Search results for: machine migration

Authors: Narendra Akhadkar, Silvestre Cano, Christophe Gourru

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Injection-molded parts are widely used in power system protection products. One of the biggest challenges in an injection molding process is shrinkage and warpage of the molded parts. All these geometrical variations may have an adverse effect on the quality of the product, functionality, cost, and time-to-market. The situation becomes more challenging in the case of intricate shapes and in mass production using multi-cavity tools. To control the effects of shrinkage and warpage, it is very important to correctly find out the input parameters that could affect the product performance. With the advances in the computer-aided engineering (CAE), different tools are available to simulate the injection molding process. For our case study, we used the MoldFlow insight tool. Our aim is to predict the spread of the functional dimensions and geometrical variations on the part due to variations in the input parameters such as material viscosity, packing pressure, mold temperature, melt temperature, and injection speed. The input parameters may vary during batch production or due to variations in the machine process settings. To perform the accurate product assembly variation simulation, the first step is to perform an individual part variation simulation to render realistic tolerance ranges. In this article, we present a method to simulate part variations coming from the input parameters variation during batch production. The method is based on computer simulations and experimental validation using the full factorial design of experiments (DoE). The robustness of the simulation model is verified through input parameter wise sensitivity analysis study performed using simulations and experiments; all the results show a very good correlation in the material flow direction. There exists a non-linear interaction between material and the input process variables. It is observed that the parameters such as packing pressure, material, and mold temperature play an important role in spread on functional dimensions and geometrical variations. This method will allow us in the future to develop accurate/realistic virtual prototypes based on trusted simulated process variation and, therefore, increase the product quality and potentially decrease the time to market.

Keywords: correlation, molding process, tolerance, sensitivity analysis, variation simulation

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Henry Memczak, Marc Hovestaedt, Bernhard Ay, Sandra Saenger, Thorsten Wolff, Frank F. Bier

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The influenza viruses cause flu epidemics every year and serious pandemics in larger time intervals. The only cost-effective protection against influenza is vaccination. Due to rapid mutation continuously new subtypes appear, what requires annual reimmunization. For a correct vaccination recommendation, the circulating influenza strains had to be detected promptly and exactly and characterized due to their antigenic properties. During the flu season 2016/17, a wrong vaccination recommendation has been given because of the great time interval between identification of the relevant influenza vaccine strains and outbreak of the flu epidemic during the following winter. Due to such recurring incidents of vaccine mismatches, there is a great need to speed up the process chain from identifying the right vaccine strains to their administration. The monitoring of subtypes as part of this process chain is carried out by national reference laboratories within the WHO Global Influenza Surveillance and Response System (GISRS). To this end, thousands of viruses from patient samples (e.g., throat smears) are isolated and analyzed each year. Currently, this analysis involves complex and time-intensive (several weeks) animal experiments to produce specific hyperimmune sera in ferrets, which are necessary for the determination of the antigen profiles of circulating virus strains. These tests also bear difficulties in standardization and reproducibility, which restricts the significance of the results. To replace this test a peptide-based assay for influenza virus subtyping from corresponding virus samples was developed. The differentiation of the viruses takes place by a set of specifically designed peptidic recognition molecules which interact differently with the different influenza virus subtypes. The differentiation of influenza subtypes is performed by pattern recognition guided by machine learning algorithms, without any animal experiments. Synthetic peptides are immobilized in multiplex format on various platforms (e.g., 96-well microtiter plate, microarray). Afterwards, the viruses are incubated and analyzed comparing different signaling mechanisms and a variety of assay conditions. Differentiation of a range of influenza subtypes, including H1N1, H3N2, H5N1, as well as fine differentiation of single strains within these subtypes is possible using the peptide-based subtyping platform. Thereby, the platform could be capable of replacing the current antigenic characterization of influenza strains using ferret hyperimmune sera.

Keywords: antigenic characterization, influenza-binding peptides, influenza subtyping, influenza surveillance

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Authors: Shinji Kajiwara

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The basic ability of a vehicle is the ability to “run”, “turn” and “stop”. The safeness and comfort during a drive on various road surfaces and speed depends on the performance of these basic abilities of the vehicle. Stability and maneuverability of a vehicle is vital in automotive engineering. Stability of a vehicle is the ability of the vehicle to revert back to a stable state during a drive when faced with crosswind and irregular road conditions. Maneuverability of a vehicle is the ability of the vehicle to change direction during a drive swiftly based on the steering of the driver. The stability and maneuverability of a vehicle can also be defined as the driving stability of the vehicle. Since fossil fueled vehicle is the main type of transportation today, the environmental factor in automotive engineering is also vital. By improving the fuel efficiency of the vehicle, the overall carbon emission will be reduced thus reducing the effect of global warming and greenhouse gas on the Earth. Another main focus of the automotive engineering is the safety performance of the vehicle especially with the worrying increase of vehicle collision every day. With better safety performance on a vehicle, every driver will be more confidence driving every day. Next, let us focus on the “turn” ability of a vehicle. By improving this particular ability of the vehicle, the cornering limit of the vehicle can be improved thus increasing the stability and maneuverability factor. In order to improve the cornering limit of the vehicle, a study to find the balance between the steering systems, the stability of the vehicle, higher lateral acceleration and the cornering limit detection must be conducted. The aim of this research is to study and develop a new suspension system that that will boost the lateral acceleration of the vehicle and ultimately improving the cornering limit of the vehicle. This research will also study environmental factor and the stability factor of the new suspension system. The double wishbone suspension system is widely used in four-wheel vehicle especially for high cornering performance sports car and racing car. The double wishbone designs allow the engineer to carefully control the motion of the wheel by controlling such parameters as camber angle, caster angle, toe pattern, roll center height, scrub radius, scuff and more. The development of the new suspension system will focus on the ability of the new suspension system to optimize the camber control and to improve the camber limit during a cornering motion. The research will be carried out using the CAE analysis tool. Using this analysis tool we will develop a JSAE Formula Machine equipped with the double wishbone system and also the new suspension system and conduct simulation and conduct studies on performance of both suspension systems.

