Search results for: platform video monitoring

Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

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In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

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Authors: Elmabruk Laias

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IEEE Standard 802.16 provides QoS (Quality of Service) for the applications such as Voice over IP, video streaming and high bandwidth file transfer. With the ability of broadband wireless access of an IEEE 802.16 system, a WiMAX TDD frame contains one downlink subframe and one uplink subframe. The capacity allocated to each subframe is a system parameter that should be determined based on the expected traffic conditions. a proper resource allocation scheme for packet transmissions is imperatively needed. In this paper, we present a new resource allocation scheme, called additional bandwidth yielding (ABY), to improve transmission efficiency of an IEEE 802.16-based network. Our proposed scheme can be adopted along with the existing scheduling algorithms and the multi-priority scheme without any change. The experimental results show that by using our ABY, the packet queuing delay could be significantly improved, especially for the service flows of higher-priority classes.

Keywords: IEEE 802.16, WiMAX, OFDMA, resource allocation, uplink-downlink mapping

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Authors: Anne-Margré C. Vink

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Background: Food-related allergies and intolerances are frequently occurring in dogs. Diagnosis and monitoring according to ‘Golden Standard’ of elimination efficiency are time-consuming, expensive, and requires expert clinical setting. In order to facilitate rapid and robust, quantitative testing of intolerance, and determining the individual offending foods, a serological test is implicated. Method: As we developed Medisynx IgG Human Screening Test ELISA before and the dog’s immune system is most similar to humans, we were able to develop Medisynx IgG Dog Screening Test ELISA as well. In this study, 47 dogs suffering from Canine Atopic Dermatitis (CAD) and several secondary induced reactions were included to participate in serological Medisynx IgG Dog Screening Test ELISA (within < 0,02 % SD). Results were expressed as titers relative to the standard OD readings to diagnose alimentary induced diseases and monitoring the efficacy of an individual eliminating diet in dogs. Split sample analysis was performed by independently sending 2 times 3 ml serum under two unique codes. Results: The veterinarian monitored these dogs to check dog’ results at least at 3, 7, 21, 49, 70 days and after period of 6 and 12 months on an individual negative diet and a positive challenge (retrospectively) at 6 months. Data of each dog were recorded in a screening form and reported that a complete recovery of all clinical manifestations was observed at or less than 70 days (between 50 and 70 days) in the majority of dogs(44 out of 47 dogs =93.6%). Conclusion: Challenge results showed a significant result of 100% in specificity as well as 100% positive predicted value. On the other hand, sensitivity was 95,7% and negative predictive value was 95,7%. In conclusion, an individual diet based on IgG ELISA in dogs provides a significant improvement of atopic dermatitis and pruritus including all other non-specific defined allergic skin reactions as erythema, itching, biting and gnawing at toes, as well as to several secondary manifestations like chronic diarrhoea, chronic constipation, otitis media, obesity, laziness or inactive behaviour, pain and muscular stiffness causing a movement disorders, excessive lacrimation, hyper behaviour, nervous behaviour and not possible to stay alone at home, anxiety, biting and aggressive behaviour and disobedience behaviour. Furthermore, we conclude that a relatively more severe systemic candidiasis, as shown by relatively higher titer (class 3 and 4 IgG reactions to Candida albicans), influence the duration of recovery from clinical manifestations in affected dogs. These findings are consistent with our preliminary human clinical studies.

Keywords: allergy, canine atopic dermatitis, CAD, food allergens, IgG-ELISA, food-incompatibility

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Authors: Tawfig Alghbaili

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LP3D Field is located near the center of the Sirt Basin in the Marada Trough approximately 215 km south Marsa Al Braga City. The Marada Trough is bounded on the west by a major fault, which forms the edge of the Beda Platform, while on the east, a bounding fault marks the edge of the Zelten Platform. The main reservoir in the LP3D Field is Upper Paleocene Beda Formation. The Beda Formation is mainly limestone interbedded with shale. The reservoir average thickness is 117.5 feet. To develop a better understanding of the characterization and distribution of the Beda reservoir, quantitative seismic data interpretation has been done, and also, well logs data were analyzed. Six reflectors corresponding to the tops of the Beda, Hagfa Shale, Gir, Kheir Shale, Khalifa Shale, and Zelten Formations were picked and mapped. Special work was done on fault interpretation part because of the complexities of the faults at the structure area. Different attribute analyses were done to build up more understanding of structures lateral extension and to view a clear image of the fault blocks. Time to depth conversion was computed using velocity modeling generated from check shot and sonic data. The simplified stratigraphic cross-section was drawn through the wells A1, A2, A3, and A4-LP3D. The distribution and the thickness variations of the Beda reservoir along the study area had been demonstrating. Petrophysical analysis of wireline logging also was done and Cross plots of some petrophysical parameters are generated to evaluate the lithology of reservoir interval. Structure and Stratigraphic Framework was designed and run to generate different model like faults, facies, and petrophysical models and calculate the reservoir volumetric. This study concluded that the depth structure map of the Beda formation shows the main structure in the area of study, which is north to south faulted anticline. Based on the Beda reservoir models, volumetric for the base case has been calculated and it has STOIIP of 41MMSTB and Recoverable oil of 10MMSTB. Seismic attributes confirm the structure trend and build a better understanding of the fault system in the area.

