Search results for: X-ray output

Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Christopher Thornton, Niina Kolehmainen, Kianoush Nazarpour

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Background: Accelerometers are widely used to measure physical activity behavior, including in children. The traditional method for processing acceleration data uses cut points, relying on calibration studies that relate the quantity of acceleration to energy expenditure. As these relationships do not generalise across diverse populations, they must be parametrised for each subpopulation, including different age groups, which is costly and makes studies across diverse populations difficult. A data-driven approach that allows physical activity intensity states to emerge from the data under study without relying on parameters derived from external populations offers a new perspective on this problem and potentially improved results. We evaluated the data-driven approach in a diverse population with a range of rapidly evolving physical and mental capabilities, namely very young children (9-38 months old), where this new approach may be particularly appropriate. Methods: We applied an unsupervised machine learning approach (a hidden semi-Markov model - HSMM) to segment and cluster the accelerometer data recorded from 275 children with a diverse range of physical and cognitive abilities. The HSMM was configured to identify a maximum of six physical activity intensity states and the output of the model was the time spent by each child in each of the states. For comparison, we also processed the accelerometer data using published cut points with available thresholds for the population. This provided us with time estimates for each child’s sedentary (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data on the children’s physical and cognitive abilities were collected using the Paediatric Evaluation of Disability Inventory (PEDI-CAT). Results: The HSMM identified two inactive states (INS, comparable to SED), two lightly active long duration states (LAS, comparable to LPA), and two short-duration high-intensity states (HIS, comparable to MVPA). Overall, the children spent on average 237/392 minutes per day in INS/SED, 211/129 minutes per day in LAS/LPA, and 178/168 minutes in HIS/MVPA. We found that INS overlapped with 53% of SED, LAS overlapped with 37% of LPA and HIS overlapped with 60% of MVPA. We also looked at the correlation between the time spent by a child in either HIS or MVPA and their physical and cognitive abilities. We found that HIS was more strongly correlated with physical mobility (R²HIS =0.5, R²MVPA= 0.28), cognitive ability (R²HIS =0.31, R²MVPA= 0.15), and age (R²HIS =0.15, R²MVPA= 0.09), indicating increased sensitivity to key attributes associated with a child’s mobility. Conclusion: An unsupervised machine learning technique can segment and cluster accelerometer data according to the intensity of movement at a given time. It provides a potentially more sensitive, appropriate, and cost-effective approach to analysing physical activity behavior in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive across diverse populations.

Keywords: physical activity, machine learning, under 5s, disability, accelerometer

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Authors: Susie Pryor, Palak Sadhwani

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The purpose of this research is to provide the foundations for a broad research agenda examining the role loneliness plays in entrepreneurship. While qualitative research in entrepreneurship incidentally captures the existence of loneliness as a part of the lived reality of entrepreneurs, to the authors’ knowledge, no academic work has to date explored this construct in this context. Moreover, many individuals reporting high levels of loneliness (women, ethnic minorities, immigrants, low income, low education) reflect those who are currently driving small business growth in the United States. Loneliness is a persistent state of emotional distress which results from feelings of estrangement and rejection or develops in the absence of social relationships and interactions. Empirical work finds links between loneliness and depression, suicide and suicide ideation, anxiety, hostility and passiveness, lack of communication and adaptability, shyness, poor social skills and unrealistic social perceptions, self-doubts, fear of rejection, and negative self-evaluation. Lonely individuals have been found to exhibit lower levels of self-esteem, higher levels of introversion, lower affiliative tendencies, less assertiveness, higher sensitivity to rejection, a heightened external locus of control, intensified feelings of regret and guilt over past events and rigid and overly idealistic goals concerning the future. These characteristics are likely to impact entrepreneurs and their work. Research identifies some key dangers of loneliness. Loneliness damages human love and intimacy, can disturb and distract individuals from channeling creative and effective energies in a meaningful way, may result in the formation of premature, poorly thought out and at times even irresponsible decisions, and produce hard and desensitized individuals, with compromised health and quality of life concerns. The current study utilizes meta-analysis and text analytics to distinguish loneliness from other related constructs (e.g., social isolation) and categorize antecedents and effects of loneliness across subpopulations. This work has the potential to materially contribute to the field of entrepreneurship by cleanly defining constructs and providing foundational background for future research. It offers a richer understanding of the evolution of loneliness and related constructs over the life cycle of entrepreneurial start-up and development. Further, it suggests preliminary avenues for exploration and methods of discovery that will result in knowledge useful to the field of entrepreneurship. It is useful to both entrepreneurs and those work with them as well as academics interested in the topics of loneliness and entrepreneurship. It adopts a grounded theory approach.

Keywords: entrepreneurship, grounded theory, loneliness, meta-analysis

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Authors: Pavel Prochazka

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The study deals with transducers for measuring vibration displacements of rotating blade tips in turbomachines. In order to prevent major accidents with extensive economic consequences, it shows an urgent need for every low-pressure steam turbine stage being equipped with modern non-contact measuring system providing information on blade loading, damage and residual lifetime under operation. The requirement of measuring vibration and static characteristics of steam turbine blades, therefore, calls for the development and operational verification of both new types of sensors and measuring principles and methods. The task is really demanding: to measure displacements of blade tips with a resolution of the order of 10 μm by speeds up to 750 m/s, humidity 100% and temperatures up to 200 °C. While in gas turbines are used primarily capacitive and optical transducers, these transducers cannot be used in steam turbines. The reason is moisture vapor, droplets of condensing water and dirt, which disable the function of sensors. Therefore, the most feasible approach was to focus on research of electromagnetic sensors featuring promising characteristics for given blade materials in a steam environment. Following types of sensors have been developed and both experimentally and theoretically studied in the Institute of Thermodynamics, Academy of Sciences of the Czech Republic: eddy-current, Hall effect, inductive and magnetoresistive. Eddy-current transducers demand a small distance of 1 to 2 mm and change properties in the harsh environment of steam turbines. Hall effect sensors have relatively low sensitivity, high values of offset, drift, and especially noise. Induction sensors do not require any supply current and have a simple construction. The magnitude of the sensors output voltage is dependent on the velocity of the measured body and concurrently on the varying magnetic induction, and they cannot be used statically. Magnetoresistive sensors are formed by magnetoresistors arranged into a Wheatstone bridge. Supplying the sensor from a current source provides better linearity. The MR sensors can be used permanently for temperatures up to 200 °C at lower values of the supply current of about 1 mA. The frequency range of 0 to 300 kHz is by an order higher comparing to the Hall effect and induction sensors. The frequency band starts at zero frequency, which is very important because the sensors can be calibrated statically. The MR sensors feature high sensitivity and low noise. The symmetry of the bridge arrangement leads to a high common mode rejection ratio and suppressing disturbances, which is important, especially in industrial applications. The MR sensors feature high sensitivity, high common mode rejection ratio, and low noise, which is important, especially in industrial applications. Magnetoresistive transducers provide a range of excellent properties indicating their priority for displacement measurements of rotating blades in turbomachines.