Keywords: automobile, camber thrust, cornering force, suspension

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Authors: Kanika Gupta, Ashok Kumar

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Biofilms are dense, highly hydrated cell clusters that are irreversibly attached to a substratum, to an interface or to each other, and are embedded in a self-produced gelatinous matrix composed of extracellular polymeric substances. Research in biofilm field has become very significant, as biofilm has shown high mechanical resilience and resistance to antibiotic treatment and constituted as a significant problem in both healthcare and other industry related to microorganisms. The massive information both stated and hidden in the biofilm literature are growing exponentially therefore it is not possible for researchers and practitioners to automatically extract and relate information from different written resources. So, the current work proposes and discusses the use of text mining techniques for the extraction of information from biofilm literature corpora containing 34306 documents. It is very difficult and expensive to obtain annotated material for biomedical literature as the literature is unstructured i.e. free-text. Therefore, we considered unsupervised approach, where no annotated training is necessary and using this approach we developed a system that will classify the text on the basis of growth and development, drug effects, radiation effects, classification and physiology of biofilms. For this, a two-step structure was used where the first step is to extract keywords from the biofilm literature using a metathesaurus and standard natural language processing tools like Rapid Miner_v5.3 and the second step is to discover relations between the genes extracted from the whole set of biofilm literature using pubmed.mineR_v1.0.11. We used unsupervised approach, which is the machine learning task of inferring a function to describe hidden structure from 'unlabeled' data, in the above-extracted datasets to develop classifiers using WinPython-64 bit_v3.5.4.0Qt5 and R studio_v0.99.467 packages which will automatically classify the text by using the mentioned sets. The developed classifiers were tested on a large data set of biofilm literature which showed that the unsupervised approach proposed is promising as well as suited for a semi-automatic labeling of the extracted relations. The entire information was stored in the relational database which was hosted locally on the server. The generated biofilm vocabulary and genes relations will be significant for researchers dealing with biofilm research, making their search easy and efficient as the keywords and genes could be directly mapped with the documents used for database development.

Keywords: biofilms literature, classifiers development, text mining, unsupervised learning approach, unstructured data, relational database

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Authors: Bruno Cossu, Elio Carlo

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The invention (patent pending) relates to the field of devices aimed to harness wave energy (WEC) especially for artificial upwelling, forced downwelling, production of compressed air. In its basic form, the pump consists of a hydro-pneumatic machine, driven by wave energy, characterised by the fact that it has no moving mechanical parts, and is made up of only two structural components: an hollow body, which is open at the bottom to the sea and partially immersed in sea water, and a tube, both joined together to form a single body. The shape of the hollow body is like a mushroom whose cap and stem are hollow; the stem is open at both ends and the lower part of its surface is crossed by holes; the tube is external and coaxial to the stem and is joined to it so as to form a single body. This shape of the hollow body and the type of connection to the tube allows the pump to operate simultaneously as an air compressor (OWC) on the cap side, and as an airlift on the stem side. The pump can be implemented in four versions, each of which provides different variants and methods of implementation: 1) firstly, for the artificial upwelling of cold, deep ocean water; 2) secondly, for the lifting and transfer of these waters to the place of use (above all, fish farming plants), even if kilometres away; 3) thirdly, for the forced downwelling of surface sea water; 4) fourthly, for the forced downwelling of surface water, its oxygenation, and the simultaneous production of compressed air. The transfer of the deep water or the downwelling of the raised surface water (as for pump versions indicated in points 2 and 3 above), is obtained by making the water raised by the airlift flow into the upper inlet of another pipe, internal or adjoined to the airlift; the downwelling of raised surface water, oxygenation, and the simultaneous production of compressed air (as for the pump version indicated in point 4), is obtained by installing a venturi tube on the upper end of the pipe, whose restricted section is connected to the external atmosphere, so that it also operates like a hydraulic air compressor (trompe). Furthermore, by combining one or more pumps for the upwelling of cold, deep water, with one or more pumps for the downwelling of the warm surface water, the system can be used in an Ocean Thermal Energy Conversion plant to supply the cold and the warm water required for the operation of the same, thus allowing to use, without increased costs, in addition to the mechanical energy of the waves, for the purposes indicated in points 1 to 4, the thermal one of the marine water treated in the process.

Keywords: air lifted upwelling, fish farming plant, hydraulic air compressor, wave energy converter

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Authors: Cherry Yieng Siang Ling, Joong Hee Lee, Myung Hwan Yun

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The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations.

Keywords: usability, qualitative data, text-processing algorithm, natural language processing

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Authors: Noureddine Henka, Sami Tazi, Mohamad Assaad

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The electrical grid is a highly intricate system designed to transfer electricity from production areas to consumption areas. The Transmission System Operator (TSO) is responsible for ensuring the efficient distribution of electricity and maintaining the grid's safety and quality. However, due to the increasing integration of intermittent renewable energy sources, there is a growing level of uncertainty, which requires a faster responsive approach. A potential solution involves the use of electrical segmentation, which involves creating coherence zones where electrical disturbances mainly remain within the zone. Indeed, by means of coherent electrical zones, it becomes possible to focus solely on the sub-zone, reducing the range of possibilities and aiding in managing uncertainty. It allows faster execution of operational processes and easier learning for supervised machine learning algorithms. Electrical segmentation can be applied to various applications, such as electrical control, minimizing electrical loss, and ensuring voltage stability. Since the electrical grid can be modeled as a graph, where the vertices represent electrical buses and the edges represent electrical lines, identifying coherent electrical zones can be seen as a clustering task on graphs, generally called community detection. Nevertheless, a critical criterion for the zones is their ability to remain resilient to the electrical evolution of the grid over time. This evolution is due to the constant changes in electricity generation and consumption, which are reflected in graph structure variations as well as line flow changes. One approach to creating a resilient segmentation is to design robust zones under various circumstances. This issue can be represented through a multiplex graph, where each layer represents a specific situation that may arise on the grid. Consequently, resilient segmentation can be achieved by conducting community detection on this multiplex graph. The multiplex graph is composed of multiple graphs, and all the layers share the same set of vertices. Our proposal involves a model that utilizes a unified representation to compute a flattening of all layers. This unified situation can be penalized to obtain (K) connected components representing the robust electrical segmentation clusters. We compare our robust segmentation to the segmentation based on a single reference situation. The robust segmentation proves its relevance by producing clusters with high intra-electrical perturbation and low variance of electrical perturbation. We saw through the experiences when robust electrical segmentation has a benefit and in which context.