Keywords: LP3D Field, Beda Formation, reservoir models, Seismic attributes

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Authors: Michael McCann

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Managerial theories propose that, in joint stock companies, executives may be tempted to waste excess free cash on unprofitable projects to keep control of resources. In order to conceal their projects' poor performance, they may seek to engage in earnings management. On the one hand, managers may manipulate earnings upwards in order to post ‘good’ performances and safeguard their position. On the other, since managers pursuit of unrewarding investments are likely to lead to low long-term profitability, managers will use negative accruals to reduce current year’s earnings, smoothing earnings over time in order to conceal the negative effects. Agency models argue that boards of directors are delegated by shareholders to ensure that companies are governed properly. Part of that responsibility is ensuring the reliability of financial information. Analyses of the impact of board characteristics, particularly board independence on the misuse of free cash flow and earnings management finds conflicting evidence. However, existing characterizations of board independence do not account for such directors gaining firm-specific knowledge over time, influencing their monitoring ability. Further, there is little analysis of the influence of the relative experience of independent directors and executives on decisions surrounding the use of free cash. This paper contributes to this literature regarding the heterogeneous characteristics of boards by investigating the influence of independent director tenure on earnings management and the relative tenures of independent directors and Chief Executives. A balanced panel dataset comprising 51 companies across 11 annual periods from 2005 to 2015 is used for the analysis. In each annual period, firms were classified as conducting earnings management if they had discretionary accruals in the bottom quartile (downwards) and top quartile (upwards) of the distributed values for the sample. Logistical regressions were conducted to determine the marginal impact of independent board tenure and a number of control variables on the probability of conducting earnings management. The findings indicate that both absolute and relative measures of board independence and experience do not have a significant impact on the likelihood of earnings management. It is the level of free cash flow which is the major influence on the probability of earnings management. Higher free cash flow increases the probability of earnings management significantly. The research also investigates whether board monitoring of earnings management is contingent on the level of free cash flow. However, the results suggest that board monitoring is not amplified when free cash flow is higher. This suggests that the extent of earnings management in companies is determined by a range of company, industry and situation-specific factors.

Keywords: corporate governance, boards of directors, agency theory, earnings management

Procedia PDF Downloads 211

Authors: Jacques Cuenca, Claudio Colangeli, Agnieszka Mroz, Karl Janssens, Gunther Riexinger, Antonio D'Antuono, Giuseppe Pandarese, Milena Martarelli, Gian Marco Revel, Carlos Barcena Martin

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This study presents an on-site acoustic inspection methodology for quality and performance evaluation of building components. The work focuses on global and detailed sound source localisation, by successively performing acoustic beamforming and sound intensity measurements. A portable experimental setup is developed, consisting of an omnidirectional broadband acoustic source and a microphone array and sound intensity probe. Three main acoustic indicators are of interest, namely the sound pressure distribution on the surface of components such as walls, windows and junctions, the three-dimensional sound intensity field in the vicinity of junctions, and the sound transmission loss of partitions. The measurement data is post-processed and converted into a three-dimensional numerical model of the acoustic indicators with the help of the simultaneously acquired geolocation information. The three-dimensional acoustic indicators are then integrated into an augmented reality platform superimposing them onto a real-time visualisation of the spatial environment. The methodology thus enables a measurement-supported inspection process of buildings and the correction of errors during construction and refurbishment. Two experimental validation cases are shown. The first consists of a laboratory measurement on a full-scale mockup of a room, featuring a prefabricated panel. The latter is installed with controlled defects such as lack of insulation and joint sealing material. It is demonstrated that the combined acoustic and augmented reality tool is capable of identifying acoustic leakages from the building defects and assist in correcting them. The second validation case is performed on a prefabricated room at a near-completion stage in the factory. With the help of the measurements and visualisation tools, the homogeneity of the partition installation is evaluated and leakages from junctions and doors are identified. Furthermore, the integration of acoustic indicators together with thermal and geometrical indicators via the augmented reality platform is shown.