Keywords: turbines, blade vibration, blade tip timing, non-contact sensors, magnetoresistive sensors

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Authors: S. H. Babu Gurucharan, Priyanka Kaushal

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Majority of the world's population is expected to live in the Asia and Pacific region by 2050 and thus their cities will generate the maximum waste. India, being the second populous country in the world, is an ideal case study to identify a solution for Asian countries. Waste minimisation and utilisation have always been part of the Indian culture. During rapid urbanisation, our society lost the art of waste minimisation and utilisation habits. Presently, Waste is not considered as a resource, thus wasting an opportunity to tap resources. The technologies in vogue are not suited for effective treatment of large quantities of generated solid waste, without impacting the environment and the population. If not treated efficiently, Waste can become a silent killer. The article is trying to highlight the Indian municipal solid waste scenario as a key indicator of Asian waste management and recommend sustainable waste management and suggest effective solutions to treat the Solid Waste. The methods followed during the research were to analyse the solid waste data on characteristics of solid waste generated in Indian cities, then evaluate the current technologies to identify the most suitable technology in Indian conditions with minimal environmental impact, interact with the technology technical teams, then generate a technical process specific to Indian conditions and further examining the environmental impact and advantages/ disadvantages of the suggested process. The most important finding from the study was the recognition that most of the current municipal waste treatment technologies being employed, operate sub-optimally in Indian conditions. Therefore, the study using the available data, generated heat and mass balance of processes to arrive at the final technical process, which was broadly divided into Waste processing, Waste Treatment, Power Generation, through various permutations and combinations at each stage to ensure that the process is techno-commercially viable in Indian conditions. Then environmental impact was arrived through secondary sources and a comparison of environmental impact of different technologies was tabulated. The major advantages of the suggested process are the effective use of waste for resource generation both in terms of maximised power output or conversion to eco-friendly products like biofuels or chemicals using advanced technologies, minimum environmental impact and the least landfill requirement. The major drawbacks are the capital, operations and maintenance costs. The existing technologies in use in Indian municipalities have their own limitations and the shortlisted technology is far superior to other technologies in vogue. Treatment of Municipal Solid Waste with an efficient green power generation is possible through a combination of suitable environment-friendly technologies. A combination of bio-reactors and plasma-based gasification technology is most suitable for Indian Waste and in turn for Asian waste conditions.

Keywords: calorific value, gas fermentation, landfill, municipal solid waste, plasma gasification, syngas

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Authors: Mohamed Elmeshad

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During the mid-1990's Egypt had become obliged to implement the Economic Reform and Structural Adjustment Program that included broad economic liberalization, expansion of the private sector and a contraction the size of government spending. This coincided as well with attempts to appear more democratic and open to liberalizing public space and discourse. At the same time, economic pressures and the proliferation of social media access and activism had led to increased pressure to open a mediascape and remove it from the clutches of the government, which had monopolized print and broadcast mass media for over 4 decades by that point. However, the mechanisms that governed the privatization of mass media allowed for sustained government control, even through the prism of ostensibly privately owned newspapers and television stations. These mechanisms involve barriers to entry from a financial and security perspective, as well as operational capacities of distribution and access to means of production. The power dynamics between mass media establishments and the state were moulded during this period in a novel way. Power dynamics within media establishments had also formed under such circumstances. The changes in the country's political economy itself somehow mirrored these developments. This paper will examine these dynamics and shed light on the political economy of Egypt's newly privatized mass media in the early 2000's especially. Methodology: This study will rely on semi-structured interviews from individuals involved with these changes from the perspective of the media organizations. It also will map out the process of media privatization by looking at the administrative, operative and legislative institutions and contexts in order to attempt to draw conclusions on methods of control and the role of the state during the process of privatization. Finally, a brief discourse analysis will be necessary in order to aptly convey how these factors ultimately reflected on media output. Findings and conclusion: The development of Egyptian private, “independent” mirrored the trajectory of transitions in the country’s political economy. Liberalization of the economy meant that a growing class of business owners would explore opportunities that such new markets would offer. However the regime’s attempts to control access to certain forms of capital, especially in sectors such as the media affected the structure of print and broadcast media, as well as the institutions that would govern them. Like the process of liberalisation, much of the regime’s manoeuvring with regards to privatization of media had been haphazardly used to indirectly expand the regime and its ruling party’s ability to retain influence, while creating a believable façade of openness. In this paper, we will attempt to uncover these mechanisms and analyse our findings in ways that explain how the manifestations prevalent in the context of a privatizing media space in a transitional Egypt provide evidence of both the intentions of this transition, and the ways in which it was being held back.

Keywords: business, mass media, political economy, power, privatisation

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Authors: Roberto de Lieto Vollaro, Emanuele de Lieto Vollaro, Gianluca Coltrinari

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The building sector is responsible, in many industrialized countries, for about 40% of the total energy requirements, so it seems necessary to devote some efforts in this area in order to achieve a significant reduction of energy consumption and of greenhouse gases emissions. The paper presents a study aiming at providing a design methodology able to identify the best configuration of the system building/plant, from a technical, economic and environmentally point of view. Normally, the classical approach involves a building's energy loads analysis under steady state conditions, and subsequent selection of measures aimed at improving the energy performance, based on previous experience made by architects and engineers in the design team. Instead, the proposed approach uses a sequence of two well known scientifically validated calculation methods (TRNSYS and RETScreen), that allow quite a detailed feasibility analysis. To assess the validity of the calculation model, an existing, historical building in Central Italy, that will be the object of restoration and preservative redevelopment, was selected as a case-study. The building is made of a basement and three floors, with a total floor area of about 3,000 square meters. The first step has been the determination of the heating and cooling energy loads of the building in a dynamic regime by means of TRNSYS, which allows to simulate the real energy needs of the building in function of its use. Traditional methodologies, based as they are on steady-state conditions, cannot faithfully reproduce the effects of varying climatic conditions and of inertial properties of the structure. With TRNSYS it is possible to obtain quite accurate and reliable results, that allow to identify effective combinations building-HVAC system. The second step has consisted of using output data obtained with TRNSYS as input to the calculation model RETScreen, which enables to compare different system configurations from the energy, environmental and financial point of view, with an analysis of investment, and operation and maintenance costs, so allowing to determine the economic benefit of possible interventions. The classical methodology often leads to the choice of conventional plant systems, while RETScreen provides a financial-economic assessment for innovative energy systems and low environmental impact. Computational analysis can help in the design phase, particularly in the case of complex structures with centralized plant systems, by comparing the data returned by the calculation model RETScreen for different design options. For example, the analysis performed on the building, taken as a case study, found that the most suitable plant solution, taking into account technical, economic and environmental aspects, is the one based on a CCHP system (Combined Cooling, Heating, and Power) using an internal combustion engine.

Keywords: energy, system, building, cooling, electrical

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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: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: Tashinga Matindike

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By means of definition, digital artworks encompass an array of artistic productions that are expressed in a technological form as an essential part of a creative process. Examples include illustrations, photos, videos, sculptures, and installations. Within the context of the visual arts, the process of repatriation involves the return of once-appropriated goods. Archiving denotes the preservation of a commodity for storage purposes in order to nurture its continuity. The aforementioned definitions form the foundation of the academic framework and premise of the argument, which is outlined in this paper. This paper aims to define, discuss and decipher the complexities involved in digitising artworks, whilst explaining the benefits of the process, particularly within the South African context, which is rich in tangible and intangible traditional cultural material, objects, and performances. With the internet having been introduced to the African Continent in the early 1990s, this new form of technology, in its own right, initiated a high degree of efficiency, which also resulted in the progressive transformation of computer-generated visual output. Subsequently, this caused a revolutionary influence on the manner in which technological software was developed and uterlised in art-making. Digital technology and the digitisation of creative processes then opened up new avenues of collating and recording information. One of the first visual artists to make use of digital technology software in his creative productions was United States-based artist John Whitney. His inventive work contributed greatly to the onset and development of digital animation. Comparable by technique and originality, South African contemporary visual artists who make digital artworks, both locally and internationally, include David Goldblatt, Katherine Bull, Fritha Langerman, David Masoga, Zinhle Sethebe, Alicia Mcfadzean, Ivan Van Der Walt, Siobhan Twomey, and Fhatuwani Mukheli. In conclusion, the main objective of this paper is to address the following questions: In which ways has the South African art community of visual artists made use of and benefited from technology, in its digital form, as a means to further advance creativity? What are the positive changes that have resulted in art production in South Africa since the onset and use of digital technological software? How has digitisation changed the manner in which we record, interpret, and archive both written and visual information? What is the role of South African art institutions in the development of digital technology and its use in the field of visual art. What role does digitisation play in the process of the repatriation of artworks and artefacts. The methodology in terms of the research process of this paper takes on a multifacted form, inclusive of data analysis of information attained by means of qualitative and quantitative approaches.