Keywords: community detection, electrical segmentation, multiplex graph, power grid

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Authors: Arjun Prakash, Vinutha H., Karthik N.

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BACKGROUND: Carpal tunnel syndrome (CTS) is a common entrapment neuropathy with an estimated prevalence of 0.6 - 5.8% in the general adult population. It is caused by compression of the Median Nerve (MN) at the wrist as it passes through a narrow osteofibrous canal. Presently, the diagnosis is established by the clinical symptoms and physical examination and Nerve conduction study (NCS) is used to assess its severity. However, it is considered to be painful, time consuming and expensive, with a false-negative rate between 16 - 34%. Ultrasonography (USG) is now increasingly used as a diagnostic tool in CTS due to its non-invasive nature, increased accessibility and relatively low cost. Elastography is a newer modality in USG which helps to assess stiffness of tissues. However, there is limited available literature about its applications in peripheral nerves. OBJECTIVES: Our objectives were to measure the Cross-Sectional Area (CSA) and elasticity of MN at the carpal tunnel using Grey scale Ultrasonography (USG), Strain Elastography (SE) and Shear Wave Elastography (SWE). We also made an attempt to independently evaluate the role of Gray scale USG, SE and SWE in grading the severity of CTS, keeping NCS as the gold standard. MATERIALS AND METHODS: After approval from the Institutional Ethics Review Board, we conducted a comparative cross sectional study for a period of 18 months. The participants were divided into two groups. Group A consisted of 54 patients with clinically diagnosed CTS who underwent NCS, and Group B consisted of 50 controls without any clinical symptoms of CTS. All Ultrasound examinations were performed on SAMSUNG RS 80 EVO Ultrasound machine with 2 - 9 Mega Hertz linear probe. In both groups, CSA of the MN was measured on Grey scale USG, and its elasticity was measured at the carpal tunnel (in terms of Strain ratio and Shear Modulus). The variables were compared between both groups by using ‘Independent t test’, and subgroup analyses were performed using one-way analysis of variance. Receiver operating characteristic curves were used to evaluate the diagnostic performance of each variable. RESULTS: The mean CSA of the MN was 13.60 + 3.201 mm2 and 9.17 + 1.665 mm2 in Group A and Group B, respectively (p < 0.001). The mean SWE was 30.65 + 12.996 kPa and 17.33 + 2.919 kPa in Group A and Group B, respectively (p < 0.001), and the mean Strain ratio was 7.545 + 2.017 and 5.802 + 1.153 in Group A and Group B respectively (p < 0.001). CONCLUSION: The combined use of Gray scale USG, SE and SWE is extremely useful in grading the severity of CTS and can be used as a painless and cost-effective alternative to NCS. Early diagnosis and grading of CTS and effective treatment is essential to avoid permanent nerve damage and functional disability.

Keywords: carpal tunnel, ultrasound, elastography, nerve conduction study

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Authors: Yonwaba Atyosi, Moses Cho, Abel Ramoelo, Nobuhle Majozi, Cecilia Masemola, Yoliswa Mkhize

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Spectroscopy has been widely used to understand the hyperspectral remote sensing of soils. Accurate and efficient measurement of soil moisture is essential for precision agriculture. The aim of this study was to understand the spectral behavior of soil at different soil water content levels and identify the significant spectral bands for soil moisture content retrieval at field-scale. The study consisted of 60 soil samples from a maize farm, divided into four different treatments representing different moisture levels. Spectral signatures were measured for each sample in laboratory under artificial light using an Analytical Spectral Device (ASD) spectrometer, covering a wavelength range from 350 nm to 2500 nm, with a spectral resolution of 1 nm. The results showed that the absorption features at 1450 nm, 1900 nm, and 2200 nm were particularly sensitive to soil moisture content and exhibited strong correlations with the water content levels. Continuum removal was developed in the R programming language to enhance the absorption features of soil moisture and to precisely understand its spectral behavior at different water content levels. Statistical analysis using partial least squares regression (PLSR) models were performed to quantify the correlation between the spectral bands and soil moisture content. This study provides insights into the spectral behavior of soil at different water content levels and identifies the significant spectral bands for soil moisture content retrieval. The findings highlight the potential of spectroscopy for non-destructive and rapid soil moisture measurement, which can be applied to various fields such as precision agriculture, hydrology, and environmental monitoring. However, it is important to note that the spectral behavior of soil can be influenced by various factors such as soil type, texture, and organic matter content, and caution should be taken when applying the results to other soil systems. The results of this study showed a good agreement between measured and predicted values of Soil Moisture Content with high R2 and low root mean square error (RMSE) values. Model validation using independent data was satisfactory for all the studied soil samples. The results has significant implications for developing high-resolution and precise field-scale soil moisture retrieval models. These models can be used to understand the spatial and temporal variation of soil moisture content in agricultural fields, which is essential for managing irrigation and optimizing crop yield.

Keywords: soil moisture content retrieval, precision agriculture, continuum removal, remote sensing, machine learning, spectroscopy

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Predrag Simović, Katarina Stojanović, Milena Radenković, Dimitrija Savić Zdravković, Aleksandar Milosavljević, Bratislav Predić, Milenka Božanić, Ana Petrović, Djuradj Milošević

