Search results for: machine translation (MT)

Authors: Daniel Dahis, Haim Azhari

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Noninvasive image-guided intracranial treatments using high intensity focused ultrasound (HIFU) are on the course of translation into clinical applications. They include, among others, tumor ablation, hyperthermia, and blood-brain-barrier (BBB) penetration. Since many of these procedures are associated with local temperature elevation, thermal monitoring is essential. MRI constitutes an imaging method with high spatial resolution and thermal mapping capacity. It is the currently leading modality for temperature guidance, commonly under the name MRgHIFU (magnetic-resonance guided HIFU). Nevertheless, MRI is a very expensive non-portable modality which jeopardizes its accessibility. Ultrasonic thermal monitoring, on the other hand, could provide a modular, cost-effective alternative with higher temporal resolution and accessibility. In order to assess the feasibility of ultrasonic brain thermal monitoring, this study investigated the usage of brain tissue attenuation coefficient (AC) temporal changes as potential estimators of thermal changes. Newton's law of cooling describes a temporal exponential decay behavior for the temperature of a heated object immersed in a relatively cold surrounding. Similarly, in the case of cerebral HIFU treatments, the temperature in the region of interest, i.e., focal zone, is suggested to follow the same law. Thus, it was hypothesized that the AC of the irradiated tissue may follow a temporal exponential behavior during cool down regime. Three ex-vivo bovine brain tissue specimens were inserted into plastic containers along with four thermocouple probes in each sample. The containers were placed inside a specially built ultrasonic tomograph and scanned at room temperature. The corresponding pixel-averaged AC was acquired for each specimen and used as a reference. Subsequently, the containers were placed in a beaker containing hot water and gradually heated to about 45ᵒC. They were then repeatedly rescanned during cool down using ultrasonic through-transmission raster trajectory until reaching about 30ᵒC. From the obtained images, the normalized AC and its temporal derivative as a function of temperature and time were registered. The results have demonstrated high correlation (R² > 0.92) between both the brain AC and its temporal derivative to temperature. This indicates the validity of the hypothesis and the possibility of obtaining brain tissue temperature estimation from the temporal AC thermal changes. It is important to note that each brain yielded different AC values and slopes. This implies that a calibration step is required for each specimen. Thus, for a practical acoustic monitoring of the brain, two steps are suggested. The first step consists of simply measuring the AC at normal body temperature. The second step entails measuring the AC after small temperature elevation. In face of the urging need for a more accessible thermal monitoring technique for brain treatments, the proposed methodology enables a cost-effective high temporal resolution acoustical temperature estimation during HIFU treatments.

Keywords: attenuation coefficient, brain, HIFU, image-guidance, temperature

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: Yibeltal Chanie Manie, Zebider Asire Munyelet

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5G and 6G technology offers enhanced quality of service with high data transmission rates, which necessitates the implementation of the Internet of Things (IoT) in 5G/6G architecture. In this paper, we proposed the integration of free space optical communication (FSO) with fiber sensor networks for IoT applications. Recently, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to flexibility, high achievable optical bandwidth, and low power consumption in several applications of communications, such as disaster recovery, last-mile connectivity, drones, surveillance, backhaul, and satellite communications. Hence, high-speed FSO is an optimal choice for wireless networks to satisfy the full potential of 5G/6G technology, offering 100 Gbit/s or more speed in IoT applications. Moreover, machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. In addition, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. Moreover, we proposed deep learning techniques to estimate the strain changes and peak wavelength of multiple Fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment.

Keywords: optical sensor, artificial Intelligence, Internet of Things, free-space optics

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Authors: Sanghoon Shim, Jaehwan Jung, Sung-Ah Kim

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As the digital technology develops, studies on the TUI (Tangible User Interface) that links the physical environment utilizing the human senses with the virtual environment through the computer are actively being conducted. In addition, there has been a tremendous advance in computer design making through the use of computer-aided design techniques, which enable optimized decision-making through comparison with machine learning and parallel comparison of alternatives. However, a complex design that can respond to user requirements or performance can emerge through the intuition of the designer, but it is difficult to actualize the emerged design by the designer's ability alone. Ancillary tools such as Gaudí's Sandbag can be an instrument to reinforce and evolve emerged ideas from designers. With the advent of many commercial tools that support 3D objects, designers' intentions are easily reflected in their designs, but the degree of their reflection reflects their intentions according to the proficiency of design tools. This study embodies the environment in which the form can be implemented by the fingers of the most basic designer in the initial design phase of the complex type building design. Leapmotion is used as a sensor to recognize the hand motions of the designer, and it is converted into digital information to realize an environment that can be linked in real time in virtual reality (VR). In addition, the implemented design can be linked with Rhino™, a 3D authoring tool, and its plug-in Grasshopper™ in real time. As a result, it is possible to design sensibly using TUI, and it can serve as a tool for assisting designer intuition.