Keywords: digital art, digitisation, technology, archiving, transformation and repatriation

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Authors: Amirhossein Dodankeh, Hadi Dabiryan, Saeed Hamze

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Using fabrics to reinforce composites considerably leads to improved mechanical properties, including resistance to the impact load and the energy absorption of composites. Warp-knitted spacer fabrics (WKSF) are fabrics consisting of two layers of warp-knitted fabric connected by pile yarns. These connections create a space between the layers filled by pile yarns and give the fabric a three-dimensional shape. Today because of the unique properties of spacer fabrics, they are widely used in the transportation, construction, and sports industries. Polyurethane (PU) foams are commonly used as energy absorbers, but WKSF has much better properties in moisture transfer, compressive properties, and lower heat resistance than PU foam. It seems that the use of warp-knitted spacer fabric reinforced PU foam (WKSFRF) can lead to the production and use of composite, which has better properties in terms of energy absorption from the foam, its mold formation is enhanced, and its mechanical properties have been improved. In this paper, the energy absorption efficiency (EAE) of WKSFRF under low-velocity impact is investigated experimentally. The contribution of the effect of each of the structural parameters of the WKSF on the absorption of impact energy has also been investigated. For this purpose, WKSF with different structures such as two different thicknesses, small and large mesh sizes, and position of the meshes facing each other and not facing each other were produced. Then 6 types of composite samples with different structural parameters were fabricated. The physical properties of samples like weight per unit area and fiber volume fraction of composite were measured for 3 samples of any type of composites. Low-velocity impact with an initial energy of 5 J was carried out on 3 samples of any type of composite. The output of the low-velocity impact test is acceleration-time (A-T) graph with a lot deviation point, in order to achieve the appropriate results, these points were removed using the FILTFILT function of MATLAB R2018a. Using Newtonian laws of physics force-displacement (F-D) graph was drawn from an A-T graph. We know that the amount of energy absorbed is equal to the area under the F-D curve. Determination shows the maximum energy absorption is 2.858 J which is related to the samples reinforced with fabric with large mesh, high thickness, and not facing of the meshes relative to each other. An index called energy absorption efficiency was defined, which means absorption energy of any kind of our composite divided by its fiber volume fraction. With using this index, the best EAE between the samples is 21.6 that occurs in the sample with large mesh, high thickness, and meshes facing each other. Also, the EAE of this sample is 15.6% better than the average EAE of other composite samples. Generally, the energy absorption on average has been increased 21.2% by increasing the thickness, 9.5% by increasing the size of the meshes from small to big, and 47.3% by changing the position of the meshes from facing to non-facing.

Keywords: composites, energy absorption efficiency, foam, geometrical parameters, low-velocity impact, warp-knitted spacer fabric

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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Authors: Stephen Dankwa, Zheng Wenfeng, Xiaolu Li

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Climate change is recently gaining the attention of many countries across the world. Climate change, which is also known as global warming, referring to the increasing in average surface temperature has been a concern to the Environmental Protection Agency of Ghana. Recently, Ghana has become vulnerable to the effect of the climate change as a result of the dependence of the majority of the population on agriculture. The clearing down of trees to grow crops and burning of charcoal in the country has been a contributing factor to the rise in temperature nowadays in the country as a result of releasing of carbon dioxide and greenhouse gases into the air. Recently, petroleum stations across the cities have been on fire due to this climate changes and which have position Ghana in a way not able to withstand this climate event. As a result, the significant of this research paper is to project how the rise in the average surface temperature will be like at the end of the mid-21st century when agriculture and deforestation are allowed to continue for some time in the country. This study uses the Coupled Modeled Intercomparison Project phase 5 (CMIP5) experiment RCP 8.5 model output data to monitor the future climate changes from 2041-2050, at the end of the mid-21st century over the ten (10) major cities (Accra, Bolgatanga, Cape Coast, Koforidua, Kumasi, Sekondi-Takoradi, Sunyani, Ho, Tamale, Wa) in Ghana. In the models, Support Vector Machine and Random forest, where the cities as a function of heat wave metrics (minimum temperature, maximum temperature, mean temperature, heat wave duration and number of heat waves) assisted to provide more than 50% accuracy to predict and monitor the pattern of the surface air temperature. The findings identified were that the near-surface air temperature will rise between 1°C-2°C (degrees Celsius) over the coastal cities (Accra, Cape Coast, Sekondi-Takoradi). The temperature over Kumasi, Ho and Sunyani by the end of 2050 will rise by 1°C. In Koforidua, it will rise between 1°C-2°C. The temperature will rise in Bolgatanga, Tamale and Wa by 0.5°C by 2050. This indicates how the coastal and the southern part of the country are becoming hotter compared with the north, even though the northern part is the hottest. During heat waves from 2041-2050, Bolgatanga, Tamale, and Wa will experience the highest mean daily air temperature between 34°C-36°C. Kumasi, Koforidua, and Sunyani will experience about 34°C. The coastal cities (Accra, Cape Coast, Sekondi-Takoradi) will experience below 32°C. Even though, the coastal cities will experience the lowest mean temperature, they will have the highest number of heat waves about 62. Majority of the heat waves will last between 2 to 10 days with the maximum 30 days. The surface temperature will continue to rise by the end of the mid-21st century (2041-2050) over the major cities in Ghana and so needs to be addressed to the Environmental Protection Agency in Ghana in order to mitigate this problem.

Keywords: climate changes, CMIP5, Ghana, heat waves, random forest, SVM

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Authors: Abdulaziz Bashir Kutawa, Khairulmazmi Ahmad, Mohd Zobir Hussein, Asgar Ali, Mohd Aswad Abdul Wahab, Amara Rafi, Mahesh Tiran Gunasena, Muhammad Ziaur Rahman, Md Imam Hossain, Syazwan Afif Mohd Zobir

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Various abiotic and biotic stresses have an impact on rice production all around the world. The most serious and prevalent disease in rice plants, known as rice blast, is one of the major obstacles to the production of rice. It is one of the diseases that has the greatest negative effects on rice farming globally, the disease is caused by a fungus called Magnaporthe grisae. Since nanoparticles were shown to have an inhibitory impact on certain types of fungus, nanotechnology is a novel notion to enhance agriculture by battling plant diseases. Utilizing nanocarrier systems enables the active chemicals to be absorbed, attached, and encapsulated to produce efficient nanodelivery formulations. The objectives of this research work were to determine the efficacy and mode of action of the nanofungicides (in-vitro) and in field conditions (in-vivo). Ionic gelation method was used in the development of the nanofungicides. Using the poisoned media method, the synthesized agronanofungicides' in-vitro antifungal activity was assessed against M. grisae. The potato dextrose agar (PDA) was amended in several concentrations; 0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.15, 0.20, 0.25, 0.30, and 0.35 ppm for the nanofungicides. Medium with the only solvent served as a control. Every day, mycelial growth was measured, and PIRG (percentage inhibition of radial growth) was also computed. Every day, mycelial growth was measured, and PIRG (percentage inhibition of radial growth) was also computed. Based on the results of the zone of inhibition, the chitosan-hexaconazole agronanofungicide (2g/mL) was the most effective fungicide to inhibit the growth of the fungus with 100% inhibition at 0.2, 0.25, 0.30, and 0.35 ppm, respectively. Then followed by carbendazim analytical fungicide that inhibited the growth of the fungus (100%) at 5, 10, 25, 50, and 100 ppm, respectively. The least were found to be propiconazole and basamid fungicides with 100% inhibition only at 100 ppm. The scanning electron microscope (SEM), confocal laser scanning microscope (CLSM), and transmission electron microscope (TEM) were used to study the mechanisms of action of the M. grisae fungal cells. The results showed that both carbendazim, chitosan-hexaconazole, and HXE were found to be the most effective fungicides in disrupting the mycelia of the fungus, and internal structures of the fungal cells. The results of the field assessment showed that the CHDEN treatment (5g/L, double dosage) was found to be the most effective fungicide to reduce the intensity of the rice blast disease with DSI of 17.56%, lesion length (0.43 cm), DR of 82.44%, AUDPC of 260.54 Unit2, and PI of 65.33%, respectively. The least treatment was found to be chitosan-hexaconazole-dazomet (2.5g/L, MIC). The usage of CHDEN and CHEN nanofungicides will significantly assist in lessening the severity of rice blast in the fields, increasing output and profit for rice farmers.