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Mayflies (Ephemeroptera), stoneflies (Plecoptera), and caddisflies (Trichoptera) (collectively referred to as EPT) are key participants in most freshwater habitats and often exhibit high diversity. Moreover, their presence and relative abundance are used in freshwater ecological and biomonitoring studies. Current methods for freshwater ecosystem biomonitoring follow a traditional approach of taxa monitoring based on morphological characters, which is time-consuming, and often generates data sets with low taxonomic resolution and unverifiable identification precision. To assist in solving identification problems and contribute to the knowledge of the distribution of many species, there was a need to develop alternative approaches in macroinvertebrate sample identification. Here, we establish an automatic machine-based identification approach for EPT taxa (Insect) using deep Convolutional Neural Networks (CNNs) and computer vision to increase the efficiency and taxonomic resolution in biomonitoring. The 5 550 specimens were collected from freshwater ecosystems of Serbia, and the deep model was built upon 90 EPT taxa. The protocol for obtaining images included the following stages: taxonomic identification by human experts and DNA barcoding validation, mounting the larvae, and photographing the dorsal side using a stereomicroscope and camera (16 650 individuals). The most informative image regions (the dorsal segments of individuals) for the decision-making process in the deep learning model were visualized using Gradient Weighted Class Activation Mapping (Grad-CAM). After training the artificial neural network, a CNN model was then built that was able to classify the 90 EPT taxa into their respective taxonomic categories automatically with 98.7%. Our approach offers a straightforward and efficient solution for routine monitoring programs, focusing on key biotic descriptors, such as EPT taxa. In addition, this application provides a streamlined solution that not only saves time, reduces equipment and expert requirements but also significantly enhances reliability and information content. The identification of the EPT larvae is difficult because of the variation of morphological features even within a single genus or the close resemblance of several species, and therefore, future research should focus on increasing the number of entities (species) in the model.

Keywords: convolutional neural networks, DNA barcoding, EPT taxa, biomonitoring

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Authors: Temitayo Abayomi Ewemoje, Oluwamayowa Oluwafemi Oluwaniyi

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Agriculture and food processing, industry are among the largest industrial sectors that uses large amount of energy. Thus, a larger amount of gases from their fuel combustion technologies is being released into the environment. The choice of input energy supply not only directly having affects the environment, but also poses a threat to human health. The study was therefore designed to assess each unit production processes in order to identify hotspots using life cycle assessments (LCA) approach in South-western Nigeria. Data such as machine power rating, operation duration, inputs and outputs of shea butter materials for unit processes obtained at site were used to modelled Life Cycle Impact Analysis on GaBi6 (Holistic Balancing) software. Four scenarios were drawn for the impact assessments. Material sourcing from Kaiama, Scenarios 1, 3 and Minna Scenarios 2, 4 but different heat supply sources (Liquefied Petroleum Gas ‘LPG’ Scenarios 1, 2 and 10.8 kW Diesel Heater, scenarios 3, 4). Modelling of shea butter production on GaBi6 was for 1kg functional unit of shea butter produced and the Tool for the Reduction and Assessment of Chemical and other Environmental Impacts (TRACI) midpoint assessment was tool used to was analyse the life cycle inventories of the four scenarios. Eight categories in all four Scenarios were observed out of which three impact categories; Global Warming Potential (GWP) (0.613, 0.751, 0.661, 0.799) kg CO2¬-Equiv., Acidification Potential (AP) (0.112, 0.132, 0.129, 0.149) kg H+ moles-Equiv., and Smog (0.044, 0.059, 0.049, 0.063) kg O3-Equiv., categories had the greater impacts on the environment in Scenarios 1-4 respectively. Impacts from transportation activities was also seen to contribute more to these environmental impact categories due to large volume of petrol combusted leading to releases of gases such as CO2, CH4, N2O, SO2, and NOx into the environment during the transportation of raw shea kernel purchased. The ratio of transportation distance from Minna and Kaiama to production site was approximately 3.5. Shea butter unit processes with greater impacts in all categories was the packaging, milling and with the churning processes in ascending order of magnitude was identified as hotspots that may require attention. From the 1kg shea butter functional unit, it was inferred that locating production site at the shortest travelling distance to raw material sourcing and combustion of LPG for heating would reduce all the impact categories assessed on the environment.

Keywords: GaBi6, Life cycle assessment, shea butter production, TRACI

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Authors: Tim Crocker-Buque, Sandra Mounier-Jack, Natasha Howard

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Background: Due to both the increasing scale and speed of urbanisation, urban areas in low and middle-income countries (LMICs) host increasingly large populations of under-immunized children, with the additional associated risks of rapid disease transmission in high-density living environments. Multiple interdependent factors are associated with these coverage disparities in urban areas and most evidence comes from relatively few countries, e.g., predominantly India, Kenya, Nigeria, and some from Pakistan, Iran, and Brazil. This study aimed to identify, describe, and assess the main tools used to measure or improve coverage of immunisation services in poor urban areas. Methods: Authors used a qualitative review design, including academic and non-academic literature, to identify tools used to improve coverage of public health interventions in urban areas. Authors selected and extracted sources that provided good examples of specific tools, or categories of tools, used in a context relevant to urban immunization. Diagnostic (e.g., for data collection, analysis, and insight generation) and programme tools (e.g., for investigating or improving ongoing programmes) and interventions (e.g., multi-component or stand-alone with evidence) were selected for inclusion to provide a range of type and availability of relevant tools. These were then prioritised using a decision-analysis framework and a tool selection guide for programme managers developed. Results: Authors reviewed tools used in urban immunisation contexts and tools designed for (i) non-immunization and/or non-health interventions in urban areas, and (ii) immunisation in rural contexts that had relevance for urban areas (e.g., Reaching every District/Child/ Zone). Many approaches combined several tools and methods, which authors categorised as diagnostic, programme, and intervention. The most common diagnostic tools were cross-sectional surveys, key informant interviews, focus group discussions, secondary analysis of routine data, and geographical mapping of outcomes, resources, and services. Programme tools involved multiple stages of data collection, analysis, insight generation, and intervention planning and included guidance documents from WHO (World Health Organisation), UNICEF (United Nations Children's Fund), USAID (United States Agency for International Development), and governments, and articles reporting on diagnostics, interventions, and/or evaluations to improve urban immunisation. Interventions involved service improvement, education, reminder/recall, incentives, outreach, mass-media, or were multi-component. The main gaps in existing tools were an assessment of macro/policy-level factors, exploration of effective immunization communication channels, and measuring in/out-migration. The proposed framework uses a problem tree approach to suggest tools to address five common challenges (i.e. identifying populations, understanding communities, issues with service access and use, improving services, improving coverage) based on context and available data. Conclusion: This study identified many tools relevant to evaluating urban LMIC immunisation programmes, including significant crossover between tools. This was encouraging in terms of supporting the identification of common areas, but problematic as data volumes, instructions, and activities could overwhelm managers and tools are not always suitably applied to suitable contexts. Further research is needed on how best to combine tools and methods to suit local contexts. Authors’ initial framework can be tested and developed further.