Keywords: design environment, digital modeling, hand gesture, TUI, virtual reality

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Authors: Chen Chan Wei

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The purpose of this study was to investigate the impact of swim 1500m, 10000m run, VO2 max, and body fat on Olympic distance triathlon competition performance. The subjects were thirteen college triathletes with endurance training, with an average age, height and weight of 20.61±1.04 years (mean ± SD), 171.76±8.54 cm and 65.32±8.14 kg respectively. All subjects were required to take the tests of swim 1500m, run 10000m, VO2 max, body fat, and participate in the Olympic distance triathlon competition. First, the swim 1500m test was taken in the standardized 50m pool, with a depth of 2m, and the 10000m run test on the standardized 400m track. After three days, VO2 max was tested with the MetaMax 3B and body fat was measured with the DEXA machine. After two weeks, all 13 subjects joined the Olympic distance triathlon competition at the 2016 New Taipei City Asian Cup. The relationships between swim 1500m, 10000m run, VO2 max, body fat test, and Olympic distance triathlon competition performance were evaluated using Pearson's product-moment correlation. The results show that 10000m run and body fat had a significant positive correlation with Olympic distance triathlon performance (r=.830, .768), but VO2 max has a significant negative correlation with Olympic distance triathlon performance (r=-.735). In conclusion, for improved non-draft Olympic distance triathlon performance, triathletes should focus on running than swimming training and can be measure VO2 max to prediction triathlon performance. Also, managing body fat can improve Olympic distance triathlon performance. In addition, swimming performance was not significantly correlated to Olympic distance triathlon performance, possibly because the 2016 New Taipei City Asian Cup age group was not a drafting competition. The swimming race is the shortest component of Olympic distance triathlons. Therefore, in a non-draft competition, swimming ability is not significantly correlated with overall performance.

Keywords: triathletes, olympic, non-drafting, correlation

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Authors: Noboru Wakamoto, Kiyotaka Obunai, Kazuya Okubo, Toru Fujii

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The aim of this study is to moderate the dependence of counter frictional coefficient on temperature between counter surfaces and to reduce the wear of C/C composites at low temperature. To modify the C/C composites, Silica (SiO₂) powders were added into phenolic resin for carbon precursor. The preform plate of the precursor of C/C composites was prepared by conventional filament winding method. The C/C composites plates were obtained by carbonizing preform plate at 2200 °C under an argon atmosphere. At that time, the silicon carbides (SiC) were synthesized around the surfaces and the internal vacancies of the C/C composites. The frictional coefficient on the counter surfaces and specific wear volumes of the C/C composites were measured by our developed frictional test machine like pin-on disk type. The XRD indicated that SiC was synthesized in the body of C/C composite fabricated by current method. The results of friction test showed that coefficient of friction of unmodified C/C composites have temperature dependence when the test condition was changed. In contrast, frictional coefficient of the C/C composite modified with SiO₂ powders was almost constant at about 0.27 when the temperature condition was changed from Room Temperature (RT) to 300 °C. The specific wear rate decreased from 25×10^-6 mm²/N to 0.1×10^-6 mm²/N. The observations of the surfaces after friction tests showed that the frictional surface of the modified C/C composites was covered with a film produced by the friction. This study found that synthesizing SiC around surface and internal vacancies of C/C composites was effective to moderate the dependence on the frictional coefficient and reduce to the abrasion of C/C composites.

Keywords: C/C composites, friction coefficient, wear, SiC

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Authors: Chevon Fernando, Banuka Athuraliya

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The purpose of this research is to provide a solution to the students’ time management, which usually becomes an issue because students must study and manage their personal commitments. "TimeTune," an AI-based study planner that provides an opportunity to maneuver study timeframes by incorporating modern machine learning algorithms with calendar applications, is unveiled as the ideal solution. The research is focused on the development of LSTM models that connect to the Google Calendar API in the process of developing learning paths that would be fit for a unique student's daily life experience and study history. A key finding of this research is the success in building the LSTM model to predict optimal study times, which, integrating with the real-time data of Google Calendar, will generate the timetables automatically in a personalized and customized manner. The methodology encompasses Agile development practices and Object-Oriented Analysis and Design (OOAD) principles, focusing on user-centric design and iterative development. By adopting this method, students can significantly reduce the tension associated with poor study habits and time management. In conclusion, "TimeTune" displays an advanced step in personalized education technology. The fact that its application of ML algorithms and calendar integration is quite innovative is slowly and steadily revolutionizing the lives of students. The excellence of maintaining a balanced academic and personal life is stress reduction, which the applications promise to provide for students when it comes to managing their studies.

Keywords: personalized learning, study planner, time management, calendar integration

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Authors: Kasidis Chomrat, Nopasit Chakpitak, Anukul Tamprasirt, Annop Thananchana

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Internet of Things devices or IoT and Edge Computing has become one of the biggest things happening in innovations and one of the most discussed of the potential to improve and disrupt traditional business and industry alike. With rises of new hang cliff challenges like COVID-19 pandemic that posed a danger to workforce and business process of the system. Along with drastically changing landscape in business that left ruined aftermath of global COVID-19 pandemic, looming with the threat of global energy crisis, global warming, more heating global politic that posed a threat to become new Cold War. How emerging technology like edge computing and usage of specialized design visual processing units will be great opportunities for business. The literature reviewed on how the internet of things and disruptive wave will affect business, which explains is how all these new events is an effect on the current business and how would the business need to be adapting to change in the market and world, and example test benchmarking for consumer marketed of newer devices like the internet of things devices equipped with new edge computing devices will be increase efficiency and reducing posing a risk from a current and looming crisis. Throughout the whole paper, we will explain the technologies that lead the present technologies and the current situation why these technologies will be innovations that change the traditional practice through brief introductions to the technologies such as cloud computing, edge computing, Internet of Things and how it will be leading into future.