Keywords: chitosan, hexaconazole, disease incidence, and magnaporthe grisae

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Authors: E. A. Abdel-Hakim

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The corn stemborer Sesamia cretica (Lederer), has been viewed in many parts of the world as a major pest of cultivated maize, graminaceous crops and sugarcane. Its life cycle is comprised of two different phases, one is the growth and developmental phase (non-diapause) and the other is diapause phase which takes place at the last larval instar. Several problems associated with the use of conventional insecticides, have strongly demonstrated the need for applying alternative safe compounds. Prominent among the prototypes of such prospective chemicals are the juvenoids; i.e. the insect (JH) mimics. In fact, the hormonal effect on metabolism has long been viewed as a secondary consequence of its direct action on specific energy-requiring biosynthetic mechanisms. Therefore, the present study was undertaken essentially in a rather systematic fashion as a contribution towards clarifying metabolic and energetic changes taking place during non-diapause wandering larvae as regulated by (JH) mimic. For this purpose, we applied two different doses of JH mimic (Ro 11-0111) in a single (standard) dose of 100µg or in a single dose of 20 µg/g bw in1µl acetone topically at the onset of nondiapause wandering larvae (WL). Energetic data were obtained by indirect calorimetry methods by conversion of respiratory gas exchange volumetric data, as measured manometrically using a Warburg constant respirometer, to caloric units (g-cal/g fw/h). The findings obtained can be given in brief; these treated larvae underwent supernumerary larval moults. However, this potential the wandering larvae proved to possess whereby restoration of larval programming for S. cretica to overcome stresses even at this critical developmental period. The results obtained, particularly with the high dose used, show that 98% wandering larvae were rescued to survive up to one month (vs. 5 days for normal controls), finally the formation of larval-adult intermediates. Also, the solvent controls had resulted in about 22% additional, but stationary moultings. The basal respiratory metabolism (O2 uptake and CO2 output) of the (WL), whether un-treated or larvae not had followed reciprocal U-shaped curves all along of their developmental duration. The lowest points stood nearly to the day of prepupal formation (571±187 µl O2/gfw/h and 553±181 µl CO2/gfw/h) during un-treated in contrast to the larvae treated with JH (210±48 µl O2/gfw/h and 335±81 µl CO2/gfw/h). Un-treated (normal) larvae proved to utilize carbohydrates as the principal source for energy supply; being fully oxidised without sparing any appreciable amount for endergonic conversion to fats. While, the juvenoid-treated larvae and compared with the acetone-treated control equivalents, there existed no distinguishable differences between them; both had been observed utilising carbohydrates as the sole source of energy demand and converting endergonically almost similar percentages to fats. The overall profile, treated and un-treated (WL) utilized carbohydrates as the principal source for energy demand during this stage.

Keywords: juvenile hormone, respiratory metabolism, Sesamia cretica, wandering phase

Procedia PDF Downloads 293

Authors: Anna Costanza Russo, Daniele Landi, Michele Germani

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Nowadays, the European Ecodesign Directive has emerged as a new approach to integrate environmental concerns into the product design and related processes. Ecodesign aims to minimize environmental impacts throughout the product life cycle, without compromising performances and costs. In addition, the recent Ecodesign Directives require products which are increasingly eco-friendly and eco-efficient, preserving high-performances. It is very important for producers measuring performances, for electric cooking ranges, hobs, ovens, and grills for household use, and a low power consumption of appliances represents a powerful selling point, also in terms of ecodesign requirements. The Ecodesign Directive provides a clear framework about the sustainable design of products and it has been extended in 2009 to all energy-related products, or products with an impact on energy consumption during the use. The European Regulation establishes measures of ecodesign of ovens, hobs, and kitchen hoods, and domestic use and energy efficiency of a product has a significant environmental aspect in the use phase which is the most impactful in the life cycle. It is important that the product parameters and performances are not affected by ecodesign requirements from a user’s point of view, and the benefits of reducing energy consumption in the use phase should offset the possible environmental impact in the production stage. Accurate measurements of cooking appliance performance are essential to help the industry to produce more energy efficient appliances. The development of ecodriven products requires ecoinnovation and ecodesign tools to support the sustainability improvement. The ecodesign tools should be practical and focused on specific ecoobjectives in order to be largely diffused. The main scope of this paper is the development, implementation, and testing of an innovative tool, which could be an improvement for the sustainable design of induction hobs. In particular, a prototypical software tool is developed in order to simulate the energy performances of the induction hobs. The tool is focused on a multiphysics model which is able to simulate the energy performances and the efficiency of induction hobs starting from the design data. The multiphysics model is composed by an electromagnetic simulation and a thermal simulation. The electromagnetic simulation is able to calculate the eddy current induced in the pot, which leads to the Joule heating of material. The thermal simulation is able to measure the energy consumption during the operational phase. The Joule heating caused from the eddy currents is the output of electromagnetic simulation and the input of thermal ones. The aims of the paper are the development of integrated tools and methodologies of virtual prototyping in the context of the ecodesign. This tool could be a revolutionary instrument in the field of industrial engineering and it gives consideration to the environmental aspects of product design and focus on the ecodesign of energy-related products, in order to achieve a reduced environmental impact.

Keywords: ecodesign, energy efficiency, induction hobs, virtual prototyping

Procedia PDF Downloads 250

Authors: Álvaro Turriago-Hoyos

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Most research tends to see innovation as an explanatory factor in achieving high levels of competitiveness and productivity. More recent studies have begun to analyze the determinants of innovation in the services sector as opposed to the much-discussed industrial sector of a country’s economy. This research paper focuses on the services sector in Colombia, one of Latin America’s fastest growing and biggest economies. Over the past decade, much of Colombia’s economic expansion has relied on commodity exports (mainly oil and coffee) whilst the industrial sector has performed relatively poorly. Such developments highlight the potential of the innovative role played by the services sector of the Colombian economy and its future growth prospects. This research paper analyzes the relationship between inputs, which at the same time are internal sources of innovation (such as R&D activities), and external sources that are improved by technology acquisition. The outputs are basically the four kinds of innovation that the OECD Oslo Manual recognizes: product, process, marketing and organizational innovations. The instrument used to measure this input-output relationship is based on Knowledge Production Function approaches. We run Probit models in order to identify the existing relationships between the above inputs and outputs, but also to identify spill-overs derived from interactions of the components of the value chain of the services firms analyzed: customers, suppliers, competitors, and complementary firms. Data are obtained from the Colombian National Administrative Department of Statistics for the period 2008 to 2013 published in the II and III Colombian National Innovation Survey. A short summary of the results obtained lead to conclude that firm size and a firm’s level of technological development turn out to be important discriminating factors for the description of the innovative process at the firm level. The model’s outcomes show a positive impact on the probability of introducing any kind of innovation both on R&D and Technology Acquisition investment. Also, cooperation agreements with customers, research institutes, competitors, and the suppliers are significant. Belonging to a particular industrial group is an important determinant but only to product and organizational innovation. It is possible to establish that Health Services, Education, Computer, Wholesale trade, and Financial Intermediation are the ISIC sectors, which report the highest number of frequencies of the considered set of firms. Those five sectors of the sixteen considered, in all cases, explained more than half of the total of all kinds of innovations. Product Innovation, which is followed by Marketing Innovation, gets the highest results. Displaying the same set of firms distinguishing by size, and belonging to high and low tech services sector shows that the larger the firms the larger a number of innovations, but also that always high-tech firms show a better innovation performance.

