Search results for: utility item sets

Authors: A. Al Hajeri, M. E. Minton, B. Haskins, F. H. Cummins

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The pre-hospital endotracheal intubation is fraught with difficulties; one solution offered has been video laryngoscopy (VL) which permits better visualization of the glottis than the standard method of direct laryngoscopy (DL). This method has resulted in a higher first attempt success rate and fewer failed intubations. However, VL has mainly been evaluated by experienced providers (experienced anesthetists), and as such the utility of this device for those whom infrequently intubate has not been thoroughly assessed. We sought to evaluate this equipment to determine whether in the hands of novice providers this equipment could prove an effective airway management adjunct. DL and two VL methods (C-Mac with distal screen/C-Mac with attached screen) were evaluated by simulating practice on a Laerdal airway management trainer manikin. Twenty Emergency Medical Technicians (basics) were recruited as novice practitioners. This group was used to eliminate bias, as these clinicians had no pre-hospital experience of intubation (although they did have basic airway skills). The following areas were assessed: Time taken to intubate, number of attempts required to successfully intubate, ease of use of equipment VL (attached screen) took on average longer for novice clinicians to successfully intubate and had a lower success rate and reported higher rating of difficulty compared to DL. However, VL (with distal screen) and DL were comparable on intubation times, success rate, gastric inflation rate and rating of difficulty by the user. This study highlights the routine use of VL by inexperienced clinicians would be of no added benefit over DL. Further studies are required to determine whether Emergency Medical Technicians (Paramedics) would benefit from this airway adjunct, and ascertain whether after initial mastery of VL (with a distal screen), lower intubation times and difficulty rating may be achievable.

Keywords: direct laryngoscopy, endotracheal intubation, pre-hospital, video laryngoscopy

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Authors: Chung Danbi, Linton Oliver, Whang Yoon-Jae

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Supermodularity, or complementarity, is a popular concept in economics which can characterize many objective functions such as utility, social welfare, and production functions. Further, supermodular dominance captures a preference for greater interdependence among inputs of those functions, and it can be applied to examine which input set would produce higher expected utility, social welfare, or production. Therefore, we propose and justify a consistent testing for a useful implication of supermodular dominance. We also conduct Monte Carlo simulations to explore the finite sample performance of our test, with critical values obtained from the recentered bootstrap method, with and without the selective recentering, and the subsampling method. Under various parameter settings, we confirmed that our test has reasonably good size and power performance. Finally, we apply our test to compare the geographic and distant knowledge spillover in terms of their effects on social welfare using the National Bureau of Economic Research (NBER) patent data. We expect localized citing to supermodularly dominate distant citing if the geographic knowledge spillover engenders greater social welfare than distant knowledge spillover. Taking subgroups based on firm and patent characteristics, we found that there is industry-wise and patent subclass-wise difference in the pattern of supermodular dominance between localized and distant citing. We also compare the results from analyzing different time periods to see if the development of Internet and communication technology has changed the pattern of the dominance. In addition, to appropriately deal with the sparse nature of the data, we apply high-dimensional methods to efficiently select relevant data.

Keywords: supermodularity, supermodular dominance, stochastic dominance, Monte Carlo simulation, bootstrap, subsampling

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Authors: Victor Birikorang Danquah

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Ghana's primary energy supply is heavily reliant on petroleum, biomass, and hydropower. Currently, Ghana gets its energy from hydropower (Akosombo and Bui), thermal power plants powered by crude oil, natural gas, and diesel, solar power, and imports from La Cote d'Ivoire. Until the early 2000s, large hydroelectric dams dominated Ghana's electricity generation. Due to unreliable weather patterns, Ghana increased its reliance on thermal power. However, thermal power contributes the highest percentage in terms of electricity generation in Ghana and is predominantly supplied by Independent Power Producers (IPPs). Ghana's electricity industry operates the corporate utility model as its business model. This model is typically' vertically integrated,' with a single corporation selling the majority of power generated by its generation assets to its retail business, which then sells the electricity to retail market consumers. The corporate utility model has a straightforward value proposition that is based on increasing the number of energy units sold. The unit volume business model drives the entire energy value chain to increase throughput, locking system users into unsustainable practices. This report uses the qualitative research approach to explore the electricity industry in Ghana. There is a need for increasing renewable energy, such as wind and solar, in electricity generation. The research recommends two critical business models for the penetration of renewable energy in Ghana's power sector. The first model is the peer-to-peer electricity trading model, which relies on a software platform to connect consumers and generators in order for them to trade energy directly with one another. The second model is about encouraging local energy generation, incentivizing optimal time-of-use behaviour, and allowing any financial gains to be shared among the community members.

