Search results for: time series prediction

Authors: Guo Wenyu, Qu Youli

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A practical and simple self-indexing data structure, Partitioned Elias-Fano (PEF) - Compressed Suffix Arrays (CSA), is built in linear time for the CSA based on PEF indexes. Moreover, the PEF-CSA is compared with two classical compressed indexing methods, Ferragina and Manzini implementation (FMI) and Sad-CSA on different type and size files in Pizza & Chili. The PEF-CSA performs better on the existing data in terms of the compression ratio, count, and locates time except for the evenly distributed data such as proteins data. The observations of the experiments are that the distribution of the φ is more important than the alphabet size on the compression ratio. Unevenly distributed data φ makes better compression effect, and the larger the size of the hit counts, the longer the count and locate time.

Keywords: compressed suffix array, self-indexing, partitioned Elias-Fano, PEF-CSA

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Authors: Abderafi Charki, David Bigaud

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The aim of this oral speach is to present the PVMODREL© (PhotoVoltaic MODule RELiability) new software developed in the University of Angers. This new tool permits us to evaluate the lifetime and reliability of a PV module whatever its geographical location and environmental conditions. The electrical power output of a PV module decreases with time mainly as a result of the effects of corrosion, encapsulation discoloration, and solder bond failure. The failure of a PV module is defined as the point where the electrical power degradation reaches a given threshold value. Accelerated life tests (ALTs) are commonly used to assess the reliability of a PV module. However, ALTs provide limited data on the failure of a module and these tests are expensive to carry out. One possible solution is to conduct accelerated degradation tests. The Wiener process in conjunction with the accelerated failure time model makes it possible to carry out numerous simulations and thus to determine the failure time distribution based on the aforementioned threshold value. By this means, the failure time distribution and the lifetime (mean and uncertainty) can be evaluated. An example using the damp heat test is shown to demonstrate the usefulness PVMODREL.

Keywords: lifetime, reliability, PV Module, accelerated life testing, accelerated degradation testing

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Authors: Emmanuel Yussuf, John Muthama, John Ng'ang'A

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There has been increasing noise levels all over the world in the last decade. Many factors contribute to this increase, which is causing health related effects to humans. Developing countries are not left out of the whole picture as they are still growing and advancing their development. Motor vehicles are increasing on urban roads; there is an increase in infrastructure due to the rising population, increasing number of industries to provide goods and so many other activities. All this activities lead to the high noise levels in cities. This study was conducted in Nairobi’s Central Business District (CBD) with the main objective of simulating noise levels in order to understand the noise exposed to the people within the urban area, in relation to weather parameters namely temperature, rainfall and wind field. The study was achieved using the Neighbourhood Proximity Model and Time Series Analysis, with data obtained from proxies/remotely-sensed from satellites, in order to establish the levels of noise exposed to which people of Nairobi CBD are exposed to. The findings showed that there is an increase in temperature (0.1°C per year) and a decrease in precipitation (40 mm per year), which in comparison to the noise levels in the area, are increasing. The study also found out that noise levels exposed to people in Nairobi CBD were roughly between 61 and 63 decibels and has been increasing, a level which is high and likely to cause adverse physical and psychological effects on the human body in which air temperature, precipitation and wind contribute so much in the spread of noise. As a noise reduction measure, the use of sound proof materials in buildings close to busy roads, implementation of strict laws to most emitting sources as well as further research on the study was recommended. The data used for this study ranged from the year 2000 to 2015, rainfall being in millimeters (mm), temperature in degrees Celsius (°C) and the urban form characteristics being in meters (m).

Keywords: simulation, noise exposure, weather, proxy

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Authors: Pierre van Tonder, Dinesh Muthoo, Kim twiname

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Many existing and newly built structures are constructed on the design basis of the engineer and the workmanship of the construction company. However, when considering larger structures where more people are exposed to the building, its structural integrity is of great importance considering the safety of its occupants (Raghu, 2013). But how can the structural integrity of a building be monitored efficiently and effectively. This is where the fourth industrial revolution step in, and with minimal human interaction, data can be collected, analysed, and stored, which could also give an indication of any inconsistencies found in the data collected, this is where the Fibre Bragg Grating (FBG) monitoring system is introduced. This paper illustrates how data can be collected and converted to develop stress – strain behaviour and to produce bending moment diagrams for the utilisation and prediction of the structure’s integrity. Embedded fibre optic sensors were used in this study– fibre Bragg grating sensors in particular. The procedure entailed making use of the shift in wavelength demodulation technique and an inscription process of the phase mask technique. The fibre optic sensors considered in this report were photosensitive and embedded in the slab and beams for data collection and analysis. Two sets of fibre cables have been inserted, one purposely to collect temperature recordings and the other to collect strain and temperature. The data was collected over a time period and analysed used to produce bending moment diagrams to make predictions of the structure’s integrity. The data indicated the fibre Bragg grating sensing system proved to be useful and can be used for structural health monitoring in any environment. From the experimental data for the slab and beams, the moments were found to be64.33 kN.m, 64.35 kN.m and 45.20 kN.m (from the experimental bending moment diagram), and as per the idealistic (Ultimate Limit State), the data of 133 kN.m and 226.2 kN.m were obtained. The difference in values gave room for an early warning system, in other words, a reserve capacity of approximately 50% to failure.