Keywords: Colombia, determinants of innovation, innovation, services sector

Procedia PDF Downloads 267

Authors: Alexandre Daliberto Frugoli, Pedro Jose Gabriel Ferreira, Pedro Americo Frugoli, Lucio Leonardo, Thais Cavalheri Santos

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Laboratory classes in Engineering courses are essential for students to be able to integrate theory with practical reality, by handling equipment and observing experiments. In the researches of physical phenomena, students can learn about the complexities of science. Over the past years, universities in developing countries have been reducing the course load of engineering courses, in accordance with cutting cost agendas. Quality education is the object of study for researchers and requires educators and educational administrators able to demonstrate that the institutions are able to provide great learning opportunities at reasonable costs. Didactic test benches are indispensable equipment in educational activities related to turbo hydraulic pumps and pumping facilities study, which have a high cost and require long class time due to measurements and equipment adjustment time. In order to overcome the aforementioned obstacles, aligned with the professional objectives of an engineer, GruPEFE - UNIP (Research Group in Physics Education for Engineering - Universidade Paulista) has developed a multi-purpose stand for the discipline of fluid mechanics which allows the study of velocity and flow meters, loads losses and pump association. In this work, results obtained by the association in series and in parallel of hydraulic pumps will be presented and discussed, mainly analyzing the repeatability of experimental procedures and their agreement with the theory. For the association in series two identical pumps were used, consisting of the connection of the discharge of a pump to the suction of the next one, allowing the fluid to receive the power of all machines in the association. The characteristic curve of the set is obtained from the curves of each of the pumps, by adding the heads corresponding to the same flow rates. The same pumps were associated in parallel. In this association, the discharge piping is common to the two machines together. The characteristic curve of the set was obtained by adding to each value of H (head height), the flow rates of each pump. For the tests, the input and output pressure of each pump were measured. For each set there were three sets of measurements, varying the flow rate in range from 6.0 to 8.5 m 3 / h. For the two associations, the results showed an excellent repeatability with variations of less than 10% between sets of measurements and also a good agreement with the theory. This variation agrees with the instrumental uncertainty. Thus, the results validate the use of the fluids bench designed for didactic purposes. As a future work, a digital acquisition system is being developed, using differential sensors of extremely low pressures (2 to 2000 Pa approximately) for the microcontroller Arduino.

Keywords: engineering education, fluid mechanics, hydrostatic pumps association, multi-purpose stand

Procedia PDF Downloads 220

Authors: Andrea Bikfalvi, Josep Llach, Ferran Lazaro, Bojan Jovanovski

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Due to the continuous need to foster university-business cooperation in both developed and developing countries, some higher education institutions face the challenge of designing, piloting, operating, and consolidating knowledge and technology transfer units. University-business cooperation has different maturity stages worldwide, with some higher education institutions excelling in these practices, but with lots of others that could be qualified as intermediate, or even some situated at the very beginning of their knowledge transfer adventure. These latter face the imminent necessity to formally create the technology transfer unit and to draw its roadmap. The complexity of this operation is due to various aspects that need to align and coordinate, including a major change in mission, vision, structure, priorities, and operations. Qualitative in approach, this study presents 5 case studies, consisting of higher education institutions located in the Western Balkans – 2 in Albania, 2 in Bosnia and Herzegovina, 1 in Montenegro- fully immersed in the entrepreneurial journey of creating their knowledge and technology transfer unit. The empirical evidence is developed in a pan-European project, illustratively called KnowHub (reconnecting universities and enterprises to unleash regional innovation and entrepreneurial activity), which is being implemented in three countries and has resulted in at least 15 pilot cooperation agreements between academia and business. Based on a peer-mentoring approach including more experimented and more mature technology transfer models of European partners located in Spain, Finland, and Austria, a series of initial lessons learned are already available. The findings show that each unit developed its tailor-made approach to engage with internal and external stakeholders, offer value to the academic staff, students, as well as business partners. The latest technology underpinning KnowHub services and institutional commitment are found to be key success factors. Although specific strategies and plans differ, they are based on a general strategy jointly developed and based on common tools and methods of strategic planning and business modelling. The main output consists of providing good practice for designing, piloting, and initial operations of units aiming to fully valorise knowledge and expertise available in academia. Policymakers can also find valuable hints on key aspects considered vital for initial operations. The value of this contribution is its focus on the intersection of three perspectives (service orientation, organisational innovation, business model) since previous research has only relied on a single topic or dual approaches, most frequently in the business context and less frequently in higher education.

Keywords: business model, capacity building, entrepreneurial education, knowledge transfer

Procedia PDF Downloads 141

Authors: Chen-Yu Chen, Ping-Hsueh We, Wei-Mon Yan

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Over the past few centuries, human requirements for energy have been met by burning fossil fuels. However, exploiting this resource has led to global warming and innumerable environmental issues. Thus, finding alternative solutions to the growing demands for energy has recently been driving the development of low-carbon and even zero-carbon energy sources. Wind power and solar energy are good options but they have the problem of unstable power output due to unpredictable weather conditions. To overcome this problem, a reliable and efficient energy storage sub-system is required in future distributed-power systems. Among all kinds of energy storage technologies, the fuel cell system with hydrogen storage is a promising option because it is suitable for large-scale and long-term energy storage. The high-temperature proton exchange membrane fuel cell (HT-PEMFC) with metallic bipolar plates is a promising fuel cell system because an HT-PEMFC can tolerate a higher CO concentration and the utilization of metallic bipolar plates can reduce the cost of the fuel cell stack. However, the operating life of metallic bipolar plates is a critical issue because of the corrosion phenomenon. As a result, in this work, we try to apply different coating layer on the metal surface and to investigate the protection performance of the coating layers. The tested bipolar plates include uncoated SS304 bipolar plates, titanium nitride (TiN) coated SS304 bipolar plates and chromium nitride (CrN) coated SS304 bipolar plates. The results show that the TiN coated SS304 bipolar plate has the lowest contact resistance and through-plane resistance and has the best cell performance and operating life among all tested bipolar plates. The long-term in-situ fuel cell tests show that the HT-PEMFC with TiN coated SS304 bipolar plates has the lowest performance decay rate. The second lowest is CrN coated SS304 bipolar plate. The uncoated SS304 bipolar plate has the worst performance decay rate. The performance decay rates with TiN coated SS304, CrN coated SS304 and uncoated SS304 bipolar plates are 5.324×10⁻³ % h⁻¹, 4.513×10⁻² % h⁻¹ and 7.870×10⁻² % h⁻¹, respectively. In addition, the EIS results indicate that the uncoated SS304 bipolar plate has the highest growth rate of ohmic resistance. However, the ohmic resistance with the TiN coated SS304 bipolar plates only increases slightly with time. The growth rate of ohmic resistances with TiN coated SS304, CrN coated SS304 and SS304 bipolar plates are 2.85×10⁻³ h⁻¹, 3.56×10⁻³ h⁻¹, and 4.33×10⁻³ h⁻¹, respectively. On the other hand, the charge transfer resistances with these three bipolar plates all increase with time, but the growth rates are all similar. In addition, the effective catalyst surface areas with all bipolar plates do not change significantly with time. Thus, it is inferred that the major reason for the performance degradation is the elevated ohmic resistance with time, which is associated with the corrosion and oxidation phenomena on the surface of the stainless steel bipolar plates.