Keywords: acoustic inspection, prefabricated building components, augmented reality, sound source localization

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Authors: Emre Bayamlıoğlu

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This paper aims to provide an account of the legal and the regulative aspects of the algorithmic reputation systems with a special emphasis on the sharing economy (i.e., Uber, Airbnb, Lyft) business model. The first section starts with an analysis of the legal and commercial nature of the tripartite relationship among the parties, namely, the host platform, individual sharers/service providers and the consumers/users. The section further examines to what extent an algorithmic system of reputational information could serve as an alternative to legal regulation. Shortcomings are explained and analyzed with specific examples from Airbnb Platform which is a pioneering success in the sharing economy. The following section focuses on the issue of governance and control of the reputational information. The section first analyzes the legal consequences of algorithmic filtering systems to detect undesired comments and how a delicate balance could be struck between the competing interests such as freedom of speech, privacy and the integrity of the commercial reputation. The third section deals with the problem of manipulation by users. Indeed many sharing economy businesses employ certain techniques of data mining and natural language processing to verify consistency of the feedback. Software agents referred as "bots" are employed by the users to "produce" fake reputation values. Such automated techniques are deceptive with significant negative effects for undermining the trust upon which the reputational system is built. The third section is devoted to explore the concerns with regard to data mobility, data ownership, and the privacy. Reputational information provided by the consumers in the form of textual comment may be regarded as a writing which is eligible to copyright protection. Algorithmic reputational systems also contain personal data pertaining both the individual entrepreneurs and the consumers. The final section starts with an overview of the notion of reputation as a communitarian and collective form of referential trust and further provides an evaluation of the above legal arguments from the perspective of public interest in the integrity of reputational information. The paper concludes with certain guidelines and design principles for algorithmic reputation systems, to address the above raised legal implications.

Keywords: sharing economy, design principles of algorithmic regulation, reputational systems, personal data protection, privacy

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Authors: Ahmed Madian, Muaz Aljarhi

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Coordinate Rotation Digital Computer (CORDIC) is a unique digital computing unit intended for the computation of mathematical operations and functions. This paper presents a multi-CORDIC processor that integrates different CORDIC architectures on a single FPGA chip and allows the user to select the CORDIC architecture to proceed with based on what he wants to calculate and his/her needs. Synthesis show that radix 2 CORDIC has the lowest clock delay, radix 8 CORDIC has the highest LUT usage and lowest register usage while Hybrid Radix 4 CORDIC had the highest clock delay.

Keywords: multi, CORDIC, FPGA, processor

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Authors: Renée M. Ripken, Johannes G. E. Gardeniers, Séverine Le Gac

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Controlling the multiphase behavior of aqueous biomass mixtures is essential when working in the biomass conversion industry. Here, the vapor/liquid equilibria (VLE) of ethylene glycol, glycerol, and xylitol were studied for temperatures between 25 and 200 °C and pressures of 1 to 10 bar. These experiments were performed in a microfluidic platform, which exhibits excellent heat transfer properties so that equilibrium is reached fast. Firstly, the saturated vapor pressure as a function of the temperature and the substrate mole fraction of the substrate was calculated using AspenPlus with a Redlich-Kwong-Soave Boston-Mathias (RKS-BM) model. Secondly, we developed a high-pressure and high-temperature microfluidic set-up for experimental validation. Furthermore, we have studied the multiphase flow pattern that occurs after the saturation temperature was achieved. A glass-silicon microfluidic device containing a 0.4 or 0.2 m long meandering channel with a depth of 250 μm and a width of 250 or 500 μm was fabricated using standard microfabrication techniques. This device was placed in a dedicated chip-holder, which includes a ceramic heater on the silicon side. The temperature was controlled and monitored by three K-type thermocouples: two were located between the heater and the silicon substrate, one to set the temperature and one to measure it, and the third one was placed in a 300 μm wide and 450 μm deep groove on the glass side to determine the heat loss over the silicon. An adjustable back pressure regulator and a pressure meter were added to control and evaluate the pressure during the experiment. Aqueous biomass solutions (10 wt%) were pumped at a flow rate of 10 μL/min using a syringe pump, and the temperature was slowly increased until the theoretical saturation temperature for the pre-set pressure was reached. First and surprisingly, a significant difference was observed between our theoretical saturation temperature and the experimental results. The experimental values were 10’s of degrees higher than the calculated ones and, in some cases, saturation could not be achieved. This discrepancy can be explained in different ways. Firstly, the pressure in the microchannel is locally higher due to both the thermal expansion of the liquid and the Laplace pressure that has to be overcome before a gas bubble can be formed. Secondly, superheating effects are likely to be present. Next, once saturation was reached, the flow pattern of the gas/liquid multiphase system was recorded. In our device, the point of nucleation can be controlled by taking advantage of the pressure drop across the channel and the accurate control of the temperature. Specifically, a higher temperature resulted in nucleation further upstream in the channel. As the void fraction increases downstream, the flow regime changes along the channel from bubbly flow to Taylor flow and later to annular flow. All three flow regimes were observed simultaneously. The findings of this study are key for the development and optimization of a microreactor for hydrogen production from biomass.