Keywords: health equity, immunisation, low and middle-income countries, poverty, urban health

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Authors: Michael T. Lobmann, Camilla Wellstein, Stefan Zerbe

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Vegetation plays an important role for the stabilization of slopes against erosion processes, such as shallow erosion and landslides. Plant roots reinforce the soil, increase soil cohesion and often cross possible shear planes. Hence, plant roots reduce the risk of slope failure. Generally, shrub and tree roots penetrate deeper into the soil vertically, while roots of forbs and grasses are concentrated horizontally in the topsoil and organic layer. Therefore, shrubs and trees have a higher potential for stabilization of slopes with deep soil layers than forbs and grasses. Consequently, research mainly focused on the vertical root effects of shrubs and trees. Nevertheless, a better understanding of the stabilizing effects of grasses and forbs is needed for better evaluation of the stability of natural and artificial slopes with herbaceous vegetation. Despite the importance of vertical root effects, field observations indicate that horizontal root effects also play an important role for slope stabilization. Not only forbs and grasses, but also some shrubs and trees form tight horizontal networks of fine and coarse roots and rhizomes in the topsoil. These root networks increase soil cohesion and horizontal tensile strength. Available methods for physical measurements, such as shear-box tests, pullout tests and singular root tensile strength measurement can only provide a detailed picture of vertical effects of roots on slope stabilization. However, the assessment of horizontal root effects is largely limited to computer modeling. Here, a method for measurement of in-situ cumulative horizontal root tensile strength is presented. A traction machine was developed that allows fixation of rectangular grass sods (max. 30x60cm) on the short ends with a 30x30cm measurement zone in the middle. On two alpine grass slopes in South Tyrol (northern Italy), 30x60cm grass sods were cut out (max. depth 20cm). Grass sods were pulled apart measuring the horizontal tensile strength over 30cm width over the time. The horizontal tensile strength of the sods was measured and compared for different soil depths, hydrological conditions, and root physiological properties. The results improve our understanding of horizontal root effects on slope stabilization and can be used for improved evaluation of grass slope stability.

Keywords: grassland, horizontal root effect, landslide, mountain, pasture, shallow erosion

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis.

Keywords: wavelet analysis, pitted gear, autocorrelation, gear fault diagnosis

Procedia PDF Downloads 363

Authors: Gabriel Montague, Max Vilgalys, Catherine H. Crawford, Jorge Ortiz, Dava Newman

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There has never been so much publicly accessible atmospheric and environmental data. The possibilities of these data are exciting, but the sheer volume of available datasets represents a new challenge for researchers. The task of identifying and working with a new dataset has become more difficult with the amount and variety of available data. Datasets are often documented in ways that differ substantially from the common English used to describe the same topics. This presents a barrier not only for new scientists, but for researchers looking to find comparisons across multiple datasets or specialists from other disciplines hoping to collaborate. This paper proposes a method for addressing this obstacle: creating a knowledge graph to bridge the gap between everyday English language and the technical language surrounding these datasets. Knowledge graph generation is already a well-established field, although there are some unique challenges posed by working with Earth data. One is the sheer size of the databases – it would be infeasible to replicate or analyze all the data stored by an organization like The National Aeronautics and Space Administration (NASA) or the European Space Agency. Instead, this approach identifies topics from metadata available for datasets in NASA’s Earthdata database, which can then be used to directly request and access the raw data from NASA. By starting with a single metadata standard, this paper establishes an approach that can be generalized to different databases, but leaves the challenge of metadata harmonization for future work. Topics generated from the metadata are then linked to topics from a collection of English queries through a variety of standard and custom natural language processing (NLP) methods. The results from this method are then compared to a baseline of elastic search applied to the metadata. This comparison shows the benefits of the proposed knowledge graph system over existing methods, particularly in interpreting natural language queries and interpreting topics in metadata. For the research community, this work introduces an application of NLP to the ecological and environmental sciences, expanding the possibilities of how machine learning can be applied in this discipline. But perhaps more importantly, it establishes the foundation for a platform that can enable common English to access knowledge that previously required considerable effort and experience. By making this public data accessible to the full public, this work has the potential to transform environmental understanding, engagement, and action.

Keywords: earth metadata, knowledge graphs, natural language processing, question-answer systems

Procedia PDF Downloads 122

Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

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Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

Procedia PDF Downloads 97

Authors: Joanna Szymanska, Paulina Wawulska-Marek, Jaroslaw Mizera

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Nowadays, the rapid growth of global energy consumption and uncontrolled depletion of natural resources become a serious problem. Shale rocks are the largest and potential global basins containing hydrocarbons, trapped in closed pores of the shale matrix. Regardless of the shales origin, mining conditions are extremely unfavourable due to high reservoir pressure, great depths, increased clay minerals content and limited permeability (nanoDarcy) of the rocks. Taking into consideration such geomechanical barriers, effective extraction of natural gas from shales with plastic zones demands effective operations. Actually, hydraulic fracturing is the most developed technique based on the injection of pressurized fluid into a wellbore, to initiate fractures propagation. However, a rapid drop of pressure after fluid suction to the ground induces a fracture closure and conductivity reduction. In order to minimize this risk, proppants should be applied. They are solid granules transported with hydraulic fluids to locate inside the rock. Proppants act as a prop for the closing fracture, thus gas migration to a borehole is effective. Quartz sands are commonly applied proppants only at shallow deposits (USA). Whereas, ceramic proppants are designed to meet rigorous downhole conditions to intensify output. Ceramic granules predominate with higher mechanical strength, stability in strong acidic environment, spherical shape and homogeneity as well. Quality of ceramic proppants is conditioned by raw materials selection. Aim of this study was to obtain the proppants from aluminosilicates (the kaolinite subgroup) and mix of minerals with a high alumina content. These loamy minerals contain a tubular and platy morphology that improves mechanical properties and reduces their specific weight. Moreover, they are distinguished by well-developed surface area, high porosity, fine particle size, superb dispersion and nontoxic properties - very crucial for particles consolidation into spherical and crush-resistant granules in mechanical granulation process. The aluminosilicates were mixed with water and natural organic binder to improve liquid-bridges and pores formation between particles. Afterward, the green proppants were subjected to sintering at high temperatures. Evaluation of the minerals utility was based on their particle size distribution (laser diffraction study) and thermal stability (thermogravimetry). Scanning Electron Microscopy was useful for morphology and shape identification combined with specific surface area measurement (BET). Chemical composition was verified by Energy Dispersive Spectroscopy and X-ray Fluorescence. Moreover, bulk density and specific weight were measured. Such comprehensive characterization of loamy materials confirmed their favourable impact on the proppants granulation. The sintered granules were analyzed by SEM to verify the surface topography and phase transitions after sintering. Pores distribution was identified by X-Ray Tomography. This method enabled also the simulation of proppants settlement in a fracture, while measurement of bulk density was essential to predict their amount to fill a well. Roundness coefficient was also evaluated, whereas impact on mining environment was identified by turbidity and solubility in acid - to indicate risk of the material decay in a well. The obtained outcomes confirmed a positive influence of the loamy minerals on ceramic proppants properties with respect to the strict norms. This research is perspective for higher quality proppants production with costs reduction.

