported remarkably enhanced catalytic activity of benzoate or 1-pyrenebutyrate functionalized N-doped graphene towards the ORR in alkaline medium. The molecular and heteroatom co-doping on graphene is expected to tune the electronic structure of graphene. Therefore, an innovative catalyst architecture, in which LDHs are anchored on aromatic acid functionalized ‘N’ doped graphene may presumably boost the OER activity to a new benchmark. Herein, we report fabrication of Co₂Fe-LDH on aromatic acid (AA) functionalized ‘N’ doped reduced graphene oxide (NG) and studied their OER activities in alkaline medium. In the first step, a novel polyol method is applied for synthesis of AA functionalized NG, which is well dispersed in aqueous medium. In the second step, Co₂Fe LDH were grown on AA functionalized NG by co-precipitation method. The hybrid samples are abbreviated as Co₂Fe LDH/AA-NG, where AA is either Benzoic acid or 1, 3-Benzene dicarboxylic acid (BDA) or 1, 3, 5 Benzene tricarboxylic acid (BTA). The crystal structure and morphology of the samples were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM) and transmission electron microscope (TEM). These studies confirmed the growth of layered single phase LDH. The electrocatalytic OER activity of these hybrid materials was investigated by rotating disc electrode (RDE) technique on a glassy carbon electrode. The linear sweep voltammetry (LSV) on these catalyst samples were taken at 1600rpm. We observed significant OER performance enhancement in terms of onset potential and current density on Co₂Fe LDH/BTA-NG hybrid, indicating the synergic effect. This exploration of molecular functionalization effect in doped graphene and LDH system may provide an excellent platform for innovative design of OER catalysts.

Keywords: π-π functionalization, layered double hydroxide, oxygen evolution reaction, reduced graphene oxide

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Authors: Ella Bowles, Alexandra Hughes, Clara Karton, T. Blaine Hoshizaki

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As a fast-paced and physical game, body contact is an inevitable component in professional men's ice hockey. Despite efforts and advancements in material engineering to create safer equipment, brain trauma continues to persist and burden hockey players. Head and body contact occur in many ways and vary in terms of impact characteristics including the inbound velocity, force, direction, location, and compliance of the surfaces, which in turn influence head dynamics and brain injury outcomes including concussions. It has been reported that glass and board impacts account for approximately 40% of diagnosed concussions. This type of impact often involves the body (i.e., shoulder) contacting the surface prior to head contact, which may influence the head’s dynamic response by interrupting the head’s initial trajectory.  However, the effect of body-first contact during head impacts is not well understood. The purpose of this research is to compare the head’s kinematic response during direct and non-direct (body-first) head-to-glass impacts representative of ice hockey events. Analysis was performed under varying impact conditions of neck stiffness and impact velocity as they have been shown to influence the resulting head dynamics. Data was collected by video analysis of the 2016-17 NHL season and event reconstructions were performed using a Hybrid III headform, an unbiased neck with tension springs (uONSA), and a high-speed impactor. Direct and non-direct impacts were analyzed at three common velocities (3.0, 5.0, 7.0 m/s), and three neck stiffnesses representing low (25%), medium (75%), and high (100%) contraction. Reconstructions representing non-direct head-to-glass impacts used a shoulder bumper as the first point of contact followed by the head’s contact with the glass. The same method and equipment were used to replicate the direct head impacts, where the head made initial contact with the glass. The dynamic response of the head, specifically the peak resultant linear and rotational acceleration, was collected for each impact and compared between direct and non-direct contact under each condition. The results show that non-direct impacts created an initial head acceleration resulting from shoulder contact, preceding a secondary acceleration response from head contact with the glass. Compared to direct head impacts, non-direct impacts consistently resulted in lower linear and rotational acceleration of the head under all neck stiffness and velocity conditions with an average decrease of 32.56 g and 689.33 rad/s2. However, the linear acceleration produced from shoulder contact in non-direct impacts resulted in a higher response compared to direct impacts with low neck stiffness at 5 m/s (55.2g and 41.2g, respectively) and 7 m/s (76.1g and 73.4g, respectively), and medium neck stiffness at 5 m/s (55.4g and 43.9g, respectively ) and 7 m/s (94.4g and 69.5g, respectively. These findings show that non-direct impacts produce complex scenarios that are further influenced by interaction with neck stiffness and velocity. This research provides an understanding of the fundamentals of body-first impacts. With this basis, an understanding of the implications of body-first head-impacts to better distinguish trauma based on events, and adapt protocols, evaluations, technologies, and equipment accordingly.