Keywords: internet of things, edge computing, machine learning, pattern recognition, image classification

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Authors: Genevieve Jorolan-Quintero

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The recording as well as the preservation of indigenous literature is an important task as it deals with a significant heritage of pre-colonial culture. The beliefs and traditions of a people are reflected in their oral narratives, such as the folk epic, which must be written down to insure their preservation. The epic poem for instance, known as dawot among the Mandaya, one of the indigenous communities in the southern region of the Philippines, narrates the customs, the ways of life, and the adventures of an ancient people. Nabayra, an expert on Philippine folkloric studies, stresses that still extant after centuries of unknown origin, the dawot was handed down to the magdadawot (bard) by word of mouth, forming the greatest bulk of Mandaya oral tradition. Unhampered by modern means of communication to distract her/him, the magdadawot has a sharp memory of the intricacies of the ancient art of chanting the panayday (verses) of the epic poem. The dawot has several hullubaton (episodes), each of which takes several nights to chant . The language used in these oral traditions is archaic Mandaya, no longer spoken or clearly understood by the present generation. There is urgency to the task of recording and writing down what remain of the epic poem since the singers and storytellers who have retained the memory and the skill of chanting and narrating the dawot and other forms of oral tradition in their original forms are getting fewer. The few who are gifted and skilled to transmit these ancient arts and wisdom are old and dying. Unlike the other Philippine epics (i.e. the Darangen, the Ulahingan, the Hinilawod, etc.), the Mandaya epic is yet to be recognized and given its rightful place among the recorded epics in Philippine Folk Literature. The general aim of this study was to put together and preserve an intangible heritage, the Mandaya hullubaton (episodes of the dawot), in order to preserve and promote appreciation for the oral traditions and cultural legacy of the Mandaya. It was able to record, transcribe, and translate four hullubaton of the folk epic into two languages, Visayan and English to insure understanding of their contents and significance among non-Mandaya audiences. Evident in the contents of the episodes are the cultural practices, ideals, life values, and traditions of the ancient Mandaya. While the conquests and adventures of the Mandaya heroes Lumungtad, Dilam, and Gambong highlight heroic virtues, the role of the Mandaya matriarch in family affairs is likewise stressed. The recording and the translation of the hullubaton and the dawot into commonly spoken languages will not only promote knowledge and understanding about their culture, but will also stimulate in the members of this cultural community a sense of pride for their literature and culture. Knowledge about indigenous cultural system and philosophy derived from their oral literature will serve as a springboard to further comparative researches dealing with indigenous mores and belief systems among the different tribes in the Philippines, in Asia, in Africa, and other countries in the world.

Keywords: Dawot, epic poem, Mandaya, Philippine folk literature

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Authors: Bongs Lainjo

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Educational institutions and authorities that are mandated to run education systems in various countries need to implement a curriculum that considers the possibility and existence of elementary school dropouts. This research focuses on elementary school dropout rates and the ability to replicate various predictive models carried out globally on selected Elementary Schools. The study was carried out by comparing the classical case studies in Africa, North America, South America, Asia and Europe. Some of the reasons put forward for children dropping out include the notion of being successful in life without necessarily going through the education process. Such mentality is coupled with a tough curriculum that does not take care of all students. The system has completely led to poor school attendance - truancy which continuously leads to dropouts. In this study, the focus is on developing a model that can systematically be implemented by school administrations to prevent possible dropout scenarios. At the elementary level, especially the lower grades, a child's perception of education can be easily changed so that they focus on the better future that their parents desire. To deal effectively with the elementary school dropout problem, strategies that are put in place need to be studied and predictive models are installed in every educational system with a view to helping prevent an imminent school dropout just before it happens. In a competency-based curriculum that most advanced nations are trying to implement, the education systems have wholesome ideas of learning that reduce the rate of dropout.

Keywords: elementary school, predictive models, machine learning, risk factors, data mining, classifiers, dropout rates, education system, competency-based curriculum

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Authors: Li Xu, Huanbao Jiang, Zhenfei Huang, Lailai Zhang

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In recent years, as global warming, the sea-ice extent of North Arctic undergoes an evident decrease and Arctic channel has attracted the attention of shipping industry. Ice crystals existing in the seawater of Arctic channel which enter the seawater system of the ship with the seawater were found blocking the seawater pipe. The appearance of cooler paralysis, auxiliary machine error and even ship power system paralysis may be happened if seriously. In order to reduce the effect of high temperature in auxiliary equipment, seawater system will use external ice-water to participate in the cooling cycle and achieve the state of its flow. The distribution of ice crystals in seawater pipe can be achieved. As the ice slurry system is solid liquid two-phase system, the flow process of ice-water mixture is very complex and diverse. In this paper, the flow process in seawater pipe of ice slurry is simulated with fluid dynamics simulation software based on k-ε turbulence model. As the ice packing fraction is a key factor effecting the distribution of ice crystals, the influence of ice packing fraction on the flowing process of ice slurry is analyzed. In this work, the simulation results show that as the ice packing fraction is relatively large, the distribution of ice crystals is uneven in the flowing process of the seawater which has such disadvantage as increase the possibility of blocking, that will provide scientific forecasting methods for the forming of ice block in seawater piping system. It has important significance for the reliability of the operating of polar ships in the future.