Keywords: business model innovation, electricity generation, renewable energy, solar energy, sustainability, wind energy

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Authors: Laith F. Gulli, Nicole M. Mallory

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The undertaking to develop a psychometric instrument is monumental. Understanding the relationship between variables and events is important in structural and exploratory design of psychometric instruments. Considering this, we describe a method used to group, pair and combine multiple Philosophical Assumption statements that assisted in development of a 13 item psychometric screening instrument. We abbreviated our Philosophical Assumptions (PA)s and added parameters, which were then condensed and mathematically modeled in a specific process. This model produced clusters of combinatorics which was utilized in design and development for 1) information retrieval and categorization 2) item development and 3) estimation of interactions among variables and likelihood of events. The psychometric screening instrument measured Knowledge, Assessment (education) and Beliefs (KAB) of New Addictions Research (NAR), which we called KABNAR. We obtained an overall internal consistency for the seven Likert belief items as measured by Cronbach’s α of .81 in the final study of 40 Clinicians, calculated by SPSS 14.0.1 for Windows. We constructed the instrument to begin with demographic items (degree/addictions certifications) for identification of target populations that practiced within Outpatient Substance Abuse Counseling (OSAC) settings. We then devised education items, beliefs items (seven items) and a modifiable “barrier from learning” item that consisted of six “choose any” choices. We also conceptualized a close relationship between identifying various degrees and certifications held by Outpatient Substance Abuse Therapists (OSAT) (the demographics domain) and all aspects of their education related to EB-NAR (past and present education and desired future training). We placed a descriptive (PA)1tx in both demographic and education domains to trace relationships of therapist education within these two domains. The two perceptions domains B1/b1 and B2/b2 represented different but interrelated perceptions from the therapist perspective. The belief items measured therapist perceptions concerning EB-NAR and therapist perceptions using EB-NAR during the beginning of outpatient addictions counseling. The (PA)s were written in simple words and descriptively accurate and concise. We then devised a list of parameters and appropriately matched them to each PA and devised descriptive parametric (PA)s in a domain categorized information grid. Descriptive parametric (PA)s were reduced to simple mathematical symbols. This made it easy to utilize parametric (PA)s into algorithms, combinatorics and clusters to develop larger information grids. By using matching combinatorics we took paired demographic and education domains with a subscript of 1 and matched them to the column with each B domain with subscript 1. Our algorithmic matching formed larger information grids with organized clusters in columns and rows. We repeated the process using different demographic, education and belief domains and devised multiple information grids with different parametric clusters and geometric arrays. We found benefit combining clusters by different geometric arrays, which enabled us to trace parametric variables and concepts. We were able to understand potential differences between dependent and independent variables and trace relationships of maximum likelihoods.

Keywords: psychometric, parametric, domains, grids, therapists

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Aboulkacem El Mehdi

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We develop a theoretical model at the crossroads of labour and urban economics, used for explaining the mechanism through which the duration of home-workplace trips and their monetary costs impact the labour demand and supply in a spatially scattered labour market and how they are impacted by a change in passenger transport infrastructures and services. The spatial disconnection between home and job opportunities is referred to as the spatial mismatch hypothesis (SMH). Its harmful impact on employment has been subject to numerous theoretical propositions. However, all the theoretical models proposed so far are patterned around the American context, which is particular as it is marked by racial discrimination against blacks in the housing and the labour markets. Therefore, it is only natural that most of these models are developed in order to reproduce a steady state characterized by agents carrying out their economic activities in a mono-centric city in which most unskilled jobs being created in the suburbs, far from the Blacks who dwell in the city-centre, generating a high unemployment rates for blacks, while the White population resides in the suburbs and has a low unemployment rate. Our model doesn't rely on any racial discrimination and doesn't aim at reproducing a steady state in which these stylized facts are replicated; it takes the main principle of the SMH -the spatial disconnection between homes and workplaces- as a starting point. One of the innovative aspects of the model consists in dealing with a SMH related issue at an aggregate level. We link the parameters of the passengers transport system to employment in the whole area of a city. We consider here a city that consists of four areas: two of them are residential areas with unemployed workers, the other two host firms looking for labour force. The workers compare the indirect utility of working in each area with the utility of unemployment and choose between submitting an application for the job that generate the highest indirect utility or not submitting. This arbitration takes account of the monetary and the time expenditures generated by the trips between the residency areas and the working areas. Each of these expenditures is clearly and explicitly formulated so that the impact of each of them can be studied separately than the impact of the other. The first findings show that the unemployed workers living in an area benefiting from good transport infrastructures and services have a better chance to prefer activity to unemployment and are more likely to supply a higher 'quantity' of labour than those who live in an area where the transport infrastructures and services are poorer. We also show that the firms located in the most accessible area receive much more applications and are more likely to hire the workers who provide the highest quantity of labour than the firms located in the less accessible area. Currently, we are working on the matching process between firms and job seekers and on how the equilibrium between the labour demand and supply occurs.

Keywords: labour market, passenger transport infrastructure, spatial mismatch hypothesis, urban economics

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Authors: Jeonghyun Chang, Taegeun Lee, Heungsup Sung, Yoon-Seon Lee, Youn-Jung Kim, Mi-Na Kim

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Background: Safety device has been applied to improve safety and performance of blood culture practice. BD vacutainer® Safety-Lokᵀᴹ blood collection sets with pre-attached holder (Safety-Lok) (BD, USA) was evaluated in the emergency room (ER) of a tertiary care hospital. Methods: From April to June 2017, interns and nurses in ER were surveyed for blood culture practices with a questionnaire before and after 2 or 3 weeks of experience of Safety-Lok. All of them participated in exercise workshop for 1 hour combined with video education prior to the initial survey. The blood volume, positive and contamination rates of Safety-Lok-drawn (SD) blood cultures were compared to those of overall blood cultures. Results: Eighteen interns and 30 nurses were enrolled. As a result of the initial survey, interns had higher rates of needlestick incidence (27.8%), carriage of the blood-filled syringe with needle (88.9%) and lower rates of vacutainer use (38.9%) than nurses (13.3%, 53.3%, and 60.0%). Interns preferred to use safety devices (88.9%) rather than nurses (40.0%). The number of overall blood cultures and SD blood cultures was 9,053 and 555, respectively. While the overall blood volume of aerobic bottles was 2.6±2.1 mL, those of SD blood cultures were 5.0±3.0 mL in aerobic bottles and 6.0±3.0 mL in anaerobic bottles. Positive and contamination rates were 6.5% and 0.72% with SD blood cultures and 6.2% and 0.3% with overall blood cultures. Conclusions: The introduction of the safety device would encourage healthcare workers to collect adequate blood volume as well as lead to safer practices in the ER.