Keywords: aluminosilicates, ceramic proppants, mechanical granulation, shale gas

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Authors: Clement Yeboah, Eva Laryea

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A pretest-posttest within subjects experimental design was employed to examine the effects of a computerized basic statistics learning game on achievement and statistics-related anxiety of students enrolled in introductory graduate statistics course. Participants (N = 34) were graduate students in a variety of programs at state-funded research university in the Southeast United States. We analyzed pre-test posttest differences using paired samples t-tests for achievement and for statistics anxiety. The results of the t-test for knowledge in statistics were found to be statistically significant, indicating significant mean gains for statistical knowledge as a function of the game-based intervention. Likewise, the results of the t-test for statistics-related anxiety were also statistically significant, indicating a decrease in anxiety from pretest to posttest. The implications of the present study are significant for both teachers and students. For teachers, using computer games developed by the researchers can help to create a more dynamic and engaging classroom environment, as well as improve student learning outcomes. For students, playing these educational games can help to develop important skills such as problem solving, critical thinking, and collaboration. Students can develop an interest in the subject matter and spend quality time to learn the course as they play the game without knowing that they are even learning the presupposed hard course. The future directions of the present study are promising as technology continues to advance and become more widely available. Some potential future developments include the integration of virtual and augmented reality into educational games, the use of machine learning and artificial intelligence to create personalized learning experiences, and the development of new and innovative game-based assessment tools. It is also important to consider the ethical implications of computer game-based pedagogy, such as the potential for games to perpetuate harmful stereotypes and biases. As the field continues to evolve, it will be crucial to address these issues and work towards creating inclusive and equitable learning experiences for all students. This study has the potential to revolutionize the way basic statistics graduate students learn and offers exciting opportunities for future development and research. It is an important area of inquiry for educators, researchers, and policymakers and will continue to be a dynamic and rapidly evolving field for years to come.

Keywords: pretest-posttest within subjects, computer game-based learning, statistics achievement, statistics anxiety

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Authors: Lesley Northrop, Justin DeGrazia, Jessica Greenwood

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ASPIRA Labs now offers an en-suit and ready-to-implement technology transfer solution to enable labs and hospitals that lack the resources to build it themselves to offer in-house genetic testing. This unique platform employs a patented Molecular Inversion Probe (MIP) technology that combines the specificity of a hybrid capture protocol with the ease of an amplicon-based protocol and utilizes an advanced bioinformatics analysis pipeline based on machine learning. To demonstrate its efficacy, two independent genetic tests were validated on this technology transfer platform: expanded carrier screening (ECS) and hereditary cancer testing (HC). The analytical performance of ECS and HC was validated separately in a blinded manner for calling three different types of variants: SNVs, short indels (typically, <50 bp), and large indels/CNVs defined as multi-exonic del/dup events. The reference set was constructed using samples from Coriell Institute, an external clinical genetic testing laboratory, Maine Molecular Quality Controls Inc. (MMQCI), SeraCare and GIAB Consortium. Overall, the analytical performance showed a sensitivity and specificity of >99.4% for both ECS and HC in detecting SNVs. For indels, both tests reported specificity of 100%, and ECS demonstrated a sensitivity of 100%, whereas HC exhibited a sensitivity of 96.5%. The bioinformatics pipeline also correctly called all reference CNV events resulting in a sensitivity of 100% for both tests. No additional calls were made in the HC panel, leading to a perfect performance (specificity and F-measure of 100%). In the carrier panel, however, three additional positive calls were made outside the reference set. Two of these calls were confirmed using an orthogonal method and were re-classified as true positives leaving only one false positive. The pipeline also correctly identified all challenging carrier statuses, such as positive cases for spinal muscular atrophy and alpha-thalassemia, resulting in 100% sensitivity. After confirmation of additional positive calls via long-range PCR and MLPA, specificity for such cases was estimated at 99%. These performance metrics demonstrate that this tech-transfer solution can be confidently internalized by clinical labs and hospitals to offer mainstream ECS and HC as part of their test catalog, substantially increasing access to quality germline genetic testing for labs of all sizes and resources levels.