Keywords: fibre bragg grating, structural health monitoring, fibre optic sensors, beams

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Authors: Jialin Wu, Zhiwei Lian, Jieyu Tang, Jingyun Shen

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Tornado is a serious and unpredictable natural disaster, which has an important impact on people's production and life. The probability of being hit by tornadoes in China was analyzed considering the principles of tornado formation. Then some suggestions on layout and shapes for newly-built buildings were provided combined with the characteristics of tornado wind fields. Fuzzy clustering and inverse closeness methods were used to evaluate the probability levels of tornado risks in various provinces based on classification and ranking. GIS was adopted to display the results. Finally, wind field single-vortex tornado was studied to discuss the optimized design of rural low-rise houses in Yancheng, Jiangsu as an example. This paper may provide enough data to support building and urban design in some specific regions.

Keywords: tornado probability, computational fluid dynamics, fuzzy mathematics, optimal design

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Authors: Mohammad Asadpour, Maryam Sadeghi, Mahmoud Jafari

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The transformation of carbon dioxide into fuels and commodity chemicals is considered one of the most attractive methods to meet energy demands and reduce atmospheric CO₂ levels. Cobalt complexes have previously shown high faradaic efficiency in the reduction of CO₂ to CO. In this study, a nanostructure, referred to as a porphyrin-like buckyball, is simulated and analyzed for its electrical properties. The investigation aims to understand the unique characteristics of this material and its potential applications in electronic devices. Through computational simulations and analysis, the electrocatalytic reduction of CO₂ to CO by Cobalt-porphyrin-like buckyball is explored. The findings of this study offer valuable insights into the electrocatalytic properties of this predicted structure, paving the way for further research and development in the field of nanotechnology.

Keywords: porphyrin-like buckyball, DFT, nanomaterials, CO₂ to CO

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Authors: W. Staiano, S. Marcora

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Introduction: It has been proposed that acute negative effect of mental fatigue (MF) could potentially become a training stimulus for the brain (Brain endurance training (BET)) to adapt and improve its ability to attenuate MF states during sport competitions. Purpose: The aim of this study was to test the efficacy of 6 weeks of BET on cognitive and cycling tests in a group of well-trained subjects. We hypothesised that combination of BET and standard physical training (SPT) would increase cognitive capacity and cycling performance by reducing rating of perceived exertion (RPE) and increase resilience to fatigue more than SPT alone. Methods: In a randomized controlled trial design, 26 well trained participants, after a familiarization session, cycled to exhaustion (TTE) at 80% peak power output (PPO) and, after 90 min rest, at 65% PPO, before and after random allocation to a 6 week BET or active placebo control. Cognitive performance was measured using 30 min of STROOP coloured task performed before cycling performance. During the training, BET group performed a series of cognitive tasks for a total of 30 sessions (5 sessions per week) with duration increasing from 30 to 60 min per session. Placebo engaged in a breathing relaxation training. Both groups were monitored for physical training and were naïve to the purpose of the study. Physiological and perceptual parameters of heart rate, lactate (LA) and RPE were recorded during cycling performances, while subjective workload (NASA TLX scale) was measured during the training. Results: Group (BET vs. Placebo) x Test (Pre-test vs. Post-test) mixed model ANOVA’s revealed significant interaction for performance at 80% PPO (p = .038) or 65% PPO (p = .011). In both tests, groups improved their TTE performance; however, BET group improved significantly more compared to placebo. No significant differences were found for heart rate during the TTE cycling tests. LA did not change significantly at rest in both groups. However, at completion of 65% TTE, it was significantly higher (p = 0.043) in the placebo condition compared to BET. RPE measured at ISO-time in BET was significantly lower (80% PPO, p = 0.041; 65% PPO p= 0.021) compared to placebo. Cognitive results in the STROOP task showed that reaction time in both groups decreased at post-test. However, BET decreased significantly (p = 0.01) more compared to placebo despite no differences accuracy. During training sessions, participants in the BET showed, through NASA TLX questionnaires, constantly significantly higher (p < 0.01) mental demand rates compared to placebo. No significant differences were found for physical demand. Conclusion: The results of this study provide evidences that combining BET and SPT seems to be more effective than SPT alone in increasing cognitive and cycling performance in well trained endurance participants. The cognitive overload produced during the 6-week training of BET can induce a reduction in perception of effort at a specific power, and thus improving cycling performance. Moreover, it provides evidence that including neurocognitive interventions will benefit athletes by increasing their mental resilience, without affecting their physical training load and routine.