Keywords: blood culture, needlestick, safety device, volume

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Authors: Humberto Miranda, Haroldo G. Santana, Gabriel A. Paz, Vicente P. Lima, Jeffrey M. Willardson

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Foam rolling is a practice that has increased in popularity before and after strength training. The purpose of this study was to compare the acute effects of different foam rolling periods for the lower body muscles on subsequent performance (total repetitions and training volume), myoelectric activity and rating of perceived exertion in trained men. Fourteen trained men (26.2 ± 3.2 years, 178 ± 0.04 cm height, 82.2 ± 10 kg weight and body mass index 25.9 ± 3.3kg/m2) volunteered for this study. Four repetition maximum (4-RM) loads were determined for hexagonal bar deadlift and 45º angled leg press during test and retest sessions over two nonconsecutive days. Five experimental protocols were applied in a randomized design, which included: a traditional protocol (control)—a resistance training session without prior foam rolling; or resistance training sessions performed following one (P1), two (P2), three (P3), or four (P4) sets of 30 sec. foam rolling for the lower extremity musculature. Subjects were asked to roll over the medial and lateral aspects of each muscle group with as much pressure as possible. All foam rolling was completed at a cadence of 50 bpm. These procedures were performed on both sides unilaterally as described below. Quadriceps: between the apex of the patella and the ASIS; Hamstring: between the gluteal fold and popliteal fossa; Triceps surae: between popliteal fossa and calcaneus tendon. The resistance training consisted of five sets with 4-RM loads and two-minute rest intervals between sets, and a four-minute rest interval between the hexagonal bar deadlift and the 45º angled leg press. The number of repetitions completed, the myoelectric activity of vastus lateralis (VL), vastus medialis oblique (VMO), semitendinosus (SM) and medial gastrocnemius (GM) were recorded, as well as the rating of perceived exertion for each protocol. There were no differences between the protocols in the total repetitions for the hexagonal bar deadlift (Control - 16.2 ± 5.9; P1 - 16.9 ± 5.5; P2 - 19.2 ± 5.7; P3 - 19.4 ± 5.2; P4 - 17.2 ± 8.2) (p > 0.05) and 45º angled leg press (Control - 23.3 ± 9.7; P1 - 25.9 ± 9.5; P2 - 29.1 ± 13.8; P3 - 28.0 ± 11.7; P4 - 30.2 ± 11.2) exercises. Similar results between protocols were also noted for myoelectric activity (p > 0.05) and rating of perceived exertion (p > 0.05). Therefore, the results of the present study indicated no deleterious effects on performance, myoelectric activity and rating of perceived exertion responses during lower body resistance training.

Keywords: self myofascial release, foam rolling, electromyography, resistance training

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Authors: A. Sivakumar, S. S. Darun Prakash, P. Navaneethakrishnan

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In and around Erode District, it is estimated that more than 1250 chemical and allied textile processing fabric industries are affected, partially closed and shut off for various reasons such as poor management, poor supplier performance, lack of planning for productivity, fluctuation of output, poor investment, waste analysis, labor problems, capital/labor ratio, accumulation of stocks, poor maintenance of resources, deficiencies in the quality of fabric, low capacity utilization, age of plant and equipment, high investment and input but low throughput, poor research and development, lack of energy, workers’ fear of loss of jobs, work force mix and work ethic. The main objective of this work is to analyze the existing conditions in textile fabric sector, validate the break even of Total Productivity (TP), analyze, design and implement fuzzy sets and mathematical programming for improvement of productivity and quality dimensions in the fabric processing industry. It needs to be compatible with the reality of textile and fabric processing industries. The highly risk events from productivity and quality dimension were found by fuzzy systems and results are wrapped up among the textile fabric processing industry.

Keywords: break even point, fuzzy crisp data, fuzzy sets, productivity, productivity cycle, total productive maintenance

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Authors: Mohammadreza Akbari, Sara Akbari, Davood Domiri Ganji, Pooya Solimani, Reza Khalili

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As all experts know most of engineering system behavior in practical are nonlinear process (especially heat, fluid and mass, etc.) and analytical solving (no numeric) these problems are difficult, complex and sometimes impossible like (fluids and gas wave, these problems can't solve with numeric method, because of no have boundary condition) accordingly in this symposium we are going to exposure a innovative approach which we have named it Akbari-Ganji's Method or AGM in engineering, that can solve sets of coupled nonlinear differential equations (ODE, PDE) with high accuracy and simple solution and so this issue will be emerged after comparing the achieved solutions by Numerical method (Runge-Kutte 4th) and so compare to other methods such as HPM, ADM,… and exact solutions. Eventually, AGM method will be proved that could be created huge evolution for researchers, professors and students (engineering and basic science) in whole over the world, because of AGM coding system, so by using this software we can analytically solve all complicated linear and nonlinear differential equations, with help of that there is no difficulty for solving nonlinear differential equations(ODE and PDE). In this paper, we investigate and solve 4 types of the nonlinear differential equation with AGM method : 1-Heat and fluid, 2-Unsteady state of nonlinear partial differential, 3-Coupled nonlinear partial differential in wave equation, and 4-Nonlinear integro-differential equation.