Keywords: ice slurry, seawater pipe, ice packing fraction, numerical simulation

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Hugo Sampaio Libero, Max de Castro Magalhaes

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The perception of sound radiated from a building floor is greatly influenced by the rooms in which it is immersed and by the position of both listener and source. The main question that remains unanswered is related to the influence of the source position on the sound power radiated by a complex wall-floor system in buildings. This research is concerned with the investigation of vibration transmission across walls and floors in buildings. It is primarily based on the determination of vibration reduction index via experimental tests. Knowledge of this parameter may help in predicting noise and vibration propagation in building components. First, the physical mechanisms involving vibration transmission across structural junctions are described. An experimental setup is performed to aid this investigation. The experimental tests have shown that the vibration generation in the walls and floors is directed related to their size and boundary conditions. It is also shown that the vibration source position can affect the overall vibration spectrum significantly. Second, the characteristics of the noise spectra inside the rooms due to an impact source (tapping machine) are also presented. Conclusions are drawn for the general trend of vibration and noise spectrum of the structural components and rooms, respectively. In summary, the aim of this paper is to investigate the vibro-acoustical behavior of building floors and walls under floor impact excitation. The impact excitation was at distinct positions on the slab. The analysis has highlighted the main physical characteristics of the vibration transmission mechanism.

Keywords: vibration transmission, vibration reduction index, impact excitation, experimental tests

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Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

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Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: conditional generative adversarial net, market and credit risk management, neural network, time series

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Authors: Alexander Zibenberg, Rima’a Da’As

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According to the author’s knowledge, individuals are naturally inclined to classify people as leaders and followers. Individuals utilize cognitive structures or prototypes specifying the traits and abilities that characterize the effective leader (implicit leadership theories) and effective follower in an organization (implicit followership theories). Thus, the present study offers insights into understanding how teachers' personal values (self-enhancement and self-transcendence) explain the preference for styles of effective leader (i.e., principal) and assumptions about the traits and behaviors that characterize effective followers (i.e., student). Beyond the direct effect on perceptions of effective types of leader and follower, the present study argues that values may also interact with organizational and personal contexts in influencing perceptions. Thus authors suggest that teachers' managerial position may moderate the relationships between personal values and perception of the effective leader and follower. Specifically, two key questions are addressed in the present research: (1) Is there a relationship between personal values and perceptions of the effective leader and effective follower? and (2) Are these relationships stable or could they change across different contexts? Two hundred fifty-five Israeli teachers participated in this study, completing questionnaires – about the effective student and effective principal. Results of structural equations modeling (SEM) with maximum likelihood estimation showed: first: the model fit the data well. Second: researchers found a positive relationship between self-enhancement and anti-prototype of the effective principal and anti-prototype of the effective student. The relationship between self-transcendence value and both perceptions were found significant as well. Self-transcendence positively related to the way the teacher perceives the prototype of the effective principal and effective student. Besides, authors found that teachers' managerial position moderates these relationships. The article contributes to the literature both on perceptions and on personal values. Although several earlier studies explored issues of implicit leadership theories and implicit followership theories, personality characteristics (values) have garnered less attention in this matter. This study shows that personal values which are deeply rooted, abstract motivations that guide justify or explain attitudes, norms, opinions and actions explain differences in perception of the effective leader and follower. The results advance the theoretical understanding of the relationship between personal values and individuals’ perceptions in organizations. An additional contribution of this study is the application of the teacher's managerial position to explain a potential boundary condition of the translation of personal values into outcomes. The findings suggest that through the management process in the organization, teachers acquire knowledge and skills which augment their ability (beyond their personal values) to predict perceptions of ideal types of principal and student. The study elucidates the unique role of personal values in understanding an organizational thinking in organization. It seems that personal values might explain the differences in individual preferences of the organizational paradigm (mechanistic vs organic).

Keywords: implicit leadership theories, implicit followership theories, organizational paradigms, personal values

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Authors: Engi E. I. Sarhan, Usama M. Rashad, Ibrahim M. I. Hamoda, Mohammed K. Mohamed

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Background: This study aimed to know the effect of nitrogen-based cryotherapy on the spasticity of calf muscle in stroke patients. Patients were selected from the outpatient clinic of Neurology, Al-Mansoura general hospital, Al-Mansoura University. Subjects and methods: Thirty Stroke Patients of both sexes ranged from 45 to 60 years old were divided randomly into two equal groups, a study group (A) received a nitrogen-based cryotherapy, a selective physical therapy program and ankle foot orthosis (AFO), while as patients in control group (B) received the same program and AFO only. The treatment duration was three times per week for four weeks for both groups. We assessed spasticity of calf muscle before and after treatment subjectively using modified Ashworth scale (MAS) and objectively via measuring H / M ratio on electromyography machine. We also assessed ankle dorsiflexion ROM objectively using two dimensions motion analysis (2D). Results: After treatment, there was a highly significant improvement in the study group compared to the control group regarding the score of MAS, no significant difference in the study group compared to the control group regarding the readings of H / M ratio, highly significant improvement in the study group compared to the control group regarding the 2D motion analysis findings. Conclusion: This modality considers effective in reducing spasticity in the calf muscle and improving ankle dorsiflexion of the affected limb.

