Abstract: The average American worker sits for thirteen hours a day, often with poor posture and infrequent breaks, which can lead to health issues and back problems. The Smart Cushion was created to alert individuals of their poor postures, and may potentially alleviate back problems and correct poor posture. The Smart Cushion is a portable, rectangular, foam cushion, with five strategically placed pressure sensors, that utilizes an Arduino Uno circuit board and specifically designed software, allowing it to collect data from the five pressure sensors and store the data on an SD card. The data is then compiled into graphs and compared to controlled postures. Before volunteers sat on the cushion, their levels of back pain were recorded on a scale from 1-10. Data was recorded for an hour during sitting, and then a new, corrected posture was suggested. After using the suggested posture for an hour, the volunteers described their level of discomfort on a scale from 1-10. Different patterns of sitting postures were generated that were able to serve as early warnings of potential back problems. By using the Smart Cushion, the areas where different volunteers were applying the most pressure while sitting could be identified, and the sitting postures could be corrected. Further studies regarding the relationships between posture and specific regions of the body are necessary to better understand the origins of back pain; however, the Smart Cushion is sufficient for correcting sitting posture and preventing the development of additional back pain.
Abstract: As of 2017, many online learning professionals, institutions, and journals are still wondering how instructors can keep student engaged in the online learning environment to facilitate active learning effectively. The purpose of this qualitative single-case and narrative research is to explore whether online professors understand their role as mentors and facilitators of students’ academic success by keeping students engaged in active learning based on personalized experience in the field. Data collection tools that were used in the study included an NVivo 12 Plus qualitative software, an interview protocol, a digital audiotape, an observation sheet, and a transcription. Seven online professors in the United States from LinkedIn and residencies were interviewed for this study. Eleven online teaching techniques from previous research were used as the study framework. Data analysis process, member checking, and key themes were used to achieve saturation. About 85.7% of professors agreed on rubric as the preferred online grading technique. About 57.1% agreed on professors logging in daily, students logging in about 2-5 times weekly, knowing students to increase accountability, email as preferred communication tool, and computer access for adequate online learning. About 42.9% agreed on syllabus for clear class expectations, participation to show what has been learned, and energizing students for creativity.
Abstract: This paper describes a system to optimize the use of the internet by clients requiring downloading of videos at peak hours. The system consists of a web server belonging to a provider of video contents, a provider of internet communications and a software application running on a client’s computer. The client using the application software will communicate to the video provider a list of the client’s future video demands. The video provider calculates which videos are going to be more in demand for download in the immediate future, and proceeds to request the internet provider the most optimal hours to do the downloading. The times of the downloading will be sent to the application software, which will use the information of pre-established hours negotiated between the video provider and the internet provider to download those videos. The videos will be saved in a special protected section of the user’s hard disk, which will only be accessed by the application software in the client’s computer. When the client is ready to see a video, the application will search the list of current existent videos in the area of the hard disk; if it does exist, it will use this video directly without the need for internet access. We found that the best way to optimize the download traffic of videos is by negotiation between the internet communication provider and the video content provider.
Abstract: Aloe Vera is a short-stemmed succulent plant which is commonly used in Myanmar traditional medicine. A. vera gel was also used as food addictive. This study aims to improve the Myanmar folk medicine to a functional beverage. In this research, Aloe vera was fermented with Saccharomyces cerevisiae for 6 months. Three different processes were carried out. Process I contains A. vera 10%, sugar 30%, water 50%, and starter culture 10%, process II contains A. vera 10%, sugar 15%, honey 15%, and water 50%, starter culture 10%; process III contains A. vera 10%, honey 30%, water 50%, starter culture 10%. During wine fermentation, the wine parameters such as alcohol content, total soluble solid (ºBrix), pH, color and cell population were analyzed. After 30 days of fermentation, total cell population remained 2.8x106 in P-I, P-II and 3.2x106 in P-III. Total soluble solid content dropped to 15.8 in P-I, P-II and 15.7 in P-III. After 30 days, clear wine was transferred to other vassals for racking. After 6 months of racking, microbial population reached under detectable level and alcohol content was round about 11% but not significantly different among these processes. P-II was found to have the highest color intensity at 450 nm and it got the most taster satisfaction when sensory evaluation was carried out using five hedonic scales after 6 month of racking.