Keywords: new method AGM, sets of coupled nonlinear equations at engineering field, waves equations, integro-differential, fluid and thermal

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Authors: Afsane Amiri

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Electric Vehicle (EV) technology is expected to take a major share in the light-vehicle market in the coming decades. Charging of EVs will put an extra burden on the distribution grid and in some cases adjustments will need to be made. In this paper a review of different plug-in and vehicle to grid (V2G) capable vehicles are given along with their power electronics topologies. The economic implication of charging the vehicle or sending power back to the utility is described in brief.

Keywords: energy storage system, battery unit, cost, optimal sizing, plug-in electric vehicles (PEVs), smart grid

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Authors: Michaela Storen, Michelle Harvey, Xavier Conlan

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Gun violence internationally is increasing at an unprecedented level, becoming a favoured means for executing violence against another individual. Not only is this putting a strain on forensic scientists who attempt to determine the cause of death in circumstances where firearms have been involved in the death of an individual, but it also highlights the need for an alternative technique of identification of a gunshot wound when other established techniques have been exhausted. A corpse may be colonized by necrophagous insects following death, and this close association between the time of death and insect colonization makes entomological samples valuable evidence when remains become decomposed beyond toxicological utility. Entomotoxicology provides the potential for the identification of toxins in a decomposing corpse, with recent research uncovering the capabilities of entomotoxicology to detect gunshot residue (GSR) in a corpse. However, shortcomings of the limited literature available on this topic have not been addressed, with the bioaccumulation, detection limits, and sensitivity to gunshots not considered thus far, leaving questions as to the applicability of this new technique in the forensic context. Larvae were placed on meat contaminated with GSR at different concentrations and compared to a control meat sample to establish the uptake of GSR by the larvae, with bioaccumulation established by placing the larvae on fresh, uncontaminated meat for a period of time before analysis using ICP-MS. The findings of Pb, Ba, and Sb at each stage of the lifecycle and bioaccumulation in the larvae will be presented. In addition, throughout these previously mentioned experiments, larvae were washed once, twice and three times to evaluate the effectiveness of existing entomological practices in removing external toxins from specimens prior to entomotoxicologyical analysis. Analysis of these larval washes will be presented. By addressing these points, this research extends the utility of entomotoxicology in cause-of-death investigations and provides an additional source of evidence for forensic scientists in the circumstances involving a gunshot wound on a corpse, in addition to advising the effectiveness of current entomology collection protocols.

Keywords: bioaccumulation, chemistry, entomology, gunshot residue, toxicology

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Authors: Jui-Hsueh Hu

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This study investigated various models of refusal speech acts among three target groups: French learners of Mandarin Chinese (FM), Taiwanese native Mandarin speakers (TM), and native French speakers (NF). The refusal responses were analyzed in terms of their options, frequencies, and sequences and the contents of their semantic formulas. This study also examined differences in refusal strategies, as determined by social status and social distance, among the three groups. The difficulties of refusal speech acts encountered by FM were then generalized. The results indicated that Mandarin instructors of NF should focus on the different reasons for the pragmatic failure of French learners and should assist these learners in mastering refusal speech acts that rely on abundant cultural information. In this study, refusal policies were mainly classified according to the research of Beebe et al. (1990). Discourse completion questionnaires were collected from TM, FM, and NF, and their responses were compared to determine how refusal policies differed among the groups. This study not only emphasized the dissimilarities of refusal strategies between native Mandarin speakers and second-language Mandarin learners but also used NF as a control group. The results of this study demonstrated that regarding overall strategies, FM were biased toward NF in terms of strategy choice, order, and content, resulting in pragmatic transfer under the influence of social factors such as 'social status' and 'social distance,' strategy choices of FM were still closer to those of NF, and the phenomenon of pragmatic transfer of FM was revealed. Regarding the refusal difficulties among the three groups, the F-test in the analysis of variance revealed statistical significance was achieved for Role Playing Items 13 and 14 (P < 0.05). A difference was observed in the average number of refusal difficulties between the participants. However, after multiple comparisons, it was found that item 13 (unrecognized heterosexual junior colleague requesting contacts) was significantly more difficult for NF than for TM and FM; item 14 (contacts requested by an unrecognized classmate of the opposite sex) was significantly more difficult to refuse for NF than for TM. This study summarized the pragmatic language errors that most FM often perform, including the misuse or absence of modal words, hedging expressions, and empty words at the end of sentences, as the reasons for pragmatic failures. The common social pragmatic failures of FM include inaccurately applying the level of directness and formality.