Keywords: biomass conversion, high pressure and high temperature microfluidics, multiphase, phase diagrams, superheating

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Authors: Vahid Bairami Rad

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Due to the tremendous progress in computer technology in the last decades, the capabilities of computers increased enormously and working with a computer became a normal activity for nearly everybody. With all the possibilities a computer can offer, humans and their interaction with computers are now a limiting factor. This gave rise to a lot of research in the field of HCI (human computer interaction) aiming to make interaction easier, more intuitive, and more efficient. To research eye gaze based interfaces it is necessary to understand both sides of the interaction–the human eye and the eye tracker. The first section gives an overview on the anatomy of the eye. The second section accuracy and calibration issue. The subsequent section presents data from a user study where eye movements have been recorded while watching a video and while surfing the Internet. Statistics on the eye movement during these tasks for several individuals provide typical values and ranges for fixation times and saccade lengths and are the foundation for discussions in later chapters. The data also reveal typical limitations of eye trackers.

Keywords: human computer interaction, gaze tracking, calibration, eye movement

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Authors: Molin A., De Ramos J. M., Cadion L. G., Pico R. L.

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Malnutrition and food insecurity represent significant global challenges affecting millions of individuals, particularly in low-income and developing regions. The researchers created a solar-powered enzymatic composting bioreactor with an Arduino-based monitoring system for pH, humidity, and temperature. It manages mixed municipal solid wastes incorporating industrial enzymes and whey additives for accelerated composting and minimized carbon footprint. Within 15 days, the bioreactor yielded 54.54% compost compared to 44.85% from traditional methods, increasing yield by nearly 10%. Tests showed that the bioreactor compost had 4.84% NPK, passing metal analysis standards, while the traditional pit compost had 3.86% NPK; both are suitable for agriculture. Statistical analyses, including ANOVA and Tukey's HSD test, revealed significant differences in agricultural yield across different compost types based on leaf length, width, and number of leaves. The study compared the effects of different composts on Brassica rapa subsp. Chinesis (Petchay) and Brassica juncea (Mustasa) plant growth. For Pechay, significant effects of compost type on plant leaf length (F(5,84) = 62.33, η² = 0.79) and leaf width (F(5,84) = 12.35, η² = 0.42) were found. For Mustasa, significant effects of compost type on leaf length (F(4,70) = 20.61, η² = 0.54), leaf width (F(4,70) = 19.24, η² = 0.52), and number of leaves (F(4,70) = 13.17, η² = 0.43) were observed. This study explores the effectiveness of the enzymatic composting bioreactor and its viability in promoting carbon farming as a solution to food insecurity and malnutrition.

Keywords: malnutrition, food insecurity, enzymatic composting bioreactor, arduino-based monitoring system, enzymes, carbon farming, whey additive, NPK level

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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This study aimed to address the problem of improving the confidence of key points by fusing multi-view information, thereby estimating human posture more accurately. We first obtained multi-view image information and then used the MvP algorithm to fuse this multi-view information together to obtain a set of high-confidence human key points. We used these as the input for the Spatio-Temporal Graph Convolution (ST-GCN). ST-GCN is a deep learning model used for processing spatio-temporal data, which can effectively capture spatio-temporal relationships in video sequences. By using the MvP algorithm to fuse multi-view information and inputting it into the spatio-temporal graph convolution model, this study provides an effective method to improve the accuracy of human posture estimation and provides strong support for further research and application in related fields.

Keywords: multi-view, pose estimation, ST-GCN, joint fusion

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Authors: Venus Torabi

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ISIS, the terrorist organization, modified two videogames, ARMA III and Grand Theft Auto 5 (2013) as means of online recruitment and ideological propaganda. The urge to study the mechanism at work, whether it has been successful or not, derives (Digital) Humanities experts to explore how codes of terror, Islamic ideology and recruitment strategies are incorporated into the ludic mechanics of videogames. Another aspect of the significance lies in the fact that this is a latent problem that has not been fully addressed in an interdisciplinary framework prior to this study, to the best of the researcher’s knowledge. Therefore, due to the complexity of the subject, the present paper entangles with game studies, philosophical and religious poles to form the methodology of conducting the research. As a contextualized epistemology of such exploitation of videogames, the core argument is building on the notion of “Culture Industry” proposed by Theodore W. Adorno and Max Horkheimer in Dialectic of Enlightenment (2002). This article posits that the ideological underpinnings of ISIS’s cause corroborated by the action-bound mechanics of the videogames are in line with adhering to the Islamic Eschatology as a furnishing ground and an excuse in exercising terrorism. It is an account of ISIS’s modification of the videogames, a tool of technological progression to practice online radicalization. Dialectically, this practice is packed up in rhetoric for recognizing a religious myth (the advent of a savior), as a hallmark of regression. The study puts forth that ISIS’s wreaking havoc on the world, both in reality and within action videogames, is negotiating the process of self-assertion in the players of such videogames (by assuming one’s self a member of terrorists) that leads to self-annihilation. It tries to unfold how ludic Mod videogames are misused as tools of mass deception towards ethnic cleansing in reality and line with the distorted Eschatological myth. To conclude, this study posits videogames to be a new avenue of mass deception in the framework of the Culture Industry. Yet, this emerges as a two-edged sword of mass deception in ISIS’s modification of videogames. It shows that ISIS is not only trying to hijack the minds through online/ludic recruitment, it potentially deceives the Muslim communities or those prone to radicalization into believing that it's terrorist practices are preparing the world for the advent of a religious savior based on Islamic Eschatology. This is to claim that the harsh actions of the videogames are potentially breeding minds by seeds of terrorist propaganda and numbing them to violence. The real world becomes an extension of that harsh virtual environment in a ludic/actual continuum, the extension that is contributing to the mass deception mechanism of the terrorists, in a clandestine trend.