Keywords: ankle foot orthosis, nitrogen-based cryotherapy, stroke, spasticity

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Authors: Evangelos Koumparoudis

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The definition of death is a deep philosophical question, and no single meaning can be ascribed to it. This essay focuses on the ontological, epistemological, and ethical aspects of death and dying in view of technological progress in information technology and biomedicine. It starts with the ad hoc 1968 Harvard committee that proposed that the criterion for the definition of death be irreversible coma and then refers to the debate over the whole brain death formula, emphasizing the integrated function of the organism and higher brain formula, taking consciousness and personality as essential human characteristics. It follows with the contribution of information technology in personalized and precision medicine and anti-aging measures aimed at life prolongation. It also touches on the possibility of the creation of human-machine hybrids and how this raises ontological and ethical issues that concern the “cyborgization” of human beings and the conception of the organism and personhood based on a post/transhumanist essence, and, furthermore, if sentient AI capable of autonomous decision-making that might even surpass human intelligence (singularity, superintelligence) deserves moral or legal personhood. Finally, there is the question as to whether death and dying should be redefined at a transcendent level, which is reinforced by already-existing technologies of “virtual after-” life and the possibility of uploading human minds. In the last section, I refer to the current (and future) applications of nanomedicine in diagnostics, therapeutics, implants, and tissue engineering as well as the aspiration to “immortality” by cryonics. The definition of death is reformulated since age and disease elimination may be realized, and the criterion of irreversibility may be challenged.

Keywords: death, posthumanism, infomedicine, nanomedicine, cryonics

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Authors: Sharmili Routray, Kishor Chandra Biswal

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The use of corrosion resistant fiber reinforced polymer (FRP) reinforcement is beneficial in structures particularly those exposed to deicing salts, and/or located in highly corrosive environment. Generally Glass, Carbon and Aramid fibers are used for the strengthening purpose of the structures. Due to the necessities of low weight and high strength materials, it is required to find out the suitable substitute with low cost. Recent developments in fiber production technology allow the strengthening of structures using Basalt fiber which is made from basalt rock. Basalt fiber has good range of thermal performance, high tensile strength, resistance to acids, good electro‐magnetic properties, inert nature, resistance to corrosion, radiation and UV light, vibration and impact loading. This investigation focuses on the effect of fibre content and fiber orientation of basalt fibre on mechanical properties of the fabricated composites. Specimen prepared with unidirectional Basalt fabric as reinforcing materials and epoxy resin as a matrix in polymer composite. In this investigation different fiber orientation are taken and the fabrication is done by hand lay-up process. The variation of the properties with the increasing number of plies of fiber in the composites is also studied. Specimens are subjected to tensile strength test and the failure of the composite is examined with the help of INSTRON universal testing Machine (SATEC) of 600 kN capacities. The average tensile strength and modulus of elasticity of BFRP plates are determined from the test Program.

Keywords: BFRP, fabrication, Fiber Reinforced Polymer (FRP), strengthening

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Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

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The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: Joanna Kochanska, Dagmara Gornicka, Anna Burduk

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The article examines the process of implementing a new assembly line in a manufacturing enterprise of the household appliances industry area. At the initial stages of the project, a decision was made that one of its foundations should be the concept of lean management. Because of that, eliminating as many errors as possible in the first phases of its functioning was emphasized. During the start-up of the line, there were identified and documented all production losses (from serious machine failures, through any unplanned downtime, to micro-stops and quality defects). During 6 weeks (line start-up period), all errors resulting from problems in various areas were analyzed. These areas were, among the others, production, logistics, quality, and organization. The aim of the work was to analyze the occurrence of production failures during the initial phase of starting up the line and to propose a method for determining their critical level during its full functionality. There was examined the repeatability of the production losses in various areas and at different levels at such an early stage of implementation, by using the methods of statistical process control. Based on the Pareto analysis, there were identified the weakest points in order to focus improvement actions on them. The next step was to examine the effectiveness of the actions undertaken to reduce the level of recorded losses. Based on the obtained results, there was proposed a method for determining the critical failures level in the studied areas. The developed coefficient can be used as an alarm in case of imbalance of the production, which is caused by the increased failures level in production and production support processes in the period of the standardized functioning of the line.