Keywords: French Mandarin, interlanguage refusal, pragmatic transfer, speech acts

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Authors: Cecile Laval, Harriet Lowe

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Processing Instruction is a psycholinguistic pedagogical approach drawing insights from the Input Processing Model which establishes the initial innate strategies used by second language learners to connect form and meaning of linguistic features. With the ever-growing use of technology in Second Language Acquisition research, the present study uses eye-tracking to measure the effectiveness of Processing Instruction in the acquisition of French and its effects on learner’s cognitive strategies. The experiment was designed using a TOBII Pro-TX300 eye-tracker to measure participants’ default strategies when processing French linguistic input and any cognitive changes after receiving Processing Instruction treatment. Participants were drawn from lower intermediate adult learners of French at the University of Greenwich and randomly assigned to two groups. The study used a pre-test/post-test methodology. The pre-tests (one per linguistic item) were administered via the eye-tracker to both groups one week prior to instructional treatment. One group received full Processing Instruction treatment (explicit information on the grammatical item and on the processing strategies, and structured input activities) on the primary target linguistic feature (French past tense imperfective aspect). The second group received Processing Instruction treatment except the explicit information on the processing strategies. Three immediate post-tests on the three grammatical structures under investigation (French past tense imperfective aspect, French Subjunctive used for the expression of doubt, and the French causative construction with Faire) were administered with the eye-tracker. The eye-tracking data showed the positive change in learners’ processing of the French target features after instruction with improvement in the interpretation of the three linguistic features under investigation. 100% of participants in both groups made a statistically significant improvement (p=0.001) in the interpretation of the primary target feature (French past tense imperfective aspect) after treatment. 62.5% of participants made an improvement in the secondary target item (French Subjunctive used for the expression of doubt) and 37.5% of participants made an improvement in the cumulative target feature (French causative construction with Faire). Statistically there was no significant difference between the pre-test and post-test scores in the cumulative target feature; however, the variance approximately tripled between the pre-test and the post-test (3.9 pre-test and 9.6 post-test). This suggests that the treatment does not affect participants homogenously and implies a role for individual differences in the transfer-of-training effect of Processing Instruction. The use of eye-tracking provides an opportunity for the study of unconscious processing decisions made during moment-by-moment comprehension. The visual data from the eye-tracking demonstrates changes in participants’ processing strategies. Gaze plots from pre- and post-tests display participants fixation points changing from focusing on content words to focusing on the verb ending. This change in processing strategies can be clearly seen in the interpretation of sentences in both primary and secondary target features. This paper will present the research methodology, design and results of the experimental study using eye-tracking to investigate the primary effects and transfer-of-training effects of Processing Instruction. It will then provide evidence of the cognitive benefits of Processing Instruction in Second Language Acquisition and offer suggestion in second language teaching of grammar.

Keywords: eye-tracking, language teaching, processing instruction, second language acquisition

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Authors: Ahmad Helman Hamdani, Diana Putri Hamdiana

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The Sajau coal measures of Berau Basin, northeastern Kalimantan were deposited within a range of facies associations spanning a spectrum of settings from fluvial to marine. The transitional to terrestrial coal measures are dominated by siliciclastics, but they also contain three laterally extensive marine bands (mudstone). These bands act as marker horizons that enable correlation between fully marine and terrestrial facies. Examination of this range of facies and their sedimentology has enabled the development of a high-resolution sequence stratigraphic framework. Set against the established backdrop of third-order Sajau transgression, nine fourth-order sequences are recognized. Results show that, in the composite sequences, peat accumulation predominantly correlates in transitional areas with early transgressive sequence sets (TSS) and highstand sequence set (HSS), while in more landward areas it correlates with the middle TSS to late highstand sequence sets (HSS). Differences in peat accumulation regimes within the sequence stratigraphic framework are attributed to variations in subsidence and background siliciclastic input rates in different depositional settings, with these combining to produce differences in the rate of accommodation change. The preservation of coal resources in the middle to late HSS in this area was most likely related to the rise of the regional base level throughout the Sajau.

Keywords: sequence stratigraphy, coal, Pliocene, Berau basin

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Authors: Jihee Jang, Seowon Yoon, Gaeun Son, Minjung Kang, Joon Yeon Choeh, Kee-Hong Choi

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Background: Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple-choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological constructs to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that psychology due to small data sets and unvalidated modeling practices. Aims: The current article introduces the study method and procedure of phase II, which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods: The phase I (pilot) study was conducted on fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 425 Korean adults were recruited using a convenience sampling method via an online survey. The text data collected from interviews were analyzed using natural language processing. The results of the online survey, including demographic data, depression, anxiety, and personality inventories, were analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress (phase 2).

Keywords: personality prediction, psychological distress prediction, natural language processing, machine learning, the five-factor model of personality

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Authors: Sagwata A. Manyike, Hulisani Magada