Keywords: coating layer, high-temperature proton exchange membrane fuel cell, metallic bipolar plate, performance degradation

Procedia PDF Downloads 281

Authors: Mohammadamin Hashemi, Mohammadreza Kashizadeh

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Droughts pose a significant threat to sustainable water resource management, agriculture, and socioeconomic sectors, particularly in the field of climate change. This study investigates drought simulation using rainfall-runoff modelling in the Cuneo district, Italy, over the past 60-year period. The study leverages the TUW model, a lumped conceptual rainfall-runoff model with a semi-distributed operation capability. Similar in structure to the widely used Hydrologiska Byråns Vattenbalansavdelning (HBV) model, the TUW model operates on daily timesteps for input and output data specific to each catchment. It incorporates essential routines for snow accumulation and melting, soil moisture storage, and streamflow generation. Multiple catchments' discharge data within the Cuneo district form the basis for thorough model calibration employing the Kling-Gupta Efficiency (KGE) metric. A crucial metric for reliable drought analysis is one that can accurately represent low-flow events during drought periods. This ensures that the model provides a realistic picture of water availability during these critical times. Subsequent validation of monthly discharge simulations thoroughly evaluates overall model performance. Beyond model development, the investigation delves into drought analysis using the robust Standardized Runoff Index (SRI). This index allows for precise characterization of drought occurrences within the study area. A meticulous comparison of observed and simulated discharge data is conducted, with particular focus on low-flow events that characterize droughts. Additionally, the study explores the complex interplay between land characteristics (e.g., soil type, vegetation cover) and climate variables (e.g., precipitation, temperature) that influence the severity and duration of hydrological droughts. The study's findings demonstrate successful calibration of the TUW model across most catchments, achieving commendable model efficiency. Comparative analysis between simulated and observed discharge data reveals significant agreement, especially during critical low-flow periods. This agreement is further supported by the Pareto coefficient, a statistical measure of goodness-of-fit. The drought analysis provides critical insights into the duration, intensity, and severity of drought events within the Cuneo district. This newfound understanding of spatial and temporal drought dynamics offers valuable information for water resource management strategies and drought mitigation efforts. This research deepens our understanding of drought dynamics in the Cuneo region. Future research directions include refining hydrological modelling techniques and exploring future drought projections under various climate change scenarios.

Keywords: hydrologic extremes, hydrological drought, hydrological modelling, machine learning, rainfall-runoff modelling

Procedia PDF Downloads 41

Authors: F. Infante, W. Kaal, S. Perfetto, S. Herold

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In the last decade the demand of wireless sensors and low-power electric devices for condition monitoring in mechanical structures has been strongly increased. Networks of wireless sensors can potentially be applied in a huge variety of applications. Due to the reduction of both size and power consumption of the electric components and the increasing complexity of mechanical systems, the interest of creating dense nodes sensor networks has become very salient. Nevertheless, with the development of large sensor networks with numerous nodes, the critical problem of powering them is drawing more and more attention. Batteries are not a valid alternative for consideration regarding lifetime, size and effort in replacing them. Between possible alternative solutions for durable power sources useable in mechanical components, vibrations represent a suitable source for the amount of power required to feed a wireless sensor network. For this purpose, energy harvesting from structural vibrations has received much attention in the past few years. Suitable vibrations can be found in numerous mechanical environments including automotive moving structures, household applications, but also civil engineering structures like buildings and bridges. Similarly, a dynamic vibration absorber (DVA) is one of the most used devices to mitigate unwanted vibration of structures. This device is used to transfer the primary structural vibration to the auxiliary system. Thus, the related energy is effectively localized in the secondary less sensitive structure. Then, the additional benefit of harvesting part of the energy can be obtained by implementing dedicated components. This paper describes the design process of an energy harvesting tuned vibration absorber (EHTVA) for rotating systems using piezoelectric elements. The energy of the vibration is converted into electricity rather than dissipated. The device proposed is indeed designed to mitigate torsional vibrations as with a conventional rotational TVA, while harvesting energy as a power source for immediate use or storage. The resultant rotational multi degree of freedom (MDOF) system is initially reduced in an equivalent single degree of freedom (SDOF) system. The Den Hartog’s theory is used for evaluating the optimal mechanical parameters of the initial DVA for the SDOF systems defined. The performance of the TVA is operationally assessed and the vibration reduction at the original resonance frequency is measured. Then, the design is modified for the integration of active piezoelectric patches without detuning the TVA. In order to estimate the real power generated, a complex storage circuit is implemented. A DC-DC step-down converter is connected to the device through a rectifier to return a fixed output voltage. Introducing a big capacitor, the energy stored is measured at different frequencies. Finally, the electromechanical prototype is tested and validated achieving simultaneously reduction and harvesting functions.

Keywords: energy harvesting, piezoelectricity, torsional vibration, vibration absorber

Procedia PDF Downloads 147

Authors: Marlon Gallo, Eduardo Miguel, Laura Oca, Eneko Gonzalez, Unai Iraola

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The heat generation of an energy storage system is an essential topic when designing a battery pack and its cooling system. Heat generation estimation is used together with thermal models to predict battery temperature in operation and adapt the design of the battery pack and the cooling system to these thermal needs guaranteeing its safety and correct operation. In the present work, a comparison between the use of a heat flux sensor (HFS) for indirect measurement of heat losses in a cell and the widely used and simplified version of Bernardi’s equation for estimation is presented. First, a Li-ion cell is thermally characterized with an HFS to measure the thermal parameters that are used in a first-order lumped thermal model. These parameters are the equivalent thermal capacity and the thermal equivalent resistance of a single Li-ion cell. Static (when no current is flowing through the cell) and dynamic (making current flow through the cell) tests are conducted in which HFS is used to measure heat between the cell and the ambient, so thermal capacity and resistances respectively can be calculated. An experimental platform records current, voltage, ambient temperature, surface temperature, and HFS output voltage. Second, an equivalent circuit model is built in a Matlab-Simulink environment. This allows the comparison between the generated heat predicted by Bernardi’s equation and the HFS measurements. Data post-processing is required to extrapolate the heat generation from the HFS measurements, as the sensor records the heat released to the ambient and not the one generated within the cell. Finally, the cell temperature evolution is estimated with the lumped thermal model (using both HFS and Bernardi’s equation total heat generation) and compared towards experimental temperature data (measured with a T-type thermocouple). At the end of this work, a critical review of the results obtained and the possible mismatch reasons are reported. The results show that indirectly measuring the heat generation with HFS gives a more precise estimation than Bernardi’s simplified equation. On the one hand, when using Bernardi’s simplified equation, estimated heat generation differs from cell temperature measurements during charges at high current rates. Additionally, for low capacity cells where a small change in capacity has a great influence on the terminal voltage, the estimated heat generation shows high dependency on the State of Charge (SoC) estimation, and therefore open circuit voltage calculation (as it is SoC dependent). On the other hand, with indirect measuring the heat generation with HFS, the resulting error is a maximum of 0.28ºC in the temperature prediction, in contrast with 1.38ºC with Bernardi’s simplified equation. This illustrates the limitations of Bernardi’s simplified equation for applications where precise heat monitoring is required. For higher current rates, Bernardi’s equation estimates more heat generation and consequently, a higher predicted temperature. Bernardi´s equation accounts for no losses after cutting the charging or discharging current. However, HFS measurement shows that after cutting the current the cell continues generating heat for some time, increasing the error of Bernardi´s equation.