Keywords: production failures, level of production losses, new production line implementation, assembly line, statistical process control

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Authors: Song Zhang, Hui Wang, Johannes Henrich Schleifenbaum

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Due to the increasing demand for quality assurance and reliability for additive manufacturing, the development of an advanced in-situ monitoring system is required to monitor the process anomalies as input for further process control. Optical-based monitoring systems, such as CMOS cameras and NIR cameras, are proved as effective ways to monitor the geometrical distortion and exceptional thermal distribution. Therefore, many studies and applications are focusing on the availability of the optical-based monitoring system for detecting varied types of defects. However, the capability of the monitoring setup is not quantified. In this study, a quantification model to evaluate the capability of the monitoring setups for the LPBF machine based on acquired monitoring data of a designed test artifact is presented, while the design of the relevant test artifacts is discussed. The monitoring setup is evaluated based on its hardware properties, location of the integration, and light condition. Methodology of data processing to quantify the capacity for each aspect is discussed. The minimal capability of the detectable size of the monitoring set up in the application is estimated by quantifying its resolution and accuracy. The quantification model is validated using a CCD camera-based monitoring system for LPBF machines in the laboratory with different setups. The result shows the model to quantify the monitoring system's performance, which makes the evaluation of monitoring systems with the same concept but different setups possible for the LPBF process and provides the direction to improve the setups.

Keywords: data processing, in-situ monitoring, LPBF process, optical system, quantization model, test artifact

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Authors: Lydia Tan, Philippe Lemey, Lieselot Houspie, Marco Viveen, Darren Martin, Frank Coenjaerts

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Introduction: Human respiratory syncytial virus (hRSV) is the leading cause of severe respiratory tract infections in infants under the age of two. Genomic substitutions and related evolutionary dynamics of hRSV are of great influence on virus transmission behavior. The evolutionary patterns formed are due to a precarious interplay between the host immune response and RSV, thereby selecting the most viable and less immunogenic strains. Studying genomic profiles can teach us which genes and consequent proteins play an important role in RSV survival and transmission dynamics. Study design: In this study, genetic diversity and evolutionary rate analysis were conducted on 36 RSV subgroup B whole genome sequences and 37 subgroup A genome sequences. Clinical RSV isolates were obtained from nasopharyngeal aspirates and swabs of children between 2 weeks and 5 years old of age. These strains, collected during epidemic seasons from 2001 to 2011 in the Netherlands and Belgium by either conventional or 454-sequencing. Sequences were analyzed for genetic diversity, recombination events, synonymous/non-synonymous substitution ratios, epistasis, and translational consequences of mutations were mapped to known 3D protein structures. We used Bayesian statistical inference to estimate the rate of RSV genome evolution and the rate of variability across the genome. Results: The A and B profiles were described in detail and compared to each other. Overall, the majority of the whole RSV genome is highly conserved among all strains. The attachment protein G was the most variable protein and its gene had, similar to the non-coding regions in RSV, more elevated (two-fold) substitution rates than other genes. In addition, the G gene has been identified as the major target for diversifying selection. Overall, less gene and protein variability was found within RSV-B compared to RSV-A and most protein variation between the subgroups was found in the F, G, SH and M2-2 proteins. For the F protein mutations and correlated amino acid changes are largely located in the F2 ligand-binding domain. The small hydrophobic phosphoprotein and nucleoprotein are the most conserved proteins. The evolutionary rates were similar in both subgroups (A: 6.47E-04, B: 7.76E-04 substitution/site/yr), but estimates of the time to the most recent common ancestor were much lower for RSV-B (B: 19, A: 46.8 yrs), indicating that there is more turnover in this subgroup. Conclusion: This study provides a detailed description of whole RSV genome mutations, the effect on translation products and the first estimate of the RSV genome evolution tempo. The immunogenic G protein seems to require high substitution rates in order to select less immunogenic strains and other conserved proteins are most likely essential to preserve RSV viability. The resulting G gene variability makes its protein a less interesting target for RSV intervention methods. The more conserved RSV F protein with less antigenic epitope shedding is, therefore, more suitable for developing therapeutic strategies or vaccines.

Keywords: drug target selection, epidemiology, respiratory syncytial virus, RSV

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Authors: P. Dey, S. K. Pal

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Ceramics or glass ceramics with the property of bone bonding at the nearby tissues and producing possible bone in growth are known to be bioactive. The most extensively used glass in this context is 45S5 which is a silica based bioglass mostly explored in the field of tissue engineering as scaffolds for bone repair. Nowadays, the borate based bioglass are being utilized in orthopedic area largely due to its superior bioactivity with the formation of bone bonding. An attempt has been made, in the present study, to observe the effect of B2O3 addition in 45S5 glass and perceive its consequences on the thermal, mechanical and biological properties. The B2O3 was added in 1, 2.5, and 5 wt% with simultaneous reduction in the silica content of the 45S5 composition. The borate based bioglass has been synthesized by the means of sol-gel route. The synthesized powders were then thermally analyzed by DSC-TG. The as synthesized powders were then calcined at 600ºC for 2hrs. The calcined powders were then pressed into pellets followed by sintering at 850ºC with a holding time of 2hrs. The phase analysis and the microstructural analysis of the as synthesized and calcined powder glass samples and the sintered glass samples were being carried out using XRD and FESEM respectively. The formation of hydroxyapatite layer was performed by immersing the sintered samples in the simulated body fluid (SBF) and mechanical property has been tested for the sintered samples by universal testing machine (UTM). The sintered samples showed the presence of sodium calcium silicate phase while the formation of hydroxyapaptite takes place for SBF immersed samples. The formation of hydroxyapatite is more pronounced in case of borated based glass samples instead of 45S5.