Keywords: clinical genetics, genetic testing, molecular genetics, technology transfer

Procedia PDF Downloads 154

Authors: Luixa Reizabal, Inaki Garcia, Eneko Sansinenea, Ainize Sarrionandia, Karmele Lopez De Ipina, Elsa Fernandez

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Although the effects of incarceration in prisons close to prisoners’ and their families’ residences have been studied, little is known about the effects of remote incarceration. The present study shows the impact of secondary prisonization on mental health of elderly parents of Basque prisoners who are incarcerated in prisons located far away from prisoners’ and their families’ residences. Secondary prisonization refers to the effects that imprisonment of a family member has on relatives. In the study, psychological effects are analyzed by means of comparative methodology. Specifically, levels of psychopathology (depression, anxiety, and stress) and positive mental health (psychological, social, and emotional well-being) are studied in a sample of parents over 65 years old of prisoners incarcerated in prisons located a long distance away (concretely, some of them in a distance of less than 400 km, while others farther than 400 km) from the Basque Country. The dataset consists of data collected through a questionnaire and from a spontaneous speech recording. The statistical and automatic analyses show that levels of psychopathology and positive mental health of elderly parents of prisoners incarcerated in remote jails are affected by the incarceration of their sons or daughters. Concretely, these parents show higher levels of depression, anxiety, and stress and lower levels of emotional (but not psychological or social) wellbeing than parents with no imprisoned daughters or sons. These findings suggest that parents with imprisoned sons or daughters suffer the impact of secondary prisonization on their mental health. When comparing parents with sons or daughters incarcerated within 400 kilometers from home and parents whose sons or daughters are incarcerated farther than 400 kilometers from home, the latter present higher levels of psychopathology, but also higher levels of positive mental health (although the difference between the two groups is not statistically significant). These findings might be explained by resilience. In fact, in traumatic situations, people can develop a force to cope with the situation, and even present a posttraumatic growth. Bearing in mind all these findings, it could be concluded that secondary prisonization implies for elderly parents with sons or daughters incarcerated in remote jails suffering and, in consequence, that changes in the penitentiary policy applied to Basque prisoners are required in order to finish this suffering.

Keywords: automatic spontaneous speech analysis, elderly parents, machine learning, positive mental health, psychopathology, remote incarceration, secondary prisonization

Procedia PDF Downloads 249

Authors: Muhammad Zaman Shakir, Mingfa Yao, Zohaib Iqbal

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This study proposed four Jet fuel surrogate (S1, S2 S3, and 4) with careful selection of seven large hydrocarbon fuel components, ranging from C₉-C₁₆ of higher molecular weight and higher boiling point, adapting the standard molecular distribution size of the actual jet fuel. The surrogate was composed of seven components, including n-propyl cyclohexane (C₉H₁₈), n- propylbenzene (C₉H₁₂), n-undecane (C₁₁H₂₄), n- dodecane (C₁₂H₂₆), n-tetradecane (C₁₄H₃₀), n-hexadecane (C₁₆H₃₄) and iso-cetane (iC₁₆H₃₄). The skeletal jet fuel surrogate reaction mechanism was developed by two approaches, firstly based on a decoupling methodology by describing the C₄ -C₁₆ skeletal mechanism for the oxidation of heavy hydrocarbons and a detailed H₂ /CO/C₁ mechanism for prediction of oxidation of small hydrocarbons. The combined skeletal jet fuel surrogate mechanism was compressed into 128 species, and 355 reactions and thereby can be used in computational fluid dynamics (CFD) simulation. The extensive validation was performed for individual single-component including ignition delay time, species concentrations profile and laminar flame speed based on various fundamental experiments under wide operating conditions, and for their blended mixture, among all the surrogate, S1 has been extensively validated against the experimental data in a shock tube, rapid compression machine, jet-stirred reactor, counterflow flame, and premixed laminar flame over wide ranges of temperature (700-1700 K), pressure (8-50 atm), and equivalence ratio (0.5-2.0) to capture the properties target fuel Jet-A, while the rest of three surrogate S2, S3 and S4 has been validated for Shock Tube ignition delay time only to capture the ignition characteristic of target fuel S-8 & GTL, IPK and RP-3 respectively. Based on the newly proposed HyChem model, another four surrogate with similar components and composition, was developed and parallel validations data was used as followed for previously developed surrogate but at high-temperature condition only. After testing the mechanism prediction performance of surrogates developed by the decoupling methodology, the comparison was done with the results of surrogates developed by the HyChem model. It was observed that all of four proposed surrogates in this study showed good agreement with the experimental measurements and the study comes to this conclusion that like the decoupling methodology HyChem model also has a great potential for the development of oxidation mechanism for heavy alkanes because of applicability, simplicity, and compactness.

Keywords: computational fluid dynamics, decoupling methodology Hychem, jet fuel, surrogate, skeletal mechanism

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Authors: Vahid Asadzadeh, Amin Ataee

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Since the advent of social media, there has been a wave of optimism among researchers and civic activists about the influence of virtual networks on the democratization process, which has gradually waned. One of the lesser-known concerns is how to increase the possibility of hearing the voices of different minorities. According to the theory of media logic, the media, using their technological capabilities, act as a structure through which events and ideas are interpreted. Social media, through the use of the learning machine and the use of algorithms, has formed a kind of structure in which the voices of minorities and less popular topics are lost among the commotion of the trends. In fact, the recommended systems and algorithms used in social media are designed to help promote trends and make popular content more popular, and content that belongs to minorities is constantly marginalized. As social networks gradually play a more active role in politics, the possibility of freely participating in the reproduction and reinterpretation of structures in general and political structures in particular (as Laclau‎ and Mouffe had in mind‎) can be considered as criteria to democracy in action. The point is that the media logic of virtual networks is shaped by the rule and even the tyranny of the majority, and this logic does not make it possible to design a self-foundation and self-revolutionary model of democracy. In other words, today's social networks, though seemingly full of variety But they are governed by the logic of homogeneity, and they do not have the possibility of multiplicity as is the case in immanent radical democracies (influenced by Gilles Deleuze). However, with the emergence and increasing popularity of Clubhouse as a new social media, there seems to be a shift in the social media space, and that is the diminishing role of algorithms and systems reconditioners as content delivery interfaces. This has led to the fact that in the Clubhouse, the voices of minorities are better heard, and the diversity of political tendencies manifests itself better. The purpose of this article is to show, first, how social networks serve the elimination of minorities in general, and second, to argue that the media logic of social networks must adapt to new interpretations of democracy that give more space to minorities and human rights. Finally, this article will show how the Clubhouse serves the new interpretations of democracy at least in a minimal way. To achieve the mentioned goals, in this article by a descriptive-analytical method, first, the relation between media logic and postmodern democracy will be inquired. The political economy popularity in social media and its conflict with democracy will be discussed. Finally, it will be explored how the Clubhouse provides a new horizon for the concepts embodied in radical democracy, a horizon that more effectively serves the rights of minorities and human rights in general.