Keywords: culture industry, dialectic, ISIS, islamic eschatology, mass deception, video games

Procedia PDF Downloads 127

Authors: Noryani Natasha Yahaya, Anas Ibrahim, Juraidah Ahmad, Azura Ahmad, Mohd Ikmal Fazlan Rosli, Zailan Ramli, Muhd Sidek Muhd Norhasri

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The subgrade formation is an important element of the railway structure which holds overall track stability. Conventional track maintenance involves many substructure component replacements, as well as track re-ballasting on a regular basis is partially contributed to the embankment's long-term settlement problem. For subgrade long-term stability analysis, the geophysical method is commonly being used to diagnose those hidden sources/mechanisms of track deterioration problems that the normal visual method is unable to detect. Electrical resistivity tomography (ERT) is one of the applicable geophysical tools that are helpful in railway subgrade inspection/track monitoring due to its flexibility and reliability of the analysis. The ERT was conducted at KM 23.0 of Pinang Tunggal track to investigate the subgrade of railway track through the characterization/mapping on track formation profiling which was directly generated using 2D analysis of Res2dinv software. The profiles will allow examination of the presence and spatial extent of a significant subgrade layer and screening of any poor contact of soil boundary. Based on the finding, there is a mix/interpretation/intermixing of an interlayer between the sub-ballast and the sand. Although the embankment track considered here is at no immediate risk of settlement effect or any failure, the regular monitoring of track’s location will allow early correction maintenance if necessary. The developed data of track formation clearly shows the similarity of the side view with the assessed track. The data visualization in the 2D section of the track embankment agreed well with the initial assumption based on the main element structure general side view.

Keywords: ground track, assessment, resistivity, geophysical railway, method

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Authors: Emily Wood, James K. Westphal, Itamar Lerner

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Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

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Authors: G. Traini, G. Battistoni, F. Collamati, E. De Lucia, R. Faccini, C. Mancini-Terracciano, M. Marafini, I. Mattei, S. Muraro, A. Sarti, A. Sciubba, E. Solfaroli Camillocci, M. Toppi, S. M. Valle, C. Voena, V. Patera

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In Particle Therapy (PT) treatments a large amount of secondary particles, whose emission point is correlated to the dose released in the crossed tissues, is produced. The measurement of the secondary charged fragments component could represent a valid technique to monitor the beam range during the PT treatments, that is a still missing item in the clinical practice. A sub-millimetrical precision on the beam range measurement is required to significantly optimise the technique and to improve the treatment quality. In this contribution, a detector, named Dose Profiler (DP), is presented. It is specifically planned to monitor on-line the beam range exploiting the secondary charged particles produced in PT Carbon ions treatment. In particular, the DP is designed to track the secondary fragments emitted at large angles with respect to the beam direction (mainly protons), with the aim to reconstruct the spatial coordinates of the fragment emission point extrapolating the measured track toward the beam axis. The DP is currently under development within of the INSIDE collaboration (Innovative Solutions for In-beam Dosimetry in hadrontherapy). The tracker is made by six layers (20 × 20 cm²) of BCF-12 square scintillating fibres (500 μm) coupled to Silicon Photo-Multipliers, followed by two plastic scintillator layers of 6 mm thickness. A system of front-end boards based on FPGAs arranged around the detector provides the data acquisition. The detector characterization with cosmic rays is currently undergoing, and a data taking campaign with protons will take place in May 2017. The DP design and the performances measured with using MIPs and protons beam will be reviewed.

Keywords: fragmentation, monitoring, particle therapy, tracking

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Authors: Raja Rajeswari K

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Orthogonal Frequency Division Multiplexing (OFDM), a multi Carrier transmission technique that has been used in implementing the majority of wireless applications like Wireless Network Protocol Standards (like IEEE 802.11a, IEEE 802.11n), in telecommunications (like LTE, LTE-Advanced) and also in Digital Audio & Video Broadcast standards. The latest research and development in the area of orthogonal frequency division multiplexing, Universal Filtered Multi Carrier (UFMC) & Filtered OFDM (F-OFDM) has attracted lots of attention for wideband wireless communications. In this paper UFMC & F-OFDM system are implemented and comparative analysis are carried out in terms of M-ary QAM modulation scheme over Dolph-chebyshev filter & rectangular window filter and to estimate Bit Error Rate (BER) over Rayleigh fading channel.