Abstract: This paper presents the development of a low-cost monitoring system for a hydroponic urban vertical farm, enabling its automation and a quantitative assessment of the farm performance. Urban farming has seen increasing interest in the last decade thanks to the development of energy efficient and affordable LED lights; however, the optimal configuration of such systems (i.e. amount of nutrients, light-on time, ambient temperature etc.) is mostly based on the farmers’ experience and empirical guidelines. Moreover, even if simple, the maintenance of such systems is labor intensive as it requires water to be topped-up periodically, mixing of the nutrients etc. To unlock the full potential of urban farming, a quantitative understanding of the role that each variable plays in the growth of the plants is needed, together with a higher degree of automation. The low-cost monitoring system proposed in this paper is a step toward filling this knowledge and technological gap, as it enables collection of sensor data related to water and air temperature, water level, humidity, pressure, light intensity, pH and electric conductivity without requiring any human intervention. More sensors and actuators can also easily be added thanks to the modular design of the proposed platform. Data can be accessed remotely via a simple web interface. The proposed platform can be used both for quantitatively optimizing the setup of the farms and for automating some of the most labor-intensive maintenance activities. Moreover, such monitoring system can also potentially be used for high-level decision making, once enough data are collected.
Abstract: A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.
Abstract: Agile methodology is the current most popular technique in software development projects. Agile methods in software development bring optimistic impact on software performances, quality and customer satisfaction. There are some organizations and small-medium enterprises adopting agile into their local software development projects as well as in distributed software development projects. Adopting agile methods in local software development projects is valuable. However, agile global software deployment needs an attention. There are different key challenges in agile global software development that need to resolve and enhance the global software development cycles. The proposed systematic literature review investigates all key challenges of agile in global software development. Moreover, a quantitative methodology (an actual survey) targeted to present a real case scenario of these particular key challenges faced by one of the software houses that is BestWeb Malaysia. The outcomes of systematic literature and the results of quantitative methodology are compared with each other to evaluate if the key challenges pointed out in systematic review still exist. The proposed research and its exploratory results can assist small medium enterprises to avoid these challenges by adopting the best practices in their global software development projects. Moreover, it is helpful for novice researchers to get valuable information altogether.
Abstract: This work focuses on the symmetric alpha stable process with continuous time frequently used in modeling the signal with indefinitely growing variance, often observed with an unknown additive error. The objective of this paper is to estimate this error from discrete observations of the signal. For that, we propose a method based on the smoothing of the observations via Jackson polynomial kernel and taking into account the width of the interval where the spectral density is non-zero. This technique allows avoiding the “Aliasing phenomenon” encountered when the estimation is made from the discrete observations of a process with continuous time. We have studied the convergence rate of the estimator and have shown that the convergence rate improves in the case where the spectral density is zero at the origin. Thus, we set up an estimator of the additive error that can be subtracted for approaching the original signal without error.
Abstract: The large pose discrepancy is one of the critical
challenges in face recognition during video surveillance. Due to
the entanglement of pose attributes with identity information, the
conventional approaches for pose-independent representation lack
in providing quality results in recognizing largely posed faces. In
this paper, we propose a practical approach to disentangle the pose
attribute from the identity information followed by synthesis of a face
using a classifier network in latent space. The proposed approach
employs a modified generative adversarial network framework
consisting of an encoder-decoder structure embedded with a classifier
in manifold space for carrying out factorization on the latent
encoding. It can be further generalized to other face and non-face
attributes for real-life video frames containing faces with significant
attribute variations. Experimental results and comparison with state
of the art in the field prove that the learned representation of the
proposed approach synthesizes more compelling perceptual images
through a combination of adversarial and classification losses.
Abstract: In the last decade, a new industrial revolution seems to be emerging, supported -once again- by the rapid advancements of Information Technology in the areas of Machine-to-Machine (M2M) communication permitting large numbers of intelligent devices, e.g. sensors to communicate with each other and take decisions without any or minimum indirect human intervention. The advent of these technologies have triggered the emergence of a new category of hybrid (cyber-physical) manufacturing systems, combining advanced manufacturing techniques with innovative M2M applications based on the Internet of Things (IoT), under the umbrella term Industry 4.0. Even though the topic of Industry 4.0 has attracted much attention during the last few years, the attempts of providing a systematic literature review of the subject are scarce. In this paper, we present the authors’ initial study of the field with a special focus on the use and applications of Industry 4.0 principles in material handling automations and in-house logistics. Research shows that despite the vivid discussion and attractiveness of the subject, there are still many challenges and issues that have to be addressed before Industry 4.0 becomes standardized and widely applicable.
Abstract: In this study, a cross-layer design which combines
adaptive modulation and coding (AMC) and hybrid automatic repeat
request (HARQ) techniques for a cooperative wireless network is
investigated analytically. Previous analyses of such systems in the
literature are confined to the case where the fading channel is
independent at each retransmission, which can be unrealistic unless
the channel is varying very fast. On the other hand, temporal channel
correlation can have a significant impact on the performance of
HARQ systems. In this study, utilizing a Markov channel model
which accounts for the temporal correlation, the performance of
non-cooperative and cooperative networks are investigated in terms of
packet loss rate and throughput metrics for Chase combining HARQ
strategy.