Keywords: 45S5 bioglass, bioactive, borate, hydroxyapatite, sol-gel synthesis

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Authors: Evan W. Krieger, Vijayan K. Asari, Saibabu Arigela

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Video imagery captured for real-time security and surveillance applications is typically captured in complex lighting conditions. These less than ideal conditions can result in imagery that can have underexposed or overexposed regions. It is also typical that the video is too low in resolution for certain applications. The purpose of security and surveillance video is that we should be able to make accurate conclusions based on the images seen in the video. Therefore, if poor lighting and low resolution conditions occur in the captured video, the ability to make accurate conclusions based on the received information will be reduced. We propose a solution to this problem by using image preprocessing to improve these images before use in a particular application. The proposed algorithm will integrate an intensity enhancement algorithm with a super resolution technique. The intensity enhancement portion consists of a nonlinear inverse sign transformation and an adaptive contrast enhancement. The super resolution section is a single image super resolution technique is a Fourier phase feature based method that uses a machine learning approach with kernel regression. The proposed technique intelligently integrates these algorithms to be able to produce a high quality output while also being more efficient than the sequential use of these algorithms. This integration is accomplished by performing the proposed algorithm on the intensity image produced from the original color image. After enhancement and super resolution, a color restoration technique is employed to obtain an improved visibility color image.

Keywords: dynamic range compression, multi-level Fourier features, nonlinear enhancement, super resolution

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Authors: Hamil Uribe, Cristian Arancibia

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In Chile, the water for irrigation and hydropower generation is delivery essentially through unlined channels on earth, which have high seepage losses. Traditional seepage-abatement technologies are very expensive. The goals of this work were to quantify water loss in unlined channels and select reaches to evaluate the use of linear anionic polyacrylamide (LA-PAM) to reduce seepage losses. The study was carried out in Maule Region, central area of Chile. Water users indicated reaches with potential seepage losses, 45 km of channels in total, whose flow varied between 1.07 and 23.6 m³ s⁻¹. According to seepage measurements, 4 reaches of channels, 4.5 km in total, were selected for LA-PAM application. One to 4 LA-PAM applications were performed at rates of 11 kg ha⁻¹, considering wet perimeter area as basis of calculation. Large channels were used to allow motorboat moving against the current to carry-out LA-PAM application. For applications, a seeder machine was used to evenly distribute granulated polymer on water surface. Water flow was measured (StreamPro ADCP) upstream and downstream in selected reaches, to estimate seepage losses before and after LA-PAM application. Weekly measurements were made to quantify treatment effect and duration. In each case, water turbidity and temperature were measured. Channels showed variable losses up to 13.5%. Channels showing water gains were not treated with PAM. In all cases, LA-PAM effect was positive, achieving average loss reductions of 8% to 3.1%. Water loss was confirmed and it was possible to reduce seepage through LA-PAM applications provided that losses were known and correctly determined when applying the polymer. This could allow increasing irrigation security in critical periods, especially under drought conditions.

Keywords: canal seepage, irrigation, polyacrylamide, water management

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Authors: M A. Habit, S. A. Syed-Sahil, A. Bahari

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Abstract—Laser cutting is a method of manufacturing process that uses laser in order to cut materials. It provides and ensures extreme accuracy which has a clean cut effect, CO2 laser dominate this application due to their good- quality beam combined with high output power. It comes with a small scale and it has a limitation in cutting sizes of materials, therefore it is more appropriate for custom- made products. The same laser cutting machine is also capable in cutting fine material such as fine silk, cotton, leather, polyester, etc. Lack of explorations and knowledge besides being unaware about this technology had caused many of the designers not to use this laser cutting method in their collections. The objectives of this study are: 1) To identify the potential of laser cutting technique in Custom-Made Garments for men’s casual wear: 2) To experiment the laser cutting technique in custom made garments: 3) To offer guidelines and formula for men’s custom- made casualwear designs with aesthetic value. In order to achieve the objectives, this research has been conducted by using mixed methods which are interviews with two (2) local experts in the apparel manufacturing industries and interviews via telephone with five (5) local respondents who are local emerging fashion designers, the questionnaires were distributed to one hundred (100) respondents around Klang Valley, in order to gain the information about their understanding and awareness regarding laser cutting technology. The experiment was conducted by using natural and man- made fibers. As a conclusion, all of the objectives had been achieved in producing custom-made men’s casualwear and with the production of these attires it will help to educate and enhance the innovation in fine technology. Therefore, there will be a good linkage and collaboration between the design experts and the manufacturing companies.