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The South African National Biodiversity Institute is in the process of developing a guideline which sets out how biodiversity can be incorporated into land use (zoning) schemes. South Africa promulgated its Spatial Planning and Land Use Management Act in 2015 and the act seeks, amongst other things, to bridge the gap between spatial planning and land use management within the country. In addition, the act requires local governments to develop wall-to-wall land use schemes for their entire jurisdictions as they had previously only developed them for their urban areas. At the same time, South Africa has a rich history of systematic conservation planning whereby Critical Biodiversity Areas and Ecological Support Areas have been spatially delineated at a scale appropriate for spatial planning and land use management at the scale of local government. South Africa is also in the process of spatially delineating ecological infrastructure which is defined as naturally occurring ecosystems which provide valuable services to people such as water and climate regulation, soil formation, disaster risk reduction, etc. The Biodiversity and Land Use Project, which is funded by the Global Environmental Facility through the United Nations Development Programme is seeking to explore ways in which biodiversity information and ecological infrastructure can be incorporated into the spatial planning and land use management systems of local governments. Towards this end, the Biodiversity and Land Use Project have developed a guideline which sets out how local governments can integrate biodiversity into their land-use schemes as a way of not only ensuring sustainable development but also as a way helping them prepare for climate change. In addition, by incorporating biodiversity into land-use schemes, the project is exploring new ways of protecting biodiversity through land use schemes. The Guideline for Incorporating Biodiversity into Land Use Schemes was developed as a response to the fact that the National Land Use Scheme Guidelines only indicates that local governments needed to incorporate biodiversity without explaining how this could be achieved. The Natioanl Guideline also failed to specify which biodiversity-related layers are compatible with which land uses or what the benefits of incorporating biodiversity into the schemes will be for that local government. The guideline, therefore, sets out an argument for why biodiversity is important in land management processes and proceeds to provide a step by step guideline for how schemes can integrate priority biodiversity layers. This guideline will further be added as an addendum to the National Land Use Guidelines. Although the planning act calls for local government to have wall to wall schemes within 5 years of its enactment, many municipalities will not meet this deadline and so this guideline will support them in the development of their new schemes.

Keywords: biodiversity, climate change, land use schemes, local government

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Authors: Khadidja Rahmani, Nahla Bouaziz

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The climate change and the economic crisis, which are currently running, are the origin of the emergence of many issues and problems, which are related to the domain of energy and environment in à direct or indirect manner. Since the urban space is the core element and the key to solve the current problem, particular attention is given to it in this study. For this reason, we rented to the later a very particular attention; this is for the opportunities that it provides and that can be invested to attenuate a little this situation, which is disastrous and worried, especially in the face of the requirements of sustainable development. Indeed, the purpose of this work is to develop a method, which will allow us to guide designers towards projects with a certain degree of thermo-aeraulic comfort while requiring a minimum energy consumption. In this context, the architects, the urban planners and the engineers (energeticians) have to collaborate jointly to establish a method based on indicators for the improvement of the urban environmental quality (aeraulic-thermo comfort), correlated with a reduction in the energy demand of the entities that make up this environment, in areas with a sub-humid climate. In order to test the feasibility and to validate the method developed in this work, we carried out a series of simulations using computer-based simulation. This research allows us to evaluate the impact of the use of the indicators in the design of the urban sets, on the economic and ecological plan. Using this method, we prove that an urban design, which carefully considered energetically, can contribute significantly to the preservation of the environment and the reduction of the consumption of energy.

Keywords: comfort, energy consumption, energy mastery, morpho-energetic indicators, simulation, sub-humid climate, urban sets

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Authors: M. Miljkovic, A. Urakpo, M. Simic-Koumoutsaris

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Benefit-risk assessment (BRA) is a valuable tool that takes place in multiple stages during a medicine's lifecycle, and this assessment can be conducted in a variety of ways. The aim was to summarize current BRA methods used during approval decisions and in post-approval settings and to see possible future directions. Relevant reviews, recommendations, and guidelines published in medical literature and through regulatory agencies over the past five years have been examined. BRA implies the review of two dimensions: the dimension of benefits (determined mainly by the therapeutic efficacy) and the dimension of risks (comprises the safety profile of a drug). Regulators, industry, and academia have developed various approaches, ranging from descriptive textual (qualitative) to decision-analytic (quantitative) models, to facilitate the BRA of medicines during the product lifecycle (from Phase I trials, to authorization procedure, post-marketing surveillance and health technology assessment for inclusion in public formularies). These approaches can be classified into the following categories: stepwise structured approaches (frameworks); measures for benefits and risks that are usually endpoint specific (metrics), simulation techniques and meta-analysis (estimation techniques), and utility survey techniques to elicit stakeholders’ preferences (utilities). All these approaches share the following two common goals: to assist this analysis and to improve the communication of decisions, but each is subject to its own specific strengths and limitations. Before using any method, its utility, complexity, the extent to which it is established, and the ease of results interpretation should be considered. Despite widespread and long-time use, BRA is subject to debate, suffers from a number of limitations, and currently is still under development. The use of formal, systematic structured approaches to BRA for regulatory decision-making and quantitative methods to support BRA during the product lifecycle is a standard practice in medicine that is subject to continuous improvement and modernization, not only in methodology but also in cooperation between organizations.