Keywords: cognitive training, perception of effort, endurance performance, neuro-performance

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Authors: Aena Iqbal

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The lack of access to maternal and reproductive health in Pakistan poses a great threat to global public health. Obstetric issues, including vesicovaginal fistulas (VVF), are the most common in South Asian countries, leaving women in a more vulnerable state. Koohi Goth Women’s Hospital offers free VVF operations, which draws in women from all over Pakistan. Although reproductive health is being handled, mental health is often neglected in these scenarios. Using a series of questions inspired by the Warwick Edinburgh Model, this paper builds on the results from interviewing women who have received vesicovaginal fistula repair surgery on their mental health, a taboo topic in Pakistan.

Keywords: obstetrics, VVF, Pakistan, reproductive health

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Authors: Jihuai Wu, Zeyu Song, Zhang Lan, Liuxue Sun

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Solar energy is clean, open, and infinite, but solar radiation on the earth is fluctuating, intermittent, and unstable. So, the sustainable utilization of solar energy requires a combination of high-efficient energy conversion and low-loss energy storage technologies. Hence, a photo capacitor integrated with photo-electrical conversion and electric-chemical storage functions in single device is a cost-effective, volume-effective and functional-effective optimal choice. However, owing to the multiple components, multi-dimensional structure and multiple functions in one device, especially the mismatch of the functional modules, the overall conversion and storage efficiency of the photocapacitors is less than 13%, which seriously limits the development of the integrated system of solar conversion and energy storage. To this end, two typical photocapacitors were studied. A three-terminal photocapacitor was integrated by using perovskite solar cell as solar conversion module and symmetrical supercapacitor as energy storage module. A function portfolio management concept was proposed the relationship among various efficiencies during photovoltaic conversion and energy storage process were clarified. By harmonizing the energy matching between conversion and storage modules and seeking the maximum power points coincide and the maximum efficiency points synchronize, the overall efficiency of the photocapacitor surpassed 18 %, and Joule efficiency was closed to 90%. A voltage adjustable hybrid supercapacitor (VAHSC) was designed as energy storage module, and two Si wafers in series as solar conversion module, a three-terminal photocapacitor was fabricated. The VAHSC effectively harmonizes the energy harvest and storage modules, resulting in the current, voltage, power, and energy match between both modules. The optimal photocapacitor achieved an overall efficiency of 15.49% and Joule efficiency of 86.01%, along with excellent charge/discharge cycle stability. In addition, the Joule efficiency (ηJoule) was defined as the energy ratio of discharge/charge of the devices for the first time.

Keywords: joule efficiency, perovskite solar cell, photocapacitor, silicon solar cell, supercapacitor

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Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

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Authors: Ahmet Erdoğan, Şahin Kesici, Mustafa Baloğlu

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Information technologies have lead to changes in the areas of communication, learning, and teaching. Besides offering many opportunities to the learners, these technologies have changed the teaching methods and beliefs of teachers. What the Technological Pedagogical Content Knowledge (TPACK) means to the teachers is considerably important to integrate technology successfully into teaching processes. It is necessary to understand how to plan and apply teacher training programs in order to balance students’ pedagogical and technological knowledge. Because of many inefficient teacher training programs, teachers have difficulties in relating technology, pedagogy and content knowledge each other. While providing an efficient training supported with technology, understanding the three main components (technology, pedagogy and content knowledge) and their relationship are very crucial. The purpose of this study is to determine whether motivational beliefs and self-regulated learning strategies are significant predictors of mathematics teacher candidates' TPACK perceptions. A hundred seventy five Turkish mathematics teachers candidates responded to the Motivated Strategies for Learning Questionnaire (MSLQ) and the Technological Pedagogical And Content Knowledge (TPACK) Scale. Of the group, 129 (73.7%) were women and 46 (26.3%) were men. Participants' ages ranged from 20 to 31 years with a mean of 23.04 years (SD = 2.001). In this study, a multiple linear regression analysis was used. In multiple linear regression analysis, the relationship between the predictor variables, mathematics teacher candidates' motivational beliefs, and self-regulated learning strategies, and the dependent variable, TPACK perceptions, were tested. It was determined that self-efficacy for learning and performance and intrinsic goal orientation are significant predictors of mathematics teacher candidates' TPACK perceptions. Additionally, mathematics teacher candidates' critical thinking, metacognitive self-regulation, organisation, time and study environment management, and help-seeking were found to be significant predictors for their TPACK perceptions.