Keywords: body-first, concussion, direct, hockey, kinematics

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Authors: Anthony Nyamekeh-Armah Adjei, Toshiaki Aoki

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The unreliable and poor efficiency of electricity distribution leading to frequent power outages and high losses are the major challenge facing the power distribution sector in Ghana. Distribution system routes electricity from the power generating station at a higher voltage through the transmission grid and steps it down through the low voltage lines to end users. Approximately all electricity problems and disturbances that have increased the call for renewable and sustainable energy in recent years have their roots in the distribution system. Therefore, sustaining electricity distribution efficiency can potentially contribute to the reserve of natural energy resources use in power generation, reducing greenhouse gas emission (GHG), decreasing tariffs for consumers and effective human security. Human Security is a people-centered approach where individual human being is the principal object of concern, focuses on protecting the vital core of all human lives in ways for meeting basic needs that enhance the safety and protection of individuals and communities. The vulnerability is the diminished capacity of an individual or group to anticipate, resist and recover from the effect of natural, human-induced disaster. The research objectives are to explore the causes of frequent power outages to consumers, high losses in the distribution network and the effect of poor electricity distribution efficiency on the vulnerable (poor and ordinary) people that mostly depend on electricity for their daily activities or life to survive. The importance of the study is that in a developing country like Ghana where raising a capital for new infrastructure project is difficult, it would be beneficial to enhance the efficiency that will significantly minimize the high energy losses, reduce power outage, to ensure safe and reliable delivery of electric power to consumers to secure the security of people’s livelihood. The methodology used in this study is both interview and questionnaire survey to analyze the response from the respondents on causes of power outages and high losses facing the electricity company of Ghana (ECG) and its effect on the livelihood on the vulnerable people. Among the outcome of both administered questionnaire and the interview survey from the field were; poor maintenance of existing sub-stations, use of aging equipment, use of poor distribution infrastructure and poor metering and billing system. The main observation of this paper is that the poor network efficiency (high losses and power outages) affects the livelihood of the vulnerable people. Therefore, the paper recommends that policymakers should insist on all regulation guiding electricity distribution to improve system efficiency. In conclusion, there should be decentralization of off-grid solar PV technologies to provide a sustainable and cost-effective, which can increase daily productivity and improve the quality of life of the vulnerable people in the rural communities.

Keywords: electricity efficiency, high losses, human security, power outage

Procedia PDF Downloads 288

Authors: Fabrizio Ascione, Martina Borrelli, Rosa Francesca De Masi, Silvia Ruggiero, Giuseppe Peter Vanoli

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The Directive 2010/31/EC 'Directive of the European Parliament and of the Council of 19 may 2010 on the energy performance of buildings' moved the targets of the previous version toward more ambitious targets, for instance by establishing that, by 31 December 2020, all new buildings should demand nearly zero-energy. Moreover, the demonstrative role of public buildings is strongly affirmed so that also the target nearly zero-energy buildings is anticipated, in January 2019. On the other hand, given the very low turn-over rate of buildings (in Europe, it ranges between 1-3%/yearly), each policy that does not consider the renovation of the existing building stock cannot be effective in the short and medium periods. According to this proposal, the study provides a novel, holistic approach to design the refurbishment of educational buildings in colder cities of Mediterranean regions enabling stakeholders to understand the uncertainty to use numerical modelling and the real environmental and economic impacts of adopting some energy efficiency technologies. The case study is a university building of Molise region in the centre of Italy. The proposed approach is based on the application of the cost-optimal methodology as it is shown in the Delegate Regulation 244/2012 and Guidelines of the European Commission, for evaluating the cost-optimal level of energy performance with a macroeconomic approach. This means that the refurbishment scenario should correspond to the configuration that leads to lowest global cost during the estimated economic life-cycle, taking into account not only the investment cost but also the operational costs, linked to energy consumption and polluting emissions. The definition of the reference building has been supported by various in-situ surveys, investigations, evaluations of the indoor comfort. Data collection can be divided into five categories: 1) geometrical features; 2) building envelope audit; 3) technical system and equipment characterization; 4) building use and thermal zones definition; 5) energy building data. For each category, the required measures have been indicated with some suggestions for the identifications of spatial distribution and timing of the measurements. With reference to the case study, the collected data, together with a comparison with energy bills, allowed a proper calibration of a numerical model suitable for the hourly energy simulation by means of EnergyPlus. Around 30 measures/packages of energy, efficiency measure has been taken into account both on the envelope than regarding plant systems. Starting from results, two-point will be examined exhaustively: (i) the importance to use validated models to simulate the present performance of building under investigation; (ii) the environmental benefits and the economic implications of a deep energy refurbishment of the educational building in cold climates.

Keywords: energy simulation, modelling calibration, cost-optimal retrofit, university building

Procedia PDF Downloads 182