Keywords: benefit-risk assessment, benefit-risk profile, product lifecycle, quantitative methods, structured approaches

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Authors: Mai A. Ibrahim, Mohammed El-Beltagy, Motaz Khorshid

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Emerging Markets return distributions have shown significance departure from normality were they are characterized by fatter tails relative to the normal distribution and exhibit levels of skewness and kurtosis that constitute a significant departure from normality. Therefore, the classical Markowitz Mean-Variance is not applicable for emerging markets since it assumes normally-distributed returns (with zero skewness and kurtosis) and a quadratic utility function. Moreover, the Markowitz mean-variance analysis can be used in cases of moderate non-normality and it still provides a good approximation of the expected utility, but it may be ineffective under large departure from normality. Higher moments models and median models have been suggested in the literature for asset allocation in this case. Higher moments models have been introduced to account for the insufficiency of the description of a portfolio by only its first two moments while the median model has been introduced as a robust statistic which is less affected by outliers than the mean. Tail risk measures such as Value-at Risk (VaR) and Conditional Value-at-Risk (CVaR) have been introduced instead of Variance to capture the effect of risk. In this research, higher moment models including the Mean-Variance-Skewness (MVS) and Mean-Variance-Skewness-Kurtosis (MVSK) are formulated as single-objective non-linear programming problems (NLP) and median models including the Median-Value at Risk (MedVaR) and Median-Mean Absolute Deviation (MedMAD) are formulated as a single-objective mixed-integer linear programming (MILP) problems. The higher moment models and median models are compared to some benchmark portfolios and tested on real financial data in the Egyptian main Index EGX30. The results show that all the median models outperform the higher moment models were they provide higher final wealth for the investor over the entire period of study. In addition, the results have confirmed the inapplicability of the classical Markowitz Mean-Variance to the Egyptian stock market as it resulted in very low realized profits.

Keywords: Egyptian stock exchange, emerging markets, higher moment models, median models, mixed-integer linear programming, non-linear programming

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Sinead Impey, Damon Berry, Selma Furtado, Miriam Galvin, Loretto Grogan, Orla Hardiman, Lucy Hederman, Mark Heverin, Vincent Wade, Linda Douris, Declan O'Sullivan, Gaye Stephens

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Healthcare is a knowledge-rich environment. This knowledge, while valuable, is not always accessible outside the borders of individual clinics. This research aims to address part of this problem (at a study site) by constructing a maximal data set (knowledge artefact) for motor neurone disease (MND). This data set is proposed as an initial knowledge base for a concurrent project to develop an MND patient data platform. It represents the domain knowledge at the study site for the duration of the research (12 months). A knowledge elicitation method was also developed from the lessons learned during this process - the WICKED method. WICKED is an anagram of the words: eliciting and confirming data, information, knowledge, wisdom. But it is also a reference to the concept of wicked problems, which are complex and challenging, as is eliciting expert knowledge. The method was evaluated at a second site, and benefits and limitations were noted. Benefits include that the method provided a systematic way to manage data, information, knowledge and wisdom (DIKW) from various sources, including healthcare specialists and existing data sets. Limitations surrounded the time required and how the data set produced only represents DIKW known during the research period. Future work is underway to address these limitations.

Keywords: healthcare, knowledge acquisition, maximal data sets, action design science

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Authors: Katarzyna Wyka, Dana Sylvan, JoAnn Difede

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Background and significance: The following disaster, population-based screening programs are routinely established to assess physical and psychological consequences of exposure. These data sets are highly skewed as only a small percentage of trauma-exposed individuals develop health issues. Commonly used statistical methodology in post-disaster mental health generally involves population-averaged models. Such models aim to capture the overall response to the disaster and its aftermath; however, they may not be sensitive enough to accommodate population heterogeneity in symptomatology, such as post-traumatic stress or depressive symptoms. Methods: We use an archival longitudinal data set from Weill-Cornell 9/11 Mental Health Screening Program established following the World Trade Center (WTC) terrorist attacks in New York in 2001. Participants are rescue and recovery workers who participated in the site cleanup and restoration (n=2960). The main outcome is the post-traumatic stress symptoms (PTSD) severity score assessed via clinician interviews (CAPS). For a detailed understanding of response to the disaster and its aftermath, we are adapting quantile regression methodology with particular focus on predictors of extreme distress and resilience to trauma. Results: The response variable was defined as the quantile of the CAPS score for each individual under two different scenarios specifying the unconditional quantiles based on: 1) clinically meaningful CAPS cutoff values and 2) CAPS distribution in the population. We present graphical summaries of the differential effects. For instance, we found that the effect of the WTC exposures, namely seeing bodies and feeling that life was in danger during rescue/recovery work was associated with very high PTSD symptoms. A similar effect was apparent in individuals with prior psychiatric history. Differential effects were also present for age and education level of the individuals. Conclusion: We evaluate the utility of quantile regression in disaster research in contrast to the commonly used population-averaged models. We focused on assessing the distribution of risk factors for post-traumatic stress symptoms across quantiles. This innovative approach provides a comprehensive understanding of the relationship between dependent and independent variables and could be used for developing tailored training programs and response plans for different vulnerability groups.

Keywords: disaster workers, post traumatic stress, PTSD, quantile regression

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Authors: Sarah K. Hagi, Majdi Alnowaimi

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In the last decade, there were immense advancements in the medical imaging modalities. These advancements can scan a whole volume of the lung organ in high resolution images within a short time. According to this performance, the physicians can clearly identify the complicated anatomical and pathological structures of lung. Therefore, these advancements give large opportunities for more advance of all types of lung cancer treatment available and will increase the survival rate. However, lung cancer is still one of the major causes of death with around 19% of all the cancer patients. Several factors may affect survival rate. One of the serious effects is the breathing process, which can affect the accuracy of diagnosis and lung tumor treatment plan. We have therefore developed a semi automated algorithm to localize the 3D lung tumor positions across all respiratory data during respiratory motion. The algorithm can be divided into two stages. First, a lung tumor segmentation for the first phase of the 4D computed tomography (CT). Lung tumor segmentation is performed using an active contours method. Then, localize the tumor 3D position across all next phases using a 12 degrees of freedom of an affine transformation. Two data set where used in this study, a compute simulate for 4D CT using extended cardiac-torso (XCAT) phantom and 4D CT clinical data sets. The result and error calculation is presented as root mean square error (RMSE). The average error in data sets is 0.94 mm ± 0.36. Finally, evaluation and quantitative comparison of the results with a state-of-the-art registration algorithm was introduced. The results obtained from the proposed localization algorithm show a promising result to localize alung tumor in 4D CT data.