Keywords: candidate mathematics teachers, motivational beliefs, self-regulated learning strategies, technological and pedagogical knowledge, content knowledge

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Authors: Shadab Shishegar, Sophie Duchesne, Genevieve Pelletier

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Sustainability as one of the key elements of Smart cities, can be realized by employing Real Time Control Strategies for city’s infrastructures. Nowadays Stormwater management systems play an important role in mitigating the impacts of urbanization on natural hydrological cycle. These systems can be managed in such a way that they meet the smart cities standards. In fact, there is a huge potential for sustainable management of urban stormwater and also its adaptability to global challenges like climate change. Hence, a dynamically managed system that can adapt itself to instability of the environmental conditions is desirable. A Global Predictive Real Time Control approach is proposed in this paper to optimize the performance of stormwater management basins in terms of flooding prevention. To do so, a mathematical optimization model is developed then solved using Genetic Algorithm (GA). Results show an improved performance at system-level for the stormwater basins in comparison to static strategy.

Keywords: environmental sustainability, optimization, real time control, storm water management

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Faizan Khan, Xiaodong Yang

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Internet Service Providers are driving endless demands for higher bandwidth and data throughput as new services and applications require higher bandwidth. Users want immediate and accurate data delivery. This article focuses on converting old conventional networks into passive optical networks based on time division and wavelength division multiplexing. The main focus of this research is to use a hybrid of time-division multiplexing and wavelength-division multiplexing to improve network efficiency and performance. In this paper, we design an 80 Gbps Passive Optical Network (PON), which meets the need of the Next Generation PON Stage 2 (NGPON2) proposed in this paper. The hybrid of the Time and Wavelength division multiplexing (TWDM) is said to be the best solution for the implementation of NGPON2, according to Full-Service Access Network (FSAN). To co-exist with or replace the current PON technologies, many wavelengths of the TWDM can be implemented simultaneously. By utilizing 8 pairs of wavelengths that are multiplexed and then transmitted over optical fiber for 40 Kms and on the receiving side, they are distributed among 256 users, which shows that the solution is reliable for implementation with an acceptable data rate. From the results, it can be concluded that the overall performance, Quality Factor, and bandwidth of the network are increased, and the Bit Error rate is minimized by the integration of this approach.

Keywords: bit error rate, fiber to the home, passive optical network, time and wavelength division multiplexing

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Authors: M. Pramila Devi, N. V. N. Indra Kiran

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Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point’s exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed.

Keywords: control chart pattern recognition, neural network, backpropagation, generalization, early stopping

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Authors: Yeliz Ekinci, Melis Koyuncu

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Successful planning of solid waste management systems requires successful prediction of the amount of solid waste generated in an area. Waste management planning can protect the environment and human health, hence it is tremendously important for countries. The lack of information in waste generation can cause many environmental and health problems. Turkey is a country that plans to join European Union, hence, solid waste management is one of the most significant criteria that should be handled in order to be a part of this community. Solid waste management system requires a good forecast of solid waste generation. Thus, this study aims to forecast solid waste generation in Turkey. Artificial Neural Network and Linear Regression models will be used for this aim. Many models will be run and the best one will be selected based on some predetermined performance measures.

Keywords: forecast, solid waste generation, solid waste management, Turkey

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Authors: Gila Kurtz, Dan Kohen Vacs

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Social robots (SR) is an emerging field striving to deploy computers capable of resembling human shapes and mimicking human movements, gestures, and behaviors. The evolving capability of SR to interact with human offers groundbreaking ways for learning and training opportunities. Studies show that SR can offer instructional experiences for fostering creativity, entertainment, enjoyment, and curiosity. These added values are essential for empowering instructional opportunities as gamified learning experiences. We present our project focused on deploying an activity to be experienced in an escape room aimed at team-based learning scaffolded by an SR, NAO. An escape room is a well-known approach for gamified activities focused on a simulated scenario experienced by team-based participants. Usually, the simulation takes place in a physical environment where participants must complete a series of challenges in a limited amount of time. During this experience, players learn something about the assigned topic of the room. In the current learning simulation, students must "save the nation" by locating sensitive information stolen and stored in a vault of four locks. Team members have to look for hints and solve riddles mediated by NAO. Each solution provides a unique code for opening one of the four locks. NAO is also used to provide ongoing feedback on the team's performance. We captured the proceeding of our activity and used it to conduct an evaluation study among ten experts in related areas. The experts were interviewed on their overall assessment of the learning activity and their perception of the added value related to the robot. The results were very encouraging on the feasibility that NAO can serve as a motivational tutor in adults' collaborative game-based learning. We believe that this study marks the first step toward a template for developing innovative team-based training using escape rooms supported by a humanoid robot.