Keywords: UFMC, F-OFDM, BER, M-ary QAM

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Authors: Ho-Won Jung, Donghun Lee, Hyung-Jin Kim

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Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size.

Keywords: explanatory modeling, metabolic syndrome, predictive analytics, theory building

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Authors: Yetunde M. Dairo, Johnny Collett, Helen Dawes

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Background and Aim: Engagement with physical activity (PA) research is poor among adults with intellectual disabilities (ID), particularly in those from residential homes. This study explored why, by asking managers of residential homes, adults with ID and their carers. Methods: Participants: A convenient sample of 23 individuals from two UK local authorities, including a group of ID residential home managers, adults with ID and their support staff. Procedures: A) Residential home managers (n=6) were asked questions about their willingness to allow their residents to participate in PA research; B) eleven adults with ID and their support workers (n=6) were asked questions about their willingness to accept 7-day accelerometer monitoring and/or the International Physical Activity Questionnaire-short version (IPAQ-s) as PA measures. The IPAQ-s was administered by the researcher and they were each provided with samples of accelerometers to try on. Results: A) Five out of six managers said that the burden of wearing the accelerometer for seven days would be too high for the people they support, the majority of whom might be unable to express their wishes. They also said they would be unwilling to act as proxy respondents for the same reason. Additionally, they cited time pressure, understaffing, and reluctance to spend time on the research paperwork as further reasons for non-participation. B) All 11 individuals with ID completed the IPAQ-s while only three accepted the accelerometer, one of whom was deemed inappropriate to wear it. Reasons for rejecting accelerometers included statements from participants of: ‘too expensive’, ‘too heavy’, ‘uncomfortable’, and two people said they would not want to wear it for more than one day. All adults with ID (11) and their support workers (6) provided information about their physical activity levels through the IPAQ-s. Conclusions: Care home managers are a barrier to research participation. However, adults with ID would be happy for the IPAQ-s as a PA measure, but less so for the 7-day accelerometer monitoring. In order to improve participation in this population, the choice of PA measure is considered important. Moreover, there is a need for studies exploring how best to engage ID residential home managers in PA research.

Keywords: intellectual disability, physical activity measurement, research engagement, research participation

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Authors: Londeka Ntuli, Frasia Oosthuizen

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Background: Tenefovir disiproxil fumarate (TDF) is a nephrotoxic drug and has been proven to contribute to renal insufficiency necessitating intensive monitoring and management of adverse effects arising from prolonged exposure to the drug. TDF is one of the preferred first-line drugs used in combination therapy in most regions. There are estimated 300 000 patients being initiated on the Efavirenz/TDF/Emtricitabine first-line regimen annually in South Africa. It is against this background that this study aims to investigate the effects of TDF on renal sufficiency of HIV positive patients. Methodology: A retrospective quantitative study was conducted, analysing clinical charts of HIV positive patient’s older than 18 years of age and on a TDF-containing regimen for more than 1 year. Data were obtained from the analysis of patient files and was transcribed into Microsoft® Excel® spreadsheet. Extracted data were coded, categorised and analysed using STATA®. Results: A total of 275 patient files were included in this study. Renal function started decreasing after 3 months of treatment (with 93.5% patients having a normal EGFR), and kept on decreasing as time progressed with only 39.6% normal renal function at year 4. Additional risk factors for renal insufficiency included age below 25, female gender, and additional medication. Conclusion: It is clear from this study that the use of TDF necessitates intensive monitoring and management of adverse effects arising from prolonged exposure to the drug. The findings from this study generated pertinent information on the safety profile of the drug TDF in a resource-limited setting of a public health institution. The appropriate management is of tremendous importance in the South African context where the majority of HIV positive individuals are on the TDF containing regimen; thus it is beneficial to ascertain the possible level of toxicities these patients may be experiencing.

Keywords: renal insufficiency, tenefovir, HIV, risk factors

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Authors: Jazim Sohail, Filipe Teixeira-Dias

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Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

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Authors: Mariam Ismail, Kazeem Rufai, Jeremiah Balogun