Keywords: automated algorithm , computed tomography, lung tumor, tumor localization

Procedia PDF Downloads 597

Authors: Godwin Dennison, C. E. Boulind, O. Gould, B. de Lacy Costello, J. Allison, P. White, P. Ewings, A. Wicaksono, N. J. Curtis, A. Pullyblank, D. Jayne, J. A. Covington, N. Ratcliffe, N. K. Francis

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Background: Colorectal symptoms are common but only infrequently represent serious pathology, including colorectal cancer (CRC). A large number of invasive tests are presently performed for reassurance. We investigated the feasibility of urinary volatile organic compound (VOC) testing as a potential triage tool in patients fast-tracked for assessment for possible CRC. Methods: A prospective, multi-centre, observational feasibility study was performed across three sites. Patients referred on NHS fast-track pathways for potential CRC provided a urine sample which underwent Gas Chromatography Mass Spectrometry (GC-MS), Field Asymmetric Ion Mobility Spectrometry (FAIMS) and Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) analysis. Patients underwent colonoscopy and/or CT colonography and were grouped as either CRC, adenomatous polyp(s), or controls to explore the diagnostic accuracy of VOC output data supported by an artificial neural network (ANN) model. Results: 558 patients participated with 23 (4.1%) CRC diagnosed. 59% of colonoscopies and 86% of CT colonographies showed no abnormalities. Urinary VOC testing was feasible, acceptable to patients, and applicable within the clinical fast track pathway. GC-MS showed the highest clinical utility for CRC and polyp detection vs. controls (sensitivity=0.878, specificity=0.882, AUROC=0.884). Conclusion: Urinary VOC testing and analysis are feasible within NHS fast-track CRC pathways. Clinically meaningful differences between patients with cancer, polyps, or no pathology were identified therefore suggesting VOC analysis may have future utility as a triage tool. Acknowledgment: Funding: NIHR Research for Patient Benefit grant (ref: PB-PG-0416-20022).

Keywords: colorectal cancer, volatile organic compound, gas chromatography mass spectrometry, field asymmetric ion mobility spectrometry, selected ion flow tube mass spectrometry

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Fatma Khadra

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The objective of this research is to identify the acceptance of new technology in a sample of 150 Participants from Qatar University. Based on the Technology Acceptance Model (TAM), we used the Davis’s scale (1989) which contains two item scales for Perceived Usefulness and Perceived Ease of Use. The TAM represents an important theoretical contribution toward understanding how users come to accept and use technology. This model suggests that when people are presented with a new technology, a number of variables influence their decision about how and when they will use it. The results showed that participants accept more technology because flexibility, clarity, enhancing the experience, enjoying, facility, and useful. Also, results showed that younger participants accept more technology than others.

Keywords: new technology, perceived usefulness, perceived ease of use, technology acceptance model

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: J. D. Marinaccio, I. Mabbett, C. Glover, D. Worsley

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Through use of novel modern/rapid processing techniques such as screen printing and Near-Infrared (NIR) radiative curing, process time for the sintering of sintered nickel plaques, applicable to alkaline nickel battery chemistries, has been drastically reduced from in excess of 200 minutes with conventional convection methods to below 2 minutes using NIR curing methods. Steps have also been taken to remove the need for forming gas as a reducing agent by implementing carbon as an in-situ reducing agent, within the ink formulation.

Keywords: batteries, energy, iron, nickel, storage

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Authors: Sanjivrao Katakam, Thanumoorthi I., Antony Gerald, Ratan Kulkarni, Shaju Nair

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With increasing energy costs and its impact on the business, sustainability today has evolved from a social expectation to an economic imperative. Therefore, finding methods to reduce cost has become a critical directive for Industry leaders. Effective energy management is the only way to cut costs. However, Energy Management has been a challenge because it requires a change in old habits and legacy systems followed for decades. Today exorbitant levels of energy and operational data is being captured and stored by Industries, but they are unable to convert these structured and unstructured data sets into meaningful business intelligence. It must be noted that for quick decisions, organizations must learn to cope with large volumes of operational data in different formats. Energy analytics not only helps in extracting inferences from these data sets, but also is instrumental in transformation from old approaches of energy management to new. This in turn assists in effective decision making for implementation. It is the requirement of organizations to have an established corporate strategy for reducing operational costs through visibility and optimization of energy usage. Energy analytics play a key role in optimization of operations. The paper describes how today energy data analytics is extensively used in different scenarios like reducing operational costs, predicting energy demands, optimizing network efficiency, asset maintenance, improving customer insights and device data insights. The paper also highlights how analytics helps transform insights obtained from energy data into sustainable solutions. The paper utilizes data from an array of segments such as retail, transportation, and water sectors.

Keywords: energy analytics, energy management, operational data, business intelligence, optimization

Procedia PDF Downloads 360