Keywords: custom-made, fashion, laser cut, men’s wear

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Authors: Silvia Arrate, Waldo Salud, Eloy París

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The wear of cutting tools is one of the most decisive elements when planning tunneling works, programming the maintenance stops and saving the optimum stock of spare parts during the evolution of the excavation. Being able to predict the behavior of cutting tools can give a very competitive advantage in terms of costs and excavation performance, optimized to the needs of the TBM itself. The incredible evolution of data science in recent years gives the option to implement it at the time of analyzing the key and most critical parameters related to machinery with the purpose of knowing how the cutting head is performing in front of the excavated ground. Taking this as a case study, Metro Line 3 of Guadalajara in Mexico will develop the feasibility of using Specific Energy versus data science applied over parameters of Torque, Penetration, and Contact Force, among others, to predict the behavior and status of cutting tools. The results obtained through both techniques are analyzed and verified in the function of the wear and the field situations observed in the excavation in order to determine its effectiveness regarding its predictive capacity. In conclusion, the possibilities and improvements offered by the application of digital tools and the programming of calculation algorithms for the analysis of wear of cutting head elements compared to purely empirical methods allow early detection of possible damage to cutting tools, which is reflected in optimization of excavation performance and a significant improvement in costs and deadlines.

Keywords: cutting tools, data science, prediction, TBM, wear

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Authors: Charles Koduru, Parth Patel, M. Hassan Tanveer

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In this paper, the communication between a team of robots is used to sanitize an environment with germs is proposed. We introduce capabilities from a team of robots (most likely heterogeneous), a wheeled robot named ROSbot 2.0 that consists of a mounted LiDAR and Kinect sensor, and a modified prototype design of a unicycle-drive Roomba robot called the UV robot. The UV robot consists of ultrasonic sensors to avoid obstacles and is equipped with an ultraviolet light system to disinfect and kill germs, such as bacteria and viruses. In addition, the UV robot is equipped with disinfectant spray to target hidden objects that ultraviolet light is unable to reach. Using the sensors from the ROSbot 2.0, the robot will create a 3-D model of the environment which will be used to factor how the ultraviolet robot will disinfect the environment. Together this proposed system is known as the RME assistive robot device or RME system, which communicates between a navigation robot and a germ disinfecting robot operated by a user. The RME system includes a human-machine interface that allows the user to control certain features of each robot in the RME assistive robot device. This method allows the cleaning process to be done at a more rapid and efficient pace as the UV robot disinfects areas just by moving around in the environment while using the ultraviolet light system to kills germs. The RME system can be used in many applications including, public offices, stores, airports, hospitals, and schools. The RME system will be beneficial even after the COVID-19 pandemic. The Kennesaw State University will continue the research in the field of robotics, engineering, and technology and play its role to serve humanity.

Keywords: multi robot system, assistive robots, COVID-19 pandemic, ultraviolent technology

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Authors: Zahra Ahmadi, Fazlollah Adibnia

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Scheduling of tasks and the optimal allocation of resources in the cloud are based on the dynamic nature of tasks and the heterogeneity of resources. Applications that are based on the scientific workflow are among the most widely used applications in this field, which are characterized by high processing power and storage capacity. In order to increase their efficiency, it is necessary to plan the tasks properly and select the best virtual machine in the cloud. The goals of the system are effective factors in scheduling tasks and resource selection, which depend on various criteria such as time, cost, current workload and processing power. Multi-criteria decision-making methods are a good choice in this field. In this research, a new method of work planning and resource allocation in a heterogeneous environment based on the modified AHP algorithm is proposed. In this method, the scheduling of input tasks is based on two criteria of execution time and size. Resource allocation is also a combination of the AHP algorithm and the first-input method of the first client. Resource prioritization is done with the criteria of main memory size, processor speed and bandwidth. What is considered in this system to modify the AHP algorithm Linear Max-Min and Linear Max normalization methods are the best choice for the mentioned algorithm, which have a great impact on the ranking. The simulation results show a decrease in the average response time, return time and execution time of input tasks in the proposed method compared to similar methods (basic methods).

Keywords: hierarchical analytical process, work prioritization, normalization, heterogeneous resource allocation, scientific workflow

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Authors: Mark-Adam Kellerman

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Proteins are capable of exploring vast mutational spaces. This makes it difficult for protein engineers to devise rational methods to improve stability and function via mutagenesis. Deciding which residues to mutate requires knowledge of the characteristics they elicit. We probed the characteristics of residues in granulocyte-colony stimulating factor (G-CSF) using a thermal melt (from 295K to 323K) to denature it in a 700 MHz Bruker spectrometer. These characteristics included dynamics, micro-environmental changes experienced/ induced during denaturing and structure-function relationships. 15N-1H HSQC experiments were performed at 2K increments along with this thermal melt. We observed that dynamic residues that also undergo a lot of change in their microenvironment were predominantly in unstructured regions. Moreover, we were able to identify four residues (G4, A6, T133 and Q134) that we class as high priority targets for mutagenesis, given that they all appear in both the top 10% of measures for environmental changes and dynamics (∑Δ and ∆PI). We were also able to probe these NMR observables and combine them with molecular dynamics (MD) to elucidate what appears to be an opening motion of G-CSFs binding site III. V48 appears to be pivotal to this opening motion, which also seemingly distorts the loop region between helices A and B. This observation is in agreement with previous findings that the conformation of this loop region becomes altered in an aggregation-prone state of G-CSF. Hence, we present here an approach to profile the characteristics of residues in order to highlight their potential as rational mutagenesis targets and their roles in important conformational changes. These findings present not only an opportunity to effectively make biobetters, but also open up the possibility to further understand epistasis and machine learn residue behaviours.

Keywords: protein engineering, rational mutagenesis, NMR, molecular dynamics

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