Keywords: lithium-ion battery, heat flux sensor, heat generation, thermal characterization

Procedia PDF Downloads 389

Authors: Elena Collino, Dario A. Ronzio, Goffredo Decimi, Maurizio Riva

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The rapid increase of renewable energy in Italy is led by wind and solar installations. The 2017 Italian energy strategy foresees a further development of these sustainable technologies, especially solar. This fact has resulted in new opportunities, challenges, and different problems to deal with. The growth of renewables allows to meet the European requirements regarding energy and environmental policy, but these types of sources are difficult to manage because they are intermittent and non-programmable. Operationally, these characteristics can lead to instability on the voltage profile and increasing uncertainty on energy reserve scheduling. The increasing renewable production must be considered with more and more attention especially by the Transmission System Operator (TSO). The TSO, in fact, every day provides orders on energy dispatch, once the market outcome has been determined, on extended areas, defined mainly on the basis of power transmission limitations. In Italy, six market zone are defined: Northern-Italy, Central-Northern Italy, Central-Southern Italy, Southern Italy, Sardinia, and Sicily. An accurate hourly renewable power forecasting for the day-ahead on these extended areas brings an improvement both in terms of dispatching and reserve management. In this study, an operational forecasting tool of the hourly solar output for the six Italian market zones is presented, and the performance is analysed. The implementation is carried out by means of a numerical weather prediction model, coupled with a statistical post-processing in order to derive the power forecast on the basis of the meteorological projection. The weather forecast is obtained from the limited area model RAMS on the Italian territory, initialized with IFS-ECMWF boundary conditions. The post-processing calculates the solar power production with the Analog Ensemble technique (AN). This statistical approach forecasts the production using a probability distribution of the measured production registered in the past when the weather scenario looked very similar to the forecasted one. The similarity is evaluated for the components of the solar radiation: global (GHI), diffuse (DIF) and direct normal (DNI) irradiation, together with the corresponding azimuth and zenith solar angles. These are, in fact, the main factors that affect the solar production. Considering that the AN performance is strictly related to the length and quality of the historical data a training period of more than one year has been used. The training set is made by historical Numerical Weather Prediction (NWP) forecasts at 12 UTC for the GHI, DIF and DNI variables over the Italian territory together with corresponding hourly measured production for each of the six zones. The AN technique makes it possible to estimate the aggregate solar production in the area, without information about the technologic characteristics of the all solar parks present in each area. Besides, this information is often only partially available. Every day, the hourly solar power forecast for the six Italian market zones is made publicly available through a website.

Keywords: analog ensemble, electricity market, PV forecast, solar energy

Procedia PDF Downloads 158

Authors: Kumari Sweta, Ajanta Goswami, Abhilasha Dixit

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Landslide is a geomorphic process that plays an essential role in the evolution of the hill-slope and long-term landscape evolution. But its abrupt nature and the associated catastrophic forces of the process can have undesirable socio-economic impacts, like substantial economic losses, fatalities, ecosystem, geomorphologic and infrastructure disturbances. The estimated fatality rate is approximately 1person /100 sq. Km and the average economic loss is more than 550 crores/year in the Himalayan belt due to landslides. This study presents a comparative performance of a statistical bivariate method and a machine learning technique for landslide susceptibility mapping in and around Dharamshala, Himachal Pradesh. The final produced landslide susceptibility maps (LSMs) with better accuracy could be used for land-use planning to prevent future losses. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. A total of 209 landslide locations were identified in using high-resolution linear imaging self-scanning (LISS IV) data. The thematic maps of parameters influencing landslide occurrence were generated using remote sensing and other ancillary data in the GIS environment. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. LSMs were prepared using information value (Info Val), and Fuzzy Expert System (FES) models. Info Val is a statistical bivariate method, in which information values were calculated as the ratio of the landslide pixels per factor class (Si/Ni) to the total landslide pixel per parameter (S/N). Using this information values all parameters were reclassified and then summed in GIS to obtain the landslide susceptibility index (LSI) map. The FES method is a machine learning technique based on ‘mean and neighbour’ strategy for the construction of fuzzifier (input) and defuzzifier (output) membership function (MF) structure, and the FR method is used for formulating if-then rules. Two types of membership structures were utilized for membership function Bell-Gaussian (BG) and Trapezoidal-Triangular (TT). LSI for BG and TT were obtained applying membership function and if-then rules in MATLAB. The final LSMs were spatially and statistically validated. The validation results showed that in terms of accuracy, Info Val (83.4%) is better than BG (83.0%) and TT (82.6%), whereas, in terms of spatial distribution, BG is best. Hence, considering both statistical and spatial accuracy, BG is the most accurate one.

Keywords: bivariate statistical techniques, BG and TT membership structure, fuzzy expert system, information value method, machine learning technique

Procedia PDF Downloads 127

Authors: Jianli Jiang, Bai-Chen Xie

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The climate issue has aroused global concern. Achieving sustainable development is a good path for countries to mitigate environmental and climatic pressures, although there are many difficulties. The first step towards sustainable development is to evaluate the environmental efficiency of the energy industry with proper methods. The power sector is a major source of CO2, SO2, and NOx emissions. Evaluating the environmental efficiency (EE) of power systems is the premise to alleviate the terrible situation of energy and the environment. Data Envelopment Analysis (DEA) has been widely used in efficiency studies. However, measuring the efficiency of a system (be it a nation, region, sector, or business) is a challenging task. The classic DEA takes the decision-making units (DMUs) as independent, which neglects the interaction between DMUs. While ignoring these inter-regional links may result in a systematic bias in the efficiency analysis; for instance, the renewable power generated in a certain region may benefit the adjacent regions while the SO2 and CO2 emissions act oppositely. This study proposes a spatial network DEA (SNDEA) with a slack measure that can capture the spatial spillover effects of inputs/outputs among DMUs to measure efficiency. This approach is used to study the EE of China's power system, which consists of generation, transmission, and distribution departments, using a panel dataset from 2014 to 2020. In the empirical example, the energy and patent inputs, the undesirable CO2 output, and the renewable energy (RE) power variables are tested for a significant spatial spillover effect. Compared with the classic network DEA, the SNDEA result shows an obvious difference tested by the global Moran' I index. From a dynamic perspective, the EE of the power system experiences a visible surge from 2015, then a sharp downtrend from 2019, which keeps the same trend with the power transmission department. This phenomenon benefits from the market-oriented reform in the Chinese power grid enacted in 2015. The rapid decline in the environmental efficiency of the transmission department in 2020 was mainly due to the Covid-19 epidemic, which hinders economic development seriously. While the EE of the power generation department witnesses a declining trend overall, this is reasonable, taking the RE power into consideration. The installed capacity of RE power in 2020 is 4.40 times that in 2014, while the power generation is 3.97 times; in other words, the power generation per installed capacity shrank. In addition, the consumption cost of renewable power increases rapidly with the increase of RE power generation. These two aspects make the EE of the power generation department show a declining trend. Incorporation of the interactions among inputs/outputs into the DEA model, this paper proposes an efficiency evaluation method on the basis of the DEA framework, which sheds some light on efficiency evaluation in regional studies. Furthermore, the SNDEA model and the spatial DEA concept can be extended to other fields, such as industry, country, and so on.