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A study was conducted to apply fuzzy logic to the development of a monitoring model for child pornography based on associated risk factors, which can be used by forensic experts or integrated into forensic systems for the early detection of child pornographic activities. A number of methods were adopted in the study, which includes an extensive review of related works was done in order to identify the factors that are associated with child pornography following which they were validated by an expert sex psychologist and guidance counselor, and relevant data was collected. Fuzzy membership functions were used to fuzzify the associated variables identified alongside the risk of the occurrence of child pornography based on the inference rules that were provided by the experts consulted, and the fuzzy logic expert system was simulated using the Fuzzy Logic Toolbox available in the MATLAB Software Release 2016. The results of the study showed that there were 4 categories of risk factors required for assessing the risk of a suspect committing child pornography offenses. The results of the study showed that 2 and 3 triangular membership functions were used to formulate the risk factors based on the 2 and 3 number of labels assigned, respectively. The results of the study showed that 5 fuzzy logic models were formulated such that the first 4 was used to assess the impact of each category on child pornography while the last one takes the 4 outputs from the 4 fuzzy logic models as inputs required for assessing the risk of child pornography. The following conclusion was made; there were factors that were related to personal traits, social traits, history of child pornography crimes, and self-regulatory deficiency traits by the suspects required for the assessment of the risk of child pornography crimes committed by a suspect. Using the values of the identified risk factors selected for this study, the risk of child pornography can be easily assessed from their values in order to determine the likelihood of a suspect perpetuating the crime.

Keywords: fuzzy, membership functions, pornography, risk factors

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Authors: Chloe Shu-Hua Yeh

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Effects of videogame play on players cognitive abilities is a growing research field in the recent decades, however, little is known about how ‘out-of-school’ use of videogame influences creativity. This interdisciplinary research explores the cognitive and emotional effects of two different types of online videogames (an action videogame and a non-action videogame) on subsequent creativity performances using a within-participant design study with 36 participants. Results showed that after playing the action game participants performed higher originality, elaboration and flexibility than after playing the causal game. The results explored effects of emotional states elicited during playing the games suggesting that arousal may be a significant emotional factor which influence subsequent creativity performance. The cognitive and emotional effects of videogame were discussed followed with implications for emotion-creativity-videogame play research, game designers, educational practitioners and parents.

Keywords: attentional breadth, creativity, emotion, videogame play

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Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

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Authors: Asaf Toker

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Matricelf developed a technology that enables the production of autologous engineered tissue composed of matrix and cells derived from patients Omentum biopsy. The platform showed remarkable pre-clinical results for several medical conditions. The company recently licensed the technology that enabled scientist at Tel Aviv university that 3D printed a human heart from human cells and matrix for the first time in human history. The company plans to conduct its first human clinical trial for Acute Spinal Cord Injury (SCI) early in 2023.

Keywords: tissue engineering, regenerative medicine, spinal Cord Injury, autologous implants, iPSC

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Authors: Zongyan Yang, Gagan K. Jena, Sankar B. Karanam, Noora M. A. Hokal

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Jumeirah First beach, a segment of coastline of length 1.5 km, is one of the popular public beaches in Dubai, UAE. The stability of the beach has been affected by several coastal developmental projects, including The World, Island 2 and La Mer. A comprehensive stabilization scheme comprising of two composite groynes (of lengths 90 m and 125m), modification to the northern breakwater of Jumeirah Fishing Harbour and beach re-nourishment was implemented by Dubai Municipality in 2012. However, the performance of the implemented stabilization scheme has been compromised by La Mer project (built in 2016), which modified the wave climate at the Jumeirah First beach. The objective of the coastal modelling studies is to establish design basis for further beach stabilization scheme(s). Comprehensive coastal modelling studies had been conducted to establish the nearshore wave climate, equilibrium beach orientations and stable beach plan forms. Based on the outcomes of the modeling studies, recommendation had been made to extend the composite groynes to stabilize the Jumeirah First beach. Wave transformation was performed following an interpolation approach with wave transformation matrixes derived from simulations of a possible range of wave conditions in the region. The Dubai coastal wave model is developed with MIKE21 SW. The offshore wave conditions were determined from PERGOS wave data at 4 offshore locations with consideration of the spatial variation. The lateral boundary conditions corresponding to the offshore conditions, at Dubai/Abu Dhabi and Dubai Sharjah borders, were derived with application of LitDrift 1D wave transformation module. The Dubai coastal wave model was calibrated with wave records at monitoring stations operated by Dubai Municipality. The wave transformation matrix approach was validated with nearshore wave measurement at a Dubai Municipality monitoring station in the vicinity of the Jumeirah First beach. One typical year wave time series was transformed to 7 locations in front of the beach to count for the variation of wave conditions which are affected by adjacent and offshore developments. Equilibrium beach orientations were estimated with application of LitDrift by finding the beach orientations with null annual littoral transport at the 7 selected locations. The littoral transport calculation results were compared with beach erosion/accretion quantities estimated from the beach monitoring program (twice a year including bathymetric and topographical surveys). An innovative integral method was developed to outline the stable beach plan forms from the estimated equilibrium beach orientations, with predetermined minimum beach width. The optimal lengths for the composite groyne extensions were recommended based on the stable beach plan forms.