Keywords: social robot, NAO, learning, team based activity, escape room

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Authors: Hatem Hajri, Mohamed-Cherif Rahal

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Both Lidars and Radars are sensors for obstacle detection. While Lidars are very accurate on obstacles positions and less accurate on their velocities, Radars are more precise on obstacles velocities and less precise on their positions. Sensor fusion between Lidar and Radar aims at improving obstacle detection using advantages of the two sensors. The present paper proposes a real-time Lidar/Radar data fusion algorithm for obstacle detection and tracking based on the global nearest neighbour standard filter (GNN). This algorithm is implemented and embedded in an automative vehicle as a component generated by a real-time multisensor software. The benefits of data fusion comparing with the use of a single sensor are illustrated through several tracking scenarios (on a highway and on a bend) and using real-time kinematic sensors mounted on the ego and tracked vehicles as a ground truth.

Keywords: ground truth, Hungarian algorithm, lidar Radar data fusion, global nearest neighbor filter

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Authors: Ramūnas Antanaitis, Lina Anskienė, Robertas Stoškus

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This study was conducted during 2021.05.01 – 2021.08.31 at the Lithuanian University of health sciences and one Lithuanian dairy farm with 500 dairy cows (55.911381565736, 21.881321760608195). Average calving – 50 cows per month. Cows (n=20) in the treatment group (TG) were fed with feed supplement Optipartum C+ 200 (Enzymes: Alfa- Amylase 57 Units; Beta-Glucanase 107 Units) from 21 days before calving till 30 days after calving with feeding rate 200g/cow/day. Cows in the control group (CG) were fed a feed ration without feed supplement. Measurements started from 6 days before calving and continued till 21 days after calving. The following indicators were registered: with the RumiWatch System: Rumination time; Eating time; Drinking time; Rumination chews; Eating chews; Drinking gulps; Bolus; Chews per minute; Chews per bolus. With SmaXtec system - the temperature, pH of the contents of cows' reticulorumens and cows' activity. According to our results, we found that feeding of cows, from 21 days before calving to 30 days after calving, with a feed supplement with alfa- amylase and beta-glucanase (Optipartum C+ 200) (with dose 200g/cow/day) can produce an increase in: 9% rumination time and eating time, 19% drinking time, 11% rumination chews, 16% eating chews,13% number of boluses per rumination, 5% chews per minute and 16% chews per bolus. We found 1.28 % lower reiticulorumen pH and 0.64% lower reticulorumen temperature in cows fed with the supplement compared with control group cows. Also, cows feeding with enzymes were 8.80% more active.

Keywords: Alfa-Amylase, Beta-Glucanase, cows, in-line, sensors

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Authors: Ruixia Liu

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Qinghai-Tibet Plateau has an important influence on the precipitation of its lower reaches. Data from remote sensing has itself advantage and numerical prediction model which assimilates RS data will be better than other. We got the assimilation data of MHS and terrestrial and sounding from GSI, and introduced the result into WRF, then got the result of RH and precipitation forecast. We found that assimilating MHS and terrestrial and sounding made the forecast on precipitation, area and the center of the precipitation more accurate by comparing the result of 1h,6h,12h, and 24h. Analyzing the difference of the initial field, we knew that the data assimilating about Qinghai-Tibet Plateau influence its lower reaches forecast by affecting on initial temperature and RH.

Keywords: Qinghai-Tibet Plateau, precipitation, data assimilation, GSI

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Authors: L. Guevara, Glória L. S., Corea E. E, A. Ramírez-Zamora M., Salinas-Martinez J. A., Angeles-Hernandez J. C.

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Mathematical models are useful for identifying the characteristics of sheep lactation curves to develop and implement improved strategies. However, the accuracy of these models is influenced by factors such as the recording regime, mainly the intervals between test day records (TDR). The current study aimed to evaluate the effect of different TDR intervals on the goodness of fit of the Wood model (WM) applied to dairy sheep lactations. A total of 4,494 weekly TDRs from 156 lactations of dairy crossbred sheep were analyzed. Three new databases were generated from the original weekly TDR data (7D), comprising intervals of 14(14D), 21(21D), and 28(28D) days. The parameters of WM were estimated using the “minpack.lm” package in the R software. The shape of the lactation curve (typical and atypical) was defined based on the WM parameters. The goodness of fit was evaluated using the mean square of prediction error (MSPE), Root of MSPE (RMSPE), Akaike´s Information Criterion (AIC), Bayesian´s Information Criterion (BIC), and the coefficient of correlation (r) between the actual and estimated total milk yield (TMY). WM showed an adequate estimate of TMY regardless of the TDR interval (P=0.21) and shape of the lactation curve (P=0.42). However, we found higher values of r for typical curves compared to atypical curves (0.9vs.0.74), with the highest values for the 28D interval (r=0.95). In the same way, we observed an overestimated peak yield (0.92vs.6.6 l) and underestimated time of peak yield (21.5vs.1.46) in atypical curves. The best values of RMSPE were observed for the 28D interval in both lactation curve shapes. The significant lowest values of AIC (P=0.001) and BIC (P=0.001) were shown by the 7D interval for typical and atypical curves. These results represent the first approach to define the adequate interval to record the regime of dairy sheep in Latin America and showed a better fitting for the Wood model using a 7D interval. However, it is possible to obtain good estimates of TMY using a 28D interval, which reduces the sampling frequency and would save additional costs to dairy sheep producers.