Keywords: spatial network DEA, environmental efficiency, sustainable development, power system

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Authors: Hamid Reza Mahdipoor, Mehdi Bahrami, Mohammad Bodaghi, Seyed Ali Akbar Mansoori

Abstract:

Nowadays, with stricter limitations to reduce emissions, considerable penalties are imposed if pollution limits are exceeded. Therefore, refineries, along with focusing on improving the quality of their products, are also focused on producing products with the least environmental impact. The duty of the sulfur recovery unit (SRU) is to convert H₂S gas coming from the upstream units to elemental sulfur and minimize the burning of sulfur compounds to SO₂. The Claus process is a common process for converting H₂S to sulfur, including a reaction furnace followed by catalytic reactors and sulfur condensers. In addition to a Claus section, SRUs usually consist of a tail gas treatment (TGT) section to decrease the concentration of SO₂ in the flue gas below the emission limits. To operate an SRU properly, the flow rate of combustion air to the reaction furnace must be adjusted so that the Claus reaction is performed according to stoichiometry. Accurate control of the air demand leads to an optimum recovery of sulfur during the flow and composition fluctuations in the acid gas feed. Therefore, the major control system in the SRU is the air demand control loop, which includes a feed-forward control system based on predetermined feed flow rates and a feed-back control system based on the signal from the tail gas online analyzer. The use of online analyzers requires compliance with the installation and operation instructions. Unfortunately, most of these analyzers in Iran are out of service for different reasons, like the low importance of environmental issues and a lack of access to after-sales services due to sanctions. In this paper, an SRU in Iran was simulated and calibrated using industrial experimental data. Afterward, the effect of the malfunction of the online analyzer on the performance of SRU was investigated using the calibrated simulation. The results showed that an increase in the SO₂ concentration in the tail gas led to an increase in the temperature of the reduction reactor in the TGT section. This increase in temperature caused the failure of TGT and increased the concentration of SO₂ from 750 ppm to 35,000 ppm. In addition, the lack of a control system for the adjustment of the combustion air caused further increases in SO₂ emissions. In some processes, the major variable cannot be controlled directly due to difficulty in measurement or a long delay in the sampling system. In these cases, a secondary variable, which can be measured more easily, is considered to be controlled. With the correct selection of this variable, the main variable is also controlled along with the secondary variable. This strategy for controlling a process system is referred to as inferential control" and is considered in this paper. Therefore, a sensitivity analysis was performed to investigate the sensitivity of other measurable parameters to input disturbances. The results revealed that the output temperature of the first Claus reactor could be used for inferential control of the combustion air. Applying this method to the operation led to maximizing the sulfur recovery in the Claus section.

Keywords: sulfur recovery, online analyzer, inferential control, SO₂ emission

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

Abstract:

Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Asegid Belay Kebede, Getachew Biru Worku

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The Earth and its inhabitants have faced an existential threat as a result of severe manmade actions. Global warming and climate change have been the most apparent manifestations of this threat throughout the world, with increasingly intense heat waves, temperature rises, flooding, sea-level rise, ice sheet melting, and so on. One of the major contributors to this disaster is the ever-increasing production and consumption of energy, which is still primarily fossil-based and emits billions of tons of hazardous GHG. The transportation industry is recognized as the biggest actor in terms of emissions, accounting for 24% of direct CO2 emissions and being one of the few worldwide sectors where CO2 emissions are still growing. Rail transportation, which includes all from light rail transit to high-speed rail services, is regarded as one of the most efficient modes of transportation, accounting for 9% of total passenger travel and 7% of total freight transit. Nonetheless, there is still room for improvement in the transportation sector, which might be done by incorporating alternative and/or renewable energy sources. As a result of these rapidly changing global energy situations and rapidly dwindling fossil fuel supplies, we were driven to analyze the possibility of renewable energy sources for traction applications. Even a small achievement in energy conservation or harnessing might significantly influence the total railway system and have the potential to transform the railway sector like never before. As a result, the paper begins by assessing the potential for photovoltaic (PV) power generation on train rooftops and existing infrastructure such as railway depots, passenger stations, traction substation rooftops, and accessible land along rail lines. As a result, a method based on a Google Earth system (using Helioscopes software) is developed to assess the PV potential along rail lines and on train station roofs. As an example, the Addis Ababa light rail transit system (AA-LRTS) is utilized. The case study examines the electricity-generating potential and economic performance of photovoltaics installed on AALRTS. As a consequence, the overall capacity of solar systems on all stations, including train rooftops, reaches 72.6 MWh per day, with an annual power output of 10.6 GWh. Throughout a 25-year lifespan, the overall CO2 emission reduction and total profit from PV-AA-LRTS can reach 180,000 tons and 892 million Ethiopian birrs, respectively. The PV-AA-LRTS has a 200% return on investment. All PV stations have a payback time of less than 13 years, and the price of solar-generated power is less than $0.08/kWh, which can compete with the benchmark price of coal-fired electricity. Our findings indicate that PV-AA-LRTS has tremendous potential, with both energy and economic advantages.

Keywords: sustainable development, global warming, energy crisis, photovoltaic energy conversion, techno-economic analysis, transportation system, light rail transit

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Authors: H. Weigelt-Marom

Abstract:

Over the years and up to the arousal of the COVID-19 pandemic, deaf or hard of hearing students studying in higher education institutions, participated lectures on campus using hearing aids and strategies adapted for frontal learning in a classroom. Usually, these aids were well known to them from their earlier study experience in school. However, the transition to online lessons, due to the latest pandemic, led deaf or hard of hearing students to study outside of their physical, well known learning environment. The change of learning environment and structure rose new challenges for these students. The present study examined the learning experience, limitations, challenges and benefits regarding learning online with lecture and classmates via the “Zoom” video conference program, among deaf or hard of hearing students in academia setting. In addition, emotional and social aspects related to learning in general versus the “Zoom” were examined. The study included 18 students diagnosed as deaf or hard of hearing, studying in various higher education institutions in Israel. All students had experienced lessons on the “Zoom”. Following allocation of the group study by the deaf and hard of hearing non-profit organization “Ma’agalei Shema”, and receiving the participants inform of consent, students were requested to answer a google form questioner and participate in an interview. The questioner included background information (e.g., age, year of studying, faculty etc.), level of computer literacy, and level of hearing and forms of communication (e.g., lip reading, sign language etc.). The interviews included a one on one, semi-structured, in-depth interview, conducted by the main researcher of the study (interview duration: up to 60 minutes). The interviews were held on “ZOOM” using specific adaptations for each interviewee: clear face screen of the interviewer for lip and face reading, and/ or professional sign language or live text transcript of the conversation. Additionally, interviewees used their audio devices if needed. Questions regarded: learning experience, difficulties and advantages studying using “Zoom”, learning in a classroom versus on “Zoom”, and questions concerning emotional and social aspects related to learning. Thematic analysis of the interviews revealed severe difficulties regarding the ability of deaf or hard of hearing students to comprehend during ”Zoom“ lessons without adoptive aids. For example, interviewees indicated difficulties understanding “Zoom” lessons due to their inability to use hearing devices commonly used by them in the classroom (e.g., FM systems). 80% indicated that they could not comprehend “Zoom” lessons since they could not see the lectures face, either because lectures did not agree to open their cameras or, either because they did not keep a straight forward clear face appearance while teaching. However, not all descriptions regarded learning via the “zoom” were negative. For example, 20% reported the recording of “Zoom” lessons as a main advantage. Enabling then to repeatedly watch the lessons at their own pace, mostly assisted by friends and family to translate the audio output into an accessible input. These finding and others regarding the learning experience of the group study on the “Zoom”, as well as their recommendation to enable deaf or hard of hearing students to study inclusively online, will be presented at the conference.

Keywords: deaf or hard of hearing, learning experience, Zoom, qualitative research

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