Abstract: The construction industry and housing subsector are fraught with risks that have the potential of negatively impacting on the achievement of project objectives. The success or otherwise of most construction projects depends to large extent on how well these risks have been managed. The recent paradigm shift by the subsector to use of formal risk management approach in contrast to hitherto developed rules of thumb means that risks must not only be identified but also properly assessed and responded to in a systematic manner. The study focused on identifying risks associated with housing development projects and prioritisation assessment of the identified risks in order to provide basis for informed decision. The study used a three-step identification framework: review of literature for similar projects, expert consultation and questionnaire based survey to identify potential risk factors. Delphi survey method was employed in carrying out the relative prioritization assessment of the risks factors using computer-based Analytical Hierarchical Process (AHP) software. The results show that 19 out of the 50 risks significantly impact on housing development projects. The study concludes that although significant numbers of risk factors have been identified as having relevance and impacting to housing construction projects, economic risk group and, in particular, ‘changes in demand for houses’ is prioritised by most developers as posing a threat to the achievement of their housing development objectives. Unless these risks are carefully managed, their effects will continue to impede success in these projects. The study recommends the adoption and use of the combination of multi-technique identification framework and AHP prioritization assessment methodology as a suitable model for the assessment of risks in housing development projects.
Abstract: Building system is highly vulnerable to different kinds
of faults and human misbehaviors. Energy efficiency and user comfort
are directly targeted due to abnormalities in building operation. The
available fault diagnosis tools and methodologies particularly rely on
rules or pure model-based approaches. It is assumed that model or
rule-based test could be applied to any situation without taking into
account actual testing contexts. Contextual tests with validity domain
could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when
validate the test model considering the non-modeled events such
as occupancy, weather conditions, door and window openings and
the integration of the knowledge of the expert on the state of the
system. The concept of heterogeneous tests is combined with test
validity to generate fault diagnoses. A combination of rules, range
and model-based tests known as heterogeneous tests are proposed
to reduce the modeling complexity. Calculation of logical diagnoses
coming from artificial intelligence provides a global explanation
consistent with the test result. An application example shows the efficiency of the proposed
technique: an office setting at Grenoble Institute of Technology.
Abstract: This study developed a method for teaching
acupuncture manipulation in small groups optimized with the
theory of motor learning. Sixty acupuncture students and their
teacher participated in our research. Motion videos were recorded
of their manipulations using the lifting-thrusting method. These
videos were analyzed using Simi Motion software to acquire the
movement parameters of the thumb tip. The parameter velocity
curves along Y axis was used to generate small teaching groups
clustered by a self-organized map (SOM) and K-means. Ten groups
were generated. All the targeted instruction based on the comparative
results groups as well as the videos of teacher and student was
provided to the members of each group respectively. According to
the theory and research of motor learning, the factors or technologies
such as video instruction, observational learning, external focus and
summary feedback were integrated into this teaching method. Such
efforts were desired to improve and enhance the effectiveness of
current acupuncture teaching methods in limited classroom teaching
time and extracurricular training.
Abstract: This paper presents the review of the studies on the ergonomics, virtual reality, and work measurement (time study) at the industrial workstations because each of these three individual techniques can be used to improve the design of workstations and task position. The objective of this paper is to give an overall literature review that if there is any relation between these three different techniques. Therefore, it is so important to review the scientific studies to find a better and effective way for improving design of workstations. On the other hand, manufacturers found that instead of using one of the approaches, utilizing the combination of these individual techniques are more effective to reduce the cost and production time.
Abstract: This study examines the levels of dopamine and serotonin in blood samples of children who stutter compared with normal fluent speakers. Blood specimens from 50 children who stutter (6 females, 44 males) and 50 normal children matched age and gender were collected for the purpose of the current study. The concentrations of dopamine and serotonin were measured using the 1100 series high-performance liquid chromatography coupled with ultraviolet detector instrument (HPLC-UV). It was revealed that dopamine level in the blood samples of stuttering group and fluent group was not significant (P = 0.769), whereas the level of serotonin was significantly higher in the blood samples of stuttering group than the blood samples of fluent normal group (P = 0.015). It is concluded that serotonin blockers could be used in future studies to evaluate its role as a medication for the treatment of stuttering.
Abstract: Data assets protection is a crucial issue in the
cybersecurity field. Companies use logical access control tools to
vault their information assets and protect them against external
threats, but they lack solutions to counter insider threats. Nowadays,
insider threats are the most significant concern of security analysts.