Keywords: gamma incomplete, ewes, shape curves, modeling

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Authors: Okay Gunes

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Unsatisfactory experiences due to an information shortage regarding the future pay-offs of actual choices, yield satisficing decision-making. This research will examine, for the first time in the literature, the motivation behind suboptimal decisions due to uncertainty by subjecting Adam Smith’s and Jeremy Bentham’s assumptions about the nature of the actions that lead to satisficing behavior, in order to clarify the theoretical background of a “consumption-based satisfactory time” concept. The contribution of this paper with respect to the existing literature is threefold: Firstly, it is showed in this paper that Adam Smith’s uncertainty is related to the problem of the constancy of ideas and not related directly to beliefs. Secondly, possessions, as in Jeremy Bentham’s oeuvre, are assumed to be just as pleasing, as protecting and improving the actual or expected quality of life, so long as they reduce any displeasure due to the undesired outcomes of uncertainty. Finally, each consumption decision incurs its own satisfactory time period, owed to not feeling hungry, being healthy, not having transportation…etc. This reveals that the level of satisfaction is indeed a behavioral phenomenon where its value would depend on the simultaneous satisfaction derived from all activities.

Keywords: decision-making, idea and belief, satisficing, uncertainty

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Authors: Hala Raslan, Noha Eltaweel, Hanaa Rasmi, Solaf Kamel, May Magdy, Sherif Ismail, Khalda Amr

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Introduction: Rheumatoid arthritis (RA) is an inflammatory, autoimmune disorder with progressive joint damage. Osteoarthritis (OA) is a degenerative disease of the articular cartilage that shows multiple clinical manifestations or symptoms resembling those of RA. Genetic predisposition is believed to be a principal etiological factor for RA and OA. In this study, we aimed to measure the expression of the top deregulated miRNAs that might be the cause of pathogenesis in both diseases, according to our latest NGS analysis. Six of the deregulated miRNAs were selected as they had multiple target genes in the RA pathway, so they are more likely to affect the RA pathogenesis.Methods: Eighty cases were recruited in this study; 45 rheumatoid arthiritis (RA), 30 osteoarthiritis (OA) patients, as well as 20 healthy controls. The selection of the miRNAs from our latest NGS study was done using miRwalk according to the number of their target genes that are members in the KEGG RA pathway. Total RNA was isolated from plasma of all recruited cases. The cDNA was generated by the miRcury RT Kit then used as a template for real-time PCR with miRcury Primer Assays and the miRcury SYBR Green PCR Kit. Fold changes were calculated from CT values using the ΔΔCT method of relative quantification. Results were compared RA vs Controls and OA vs Controls. Target gene prediction and functional annotation of the deregulated miRNAs was done using Mienturnet. Results: Six miRNAs were selected. They were miR-15b-3p, -128-3p, -194-3p, -328-3p, -542-3p and -3180-5p. In RA samples, three of the measured miRNAs were upregulated (miR-194, -542, and -3180; mean Rq= 2.6, 3.8 and 8.05; P-value= 0.07, 0.05 and 0.01; respectively) while the remaining 3 were downregulated (miR-15b, -128 and -328; mean Rq= 0.21, 0.39 and 0.6; P-value= <0.0001, <0.0001 and 0.02; respectively) all with high statistical significance except miR-194. While in OA samples, two of the measured miRNAs were upregulated (miR-194 and -3180; mean Rq= 2.6 and 7.7; P-value= 0.1 and 0.03; respectively) while the remaining 4 were downregulated (miR-15b, -128, -328 and -542; mean Rq= 0.5, 0.03, 0.08 and 0.5; P-value= 0.0008, 0.003, 0.006 and 0.4; respectively) with statistical significance compared to controls except miR-194 and miR-542. The functional enrichment of the selected top deregulated miRNAs revealed the highly enriched KEGG pathways and GO terms. Conclusion: Five of the studied miRNAs were greatly deregulated in RA and OA, they might be highly involved in the disease pathogenesis and so might be future therapeutic targets. Further functional studies are crucial to assess their roles and actual target genes.