Keywords: composite groyne, equilibrium beach orientation, stable beach plan form, wave transformation matrix

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Jansirani Natarajan, Mickael Antoinne Joseph

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The COVID-19 pandemic has interrupted face-to-face education and forced universities into an emergency remote teaching curriculum over a short duration. This abrupt transition in the Spring 2020 semester left both faculty and students without proper preparation for continuing higher education in an online environment. Online learning took place in different formats, including fully synchronous, fully asynchronous, and blended in our university through the e-learning platform MOODLE. Studies have shown that students’ engagement, is a critical factor for optimal online teaching. Very few studies have assessed online engagement with ERT during the COVID-19 pandemic. Purpose: Therefore, this study, sought to understand how the sudden transition to emergency remote teaching impacted nursing students’ engagement with online courses in a Middle Eastern public university. Method: A cross-sectional descriptive research design was adopted in this study. Data were collected through a self-reported online survey using Dixon’s online students’ engagement questionnaire from a sample of 177 nursing students after the ERT learning semester. Results The maximum possible engagement score was 95, and the maximum scores in the domains of skills engagement, emotional engagement, participation engagement, and performance engagement were 30, 25, 30, and 10 respectively. Dixson (2010) noted that a mean item score of ≥3.5 (total score of ≥66.5) represents a highly engaged student. The majority of the participants were females (71.8%) and 84.2% were regular BSN students. Most of them (32.2%) were second-year students and 52% had a CGPA between 2 and 3. Most participants (56.5%) had low engagement scores with ERT learning during the COVID lockdown. Among the four engagement domains, 78% had low engagement scores for the participation domain. There was no significant association found between the engagement and the demographic characteristics of the participants. Conclusion The findings supported the importance of engaging students in all four categories skill, emotional, performance, and participation. Based on the results, training sessions were organized for faculty on various strategies for engaging nursing students in all domains by using the facilities available in the MOODLE (online e-learning platform). It added value as a dashboard of information regarding ERT for the administrators and nurse educators to introduce numerous active learning strategies to improve the quality of teaching and learning of nursing students in the University.

Keywords: engagement, perception, emergency remote learning, COVID-19

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Authors: András Fülöp, Lejla Heilmann, Zsolt Szabó, Ákos Koós

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Microbial fuel cells (MFCs) present a sustainable biotechnological solution to future energy demands. The aim of this study was to construct soil based, single cell, membrane-less MFC systems, operated without treatment to continuously power on-site monitoring and control systems during the soil bioremediation processes. Our Pseudomonas aeruginosa 541 isolate is an ideal choice for MFCs, because it is able to produce pyocyanin which behaves as electron-shuttle molecule, furthermore, it also has a significant antimicrobial effect. We tested several materials and structural configurations to obtain long term high power output. Comparing different configurations, a proton exchange membrane-less, 0.6 m long with 0.05 m diameter MFC tubes offered the best long-term performances. The long-term electricity production were tested from starch, yeast extract (YE), carboxymethyl cellulose (CMC) with humic acid (HA) as a mediator. In all cases, 3 kΩ external load have been used. The two best-operated systems were the Pseudomonas aeruginosa 541 containing MFCs with 1 % carboxymethyl cellulose and the MFCs with 1% yeast extract in the anode area and 35% hydrogel in the cathode chamber. The first had 3.3 ± 0.033 mW/m2 and the second had 4.1 ± 0.065 mW/m2 power density values. These systems have operated for 230 days without any treatment. The addition of 0.2 % HA and 1 % YE referred to the volume of the anode area resulted in 1.4 ± 0.035 mW/m2 power densities. The mixture of 1% starch with 0.2 % HA gave 1.82 ± 0.031 mW/m2. Using CMC as retard carbon source takes effect in the long-term bacterial survivor, thus enable the expression of the long term power output. The application of hydrogels in the cathode chamber significantly increased the performance of the MFC units due to their good water retention capacity.

Keywords: microbial fuel cell, bioremediation, Pseudomonas aeruginosa, biotechnological solution

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Authors: Stephen Miller

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The act of applying game mechanics and dynamics (which have been traditionally used in video games) into business applications is being widely trialed in an effort to make conventional business software a bit more participative, fun and engaging. This new trend, named ‘gamification’ has its believers and of course, its critics who still need convincing that the concept is an effective and beneficial business tool worthy of investment. The literature reveals that user engagement of business intelligence (BI) tools is much lower than expected and investors are failing to get a good return on their investment (ROI). So, a software prototype will be designed and developed to add gamification to a BI tool to determine its effect upon the user engagement levels of test participants. The experimental study will be evaluated using the comprehensive User Engagement Scale (UES) to see if there are improvements in areas such as; aesthetics, perceived usability, endurability, novelty, felt involvement and focused attention. The results of this unique study should demonstrate whether or not ‘gamifying’ a BI tool has the potential to increase an individual’s motivation to use BI software more often.

Keywords: business intelligence, gamification, human computer interaction, user engagement

Procedia PDF Downloads 568