They are mainly individuals with legitimate access to companies
information systems, which use their rights with malicious intents.
In several fields, behavior anomaly detection is the method used by
cyber specialists to counter the threats of user malicious activities
effectively. In this paper, we present the step toward the construction
of a user and entity behavior analysis framework by proposing a
behavior anomaly detection model. This model combines machine
learning classification techniques and graph-based methods, relying
on linear algebra and parallel computing techniques. We show the
utility of an ensemble learning approach in this context. We present
some detection methods tests results on an representative access
control dataset. The use of some explored classifiers gives results
up to 99% of accuracy.
Abstract: This research involved the utilization of fluid flow energy to predict power output using Lead Zirconate Titanate (PZT) piezoelectric stacks. The aim of this work is to extract energy from a controlled level of pressure fluctuation in single-phase flow which forms a part of the energy harvesting technology that powers flow meters. A device- Perspex box was developed and fixed to 50.8 mm rig to induce pressure fluctuation in the flow. An experimental test was carried out using the single-phase water flow in the developed rig in order to measure the power output generation from the piezoelectric stacks. 16 sets of experimental tests were conducted to ensure the maximum output result. The acquired signal of the pressure fluctuation was used to simulate the expected electrical output from the piezoelectric material. The results showed a maximum output voltage of 12 V with an instantaneous output power of 1 µW generated, when the pressure amplitude is 2.6 kPa at a frequency of 2.4 Hz.
Abstract: The paper declares effects of exercise intervention of the research project “Basic research of balance changes in seniors”, granted by the Czech Science Foundation. The objective of the presented study is to define predictors, which influence bio-psycho-social consequences and effects of balance ability in senior 65 years old and above. We focused on the Fall-Efficacy Scale changes evaluation in seniors. Comprehensive hypothesis of the project declares, that motion uncertainty (dyskinesia) can negatively affect the well-being of a senior in bio-psycho-social context. In total, random selection and testing of 100 seniors (30 males, 70 females) from Prague and Central Bohemian region was provided. The sample was divided by stratified random selection into experimental and control groups, who underwent input and output testing. For diagnostics the methods of Medical Anamnesis, Functional anthropological examinations, Tinetti Balance Assessment Tool, SF-36 Health Survey, Anamnestic comparative self-assessment scale were used. Intervention method called "Life in Balance" based on yoga techniques was applied in four-week cycle. Results of multivariate regression were verified by repeated measures ANOVA: subject factor, phase of intervention (between-subject factor), body fluid (within-subject factor) and phase of intervention × body fluid interaction). ANOVA was performed with a repetition involving the factors of subjects, experimental/control group, phase of intervention (independent variable), and x phase interaction followed by Bonferroni multiple comparison assays with a test strength of at least 0.8 on the probability level p < 0.05. In the paper results of the first-year investigation of the three years running project are analysed. Results of balance tests confirmed no significant difference between females and males in pre-test. Significant improvements in balance and walking ability were observed in experimental group in females comparing to males (F = 128.4, p < 0.001). In the females control group, there was no significant change in post- test, while in the female experimental group positive changes in posture and spine flexibility in post-tests were found. It seems that females even in senior age react better to incentives of intervention in balance and spine flexibility. On the base of results analyses, we can declare the significant improvement in social balance markers after intervention in the experimental group (F = 10.5, p < 0.001). In average, seniors are used to take four drugs daily. Number of drugs can contribute to allergy symptoms and balance problems. It can be concluded that static balance and walking ability of seniors according Tinetti Balance scale correlate significantly with psychic and social monitored markers.
Abstract: The Canadian Nuclear Power Plants (NPPs) use some portions of NUREG/CR-6850 in carrying out Fire Probabilistic Risk Assessment (PRA). An assessment for the applicability of NUREG/CR-6850 to CANDU reactors was performed and a CANDU Fire PRA was introduced. There are 19 operating CANDU reactors in Canada at five sites (Bruce A, Bruce B, Darlington, Pickering and Point Lepreau). A fire load density survey was done for all Fire Safe Shutdown Analysis (FSSA) fire zones in all CANDU sites in Canada. National Fire Protection Association (NFPA) Standard 557 proposes that a fire load survey must be conducted by either the weighing method or the inventory method or a combination of both. The combination method results in the most accurate values for fire loads. An updated CANDU Fire PRA model is demonstrated in this paper that includes the fuel survey in all Canadian CANDU stations. A qualitative screening step for the CANDU fire PRA is illustrated in this paper to include any fire events that can damage any part of the emergency power supply in addition to FSSA cables.