Keywords: MiRNAs, expression, rheumatoid arthritis, osteoarthritis

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Authors: Wedad Albalawi

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The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques, and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then, dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is an arbitrary nonempty closed subset of the real numbers. Then, the dynamic inequalities on time scales have received a lot of attention in the literature and has become a major field in pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on Hardy and Coposon inequalities, using Steklov operator on time scale in double integrals to obtain special cases of time-scale inequalities of Hardy and Copson on high dimensions. The advantage of this study is that it uses the one-dimensional classical Hardy inequality to obtain higher dimensional on time scale versions that will be applied in the solution of the Cauchy problem for the wave equation. In addition, the obtained inequalities have various applications involving discontinuous domains such as bug populations, phytoremediation of metals, wound healing, maximization problems. The proof can be done by introducing restriction on the operator in several cases. The concepts in time scale version such as time scales calculus will be used that allows to unify and extend many problems from the theories of differential and of difference equations. In addition, using chain rule, and some properties of multiple integrals on time scales, some theorems of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of hardy, inequality of coposon, steklov operator

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Authors: M. A. Alhubail, A. I. Alateyah, D. Alenezi, B. Aldousiri

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In this study conformal cooling channel (CCC) was employed to dissipate heat of, Polypropylene (PP) parts injected into the Stereolithography (SLA) insert to form tensile and flexural test specimens. The direct metal laser sintering (DMLS) process was used to fabricate a mould with optimised CCC, while optimum parameters of injection moulding were obtained using Optimal-D. The obtained results show that optimisation of the cooling channel layout using a DMLS mould has significantly shortened cycle time without sacrificing the part’s mechanical properties. By applying conformal cooling channels, the cooling time phase was reduced by 20 seconds, and also defected parts were eliminated.

Keywords: optimum parameters, injection moulding, conformal cooling channels, cycle time

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Authors: Ömer Aktaş, Olga A. Suvorova, Oleg Tretyakov

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The boundary value problem on non-canonical and arbitrary shaped contour is solved with a numerically effective method called Analytical Regularization Method (ARM) to calculate propagation parameters. As a result of regularization, the equation of first kind is reduced to the infinite system of the linear algebraic equations of the second kind in the space of L2. This equation can be solved numerically for desired accuracy by using truncation method. The parameters as cut-off wavenumber and cut-off frequency are used in waveguide evolutionary equations of electromagnetic theory in time-domain to illustrate the real-valued TM fields with lossy and lossless media.

Keywords: analytical regularization method, electromagnetic theory evolutionary equations of time-domain, TM Field

Procedia PDF Downloads 502

Authors: Geir Kirkebøen

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Most patients with serious diagnoses want to know their prognosis, in particular their expected survival time. As part of the informed consent process, physicians are legally obligated to communicate such information to patients. However, there is no established (evidence based) ‘best practice’ for how to do this. The two questions explored in this study are: How do physicians communicate expected survival time to patients, and how should it be done? We explored the first, descriptive question in a study with Norwegian oncologists as participants. The study had a scenario and a survey part. In the scenario part, the doctors should imagine that a patient, recently diagnosed with a serious cancer diagnosis, has asked them: ‘How long can I expect to live with such a diagnosis? I want an honest answer from you!’ The doctors should assume that the diagnosis is certain, and that from an extensive recent study they had optimal statistical knowledge, described in detail as a right-skewed survival curve, about how long such patients with this kind of diagnosis could be expected to live. The main finding was that very few of the oncologists would explain to the patient the variation in survival time as described by the survival curve. The majority would not give the patient an answer at all. Of those who gave an answer, the typical answer was that survival time varies a lot, that it is hard to say in a specific case, that we will come back to it later etc. The survey part of the study clearly indicates that the main reason why the oncologists would not deliver the mortality prognosis was discomfort with its uncertainty. The scenario part of the study confirmed this finding. The majority of the oncologists explicitly used the uncertainty, the variation in survival time, as a reason to not give the patient an answer. Many studies show that patients want realistic information about their mortality prognosis, and that they should be given hope. The question then is how to communicate the uncertainty of the prognosis in a realistic and optimistic – hopeful – way. Based on psychological research, our hypothesis is that the best way to do this is by explicitly describing the variation in survival time, the (usually) right skewed survival curve of the prognosis, and emphasize to the patient the (small) possibility of being a ‘lucky outlier’. We tested this hypothesis in two scenario studies with lay people as participants. The data clearly show that people prefer to receive expected survival time as a median value together with explicit information about the survival curve’s right skewedness (e.g., concrete examples of ‘positive outliers’), and that communicating expected survival time this way not only provides people with hope, but also gives them a more realistic understanding compared with the typical way expected survival time is communicated. Our data indicate that it is not the existence of the uncertainty regarding the mortality prognosis that is the problem for patients, but how this uncertainty is, or is not, communicated and explained.

Keywords: cancer patients, decision psychology, doctor-patient communication, mortality prognosis

Procedia PDF Downloads 337

Authors: Emilio Galdeano-Gómez, Ángeles Godoy-Durán, Juan C. Pérez-Mesa, Laura Piedra-Muñoz

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Eco-efficiency is receiving ever-increasing interest as an indicator of sustainability, as it links environmental and economic performances in productive activities. In agriculture, these indicators and their determinants prove relevant due to the close relationships in this activity between the use of natural resources, which is generally limited, and the provision of basic goods to society. In this context, various analyses have focused on eco-efficiency by considering individual family farms as the basic production unit. However, not only must the measure of efficiency be taken into account, but also the existence of a series of factors which constitute socio-economic, political-institutional, and environmental determinants. Said factors have been studied to a lesser extent in the literature. The present work analyzes eco-efficiency at a micro level, focusing on small-scale family farms as the main decision-making units in horticulture in southeast Spain, a sector which represents about 30% of the fresh vegetables produced in the country and about 20% of those consumed in Europe. The objectives of this study are a) to obtain a series of eco-efficiency indicators by estimating several pressure ratios and economic value added in farming, b) to analyze the influence of specific social, economic and environmental variables on the aforementioned eco-efficiency indicators. The present work applies the method of Data Envelopment Analysis (DEA), which calculates different combinations of environmental pressures (water usage, phytosanitary contamination, waste management, etc.) and aggregate economic value. In a second stage, an analysis is conducted on the influence of the socio-economic and environmental characteristics of family farms on the eco-efficiency indicators, as endogeneous variables, through the use of truncated regression and bootstrapping techniques, following Simar-Wilson methodology. The results reveal considerable inefficiency in aspects such as waste management, while there is relatively little inefficiency in water usage and nitrogen balance. On the other hand, characteristics, such as product specialization, the adoption of quality certifications and belonging to a cooperative do have a positive impact on eco-efficiency. These results are deemed to be of interest to agri-food systems structured on small-scale producers, and they may prove useful to policy-makers as regards managing public environmental programs in agriculture.

Keywords: data envelopment analysis, eco-efficiency, family farms, horticulture, socioeconomic features

Procedia PDF Downloads 199

Authors: S. Sabatino, V. Calderaro, V. Galdi, G. Graber, L. Ippolito

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Climate change is a rapidly growing global threat caused mainly by increased emissions of carbon dioxide (CO₂) into the atmosphere. These emissions come from multiple sources, including industry, power generation, and the transport sector. The need to tackle climate change and reduce CO₂ emissions is indisputable. A crucial solution to achieving decarbonization in the transport sector is the adoption of electric vehicles (EVs). These vehicles use lithium (Li-Ion) batteries as an energy source, making them extremely efficient and with low direct emissions. However, Li-Ion batteries are not without problems, including the risk of overheating and performance degradation. To ensure its safety and longevity, it is essential to use a battery management system (BMS). The BMS constantly monitors battery status, adjusts temperature and cell balance, ensuring optimal performance and preventing dangerous situations. From the monitoring carried out, it is also able to optimally manage the battery to increase its life. Among the parameters monitored by the BMS, the main ones are State of Charge (SoC), State of Health (SoH), and State of Power (SoP). The evaluation of these parameters can be carried out in two ways: offline, using benchtop batteries tested in the laboratory, or online, using batteries installed in moving vehicles. Online estimation is the preferred approach, as it relies on capturing real-time data from batteries while operating in real-life situations, such as in everyday EV use. Actual battery usage conditions are highly variable. Moving vehicles are exposed to a wide range of factors, including temperature variations, different driving styles, and complex charge/discharge cycles. This variability is difficult to replicate in a controlled laboratory environment and can greatly affect performance and battery life. Online estimation captures this variety of conditions, providing a more accurate assessment of battery behavior in real-world situations. In this article, a hybrid approach based on a neural network and a statistical method for real-time estimation of SoC, SoH, and SoP parameters of interest is proposed. These parameters are estimated from the analysis of a one-day driving profile of an electric vehicle, assumed to be divided into the following four phases: (i) Partial discharge (SoC 100% - SoC 50%), (ii) Partial discharge (SoC 50% - SoC 80%), (iii) Deep Discharge (SoC 80% - SoC 30%) (iv) Full charge (SoC 30% - SoC 100%). The neural network predicts the values of ohmic resistance and incremental capacity, while the statistical method is used to estimate the parameters of interest. This reduces the complexity of the model and improves its prediction accuracy. The effectiveness of the proposed model is evaluated by analyzing its performance in terms of square mean error (RMSE) and percentage error (MAPE) and comparing it with the reference method found in the literature.

Keywords: electric vehicle, Li-Ion battery, BMS, state-of-charge, state-of-health, state-of-power, artificial neural networks

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

Procedia PDF Downloads 125

Authors: Marsden Jacques, Dennis Wong

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A weak order is a way to rank n objects where ties are allowed. In this talk, we present a recursive framework to generate Gray codes for weak orders. We then describe a simple algorithm based on the framework that generates 2-Gray codes for weak orders in constant amortized time per string. This framework can easily be modified to generate other Gray codes for weak orders. We provide an example on using the framework to generate the first Shift Gray code for weak orders, also in constant amortized time, where consecutive strings differ by a shift or a symbol change.

Keywords: weak order, Cayley permutation, Gray code, shift Gray code

Procedia PDF Downloads 183