Keywords: algorithmic tyranny, Clubhouse, minority rights, radical democracy, social media

Procedia PDF Downloads 122

Authors: Jonathan Turner, Julie Doyle, Laura O’Philbin, Dympna O’Sullivan

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People living with dementia should be at the centre of decision-making regarding goals for daily living. These goals include basic activities (dressing, hygiene, and mobility), advanced activities (finances, transportation, and shopping), and meaningful activities that promote well-being (pastimes and intellectual pursuits). However, there is limited involvement of people living with dementia in the design of technology to support their goals. A project is described that is co-designing intelligent computer-based support for, and with, people affected by dementia and their carers. The technology will support self-management, empower participation in shared decision-making with carers and help people living with dementia remain healthy and independent in their homes for longer. It includes information from the patient’s care plan, which documents medications, contacts, and the patient's wishes on end-of-life care. Importantly for this work, the plan can outline activities that should be maintained or worked towards, such as exercise or social contact. The authors discuss how to integrate care goal information from such a care plan with data collected from passive sensors in the patient’s home in order to deliver individualized planning and interventions for persons with dementia. A number of scientific challenges are addressed: First, to co-design with dementia patients and their carers computerized support for shared decision-making about their care while allowing the patient to share the care plan. Second, to develop a new and open monitoring framework with which to configure sensor technologies to collect data about whether goals and actions specified for a person in their care plan are being achieved. This is developed top-down by associating care quality types and metrics elicited from the co-design activities with types of data that can be collected within the home, from passive and active sensors, and from the patient’s feedback collected through a simple co-designed interface. These activities and data will be mapped to appropriate sensors and technological infrastructure with which to collect the data. Third, the application of machine learning models to analyze data collected via the sensing devices in order to investigate whether and to what extent activities outlined via the care plan are being achieved. The models will capture longitudinal data to track disease progression over time; as the disease progresses and captured data show that activities outlined in the care plan are not being achieved, the care plan may recommend alternative activities. Disease progression may also require care changes, and a data-driven approach can capture changes in a condition more quickly and allow care plans to evolve and be updated.

Keywords: care goals, decision-making, dementia, self-care, sensors

Procedia PDF Downloads 143

Authors: Zhijie Li, Wenhui Peng, Zelong Yuan, Jianchun Wang

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Turbulence is a complex phenomenon that plays a crucial role in various fields, such as engineering, atmospheric science, and fluid dynamics. Predicting and understanding its behavior over long time scales have been challenging tasks. Traditional methods, such as large-eddy simulation (LES), have provided valuable insights but are computationally expensive. In the past few years, machine learning methods have experienced rapid development, leading to significant improvements in computational speed. However, ensuring stable and accurate long-term predictions remains a challenging task for these methods. In this study, we introduce the implicit U-net enhanced Fourier neural operator (IU-FNO) as a solution for stable and efficient long-term predictions of the nonlinear dynamics in three-dimensional (3D) turbulence. The IU-FNO model combines implicit re-current Fourier layers to deepen the network and incorporates the U-Net architecture to accurately capture small-scale flow structures. We evaluate the performance of the IU-FNO model through extensive large-eddy simulations of three types of 3D turbulence: forced homogeneous isotropic turbulence (HIT), temporally evolving turbulent mixing layer, and decaying homogeneous isotropic turbulence. The results demonstrate that the IU-FNO model outperforms other FNO-based models, including vanilla FNO, implicit FNO (IFNO), and U-net enhanced FNO (U-FNO), as well as the dynamic Smagorinsky model (DSM), in predicting various turbulence statistics. Specifically, the IU-FNO model exhibits improved accuracy in predicting the velocity spectrum, probability density functions (PDFs) of vorticity and velocity increments, and instantaneous spatial structures of the flow field. Furthermore, the IU-FNO model addresses the stability issues encountered in long-term predictions, which were limitations of previous FNO models. In addition to its superior performance, the IU-FNO model offers faster computational speed compared to traditional large-eddy simulations using the DSM model. It also demonstrates generalization capabilities to higher Taylor-Reynolds numbers and unseen flow regimes, such as decaying turbulence. Overall, the IU-FNO model presents a promising approach for long-term dynamics prediction in 3D turbulence, providing improved accuracy, stability, and computational efficiency compared to existing methods.

Keywords: data-driven, Fourier neural operator, large eddy simulation, fluid dynamics

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Authors: Bruno Vicente, Sandra Rafael, Vera Rodrigues, Sandra Sorte, Sara Silva, Ana Isabel Miranda, Carlos Borrego

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Wind comfort for pedestrians is an important condition in urban areas. In Portugal, a country with 900 km of coastline, the wind direction are predominantly from Nor-Northwest with an average speed of 2.3 m·s -1 (at 2 m height). As a result, a set of city authorities have been requesting studies of pedestrian wind comfort for new urban areas/buildings, as well as to mitigate wind discomfort issues related to existing structures. This work covers the efficiency evaluation of a set of measures to reduce the wind speed in an outdoor auditorium (open space) located in a coastal Portuguese urban area. These measures include the construction of barriers, placed at upstream and downstream of the auditorium, and the planting of trees, placed upstream of the auditorium. The auditorium is constructed in the form of a porch, aligned with North direction, driving the wind flow within the auditorium, promoting channelling effects and increasing its speed, causing discomfort in the users of this structure. To perform the wind comfort assessment, two approaches were used: i) a set of experiments using the wind tunnel (physical approach), with a representative mock-up of the study area; ii) application of the CFD (Computational Fluid Dynamics) model VADIS (numerical approach). Both approaches were used to simulate the baseline scenario and the scenarios considering a set of measures. The physical approach was conducted through a quantitative method, using hot-wire anemometer, and through a qualitative analysis (visualizations), using the laser technology and a fog machine. Both numerical and physical approaches were performed for three different velocities (2, 4 and 6 m·s-1 ) and two different directions (NorNorthwest and South), corresponding to the prevailing wind speed and direction of the study area. The numerical results show an effective reduction (with a maximum value of 80%) of the wind speed inside the auditorium, through the application of the proposed measures. A wind speed reduction in a range of 20% to 40% was obtained around the audience area, for a wind direction from Nor-Northwest. For southern winds, in the audience zone, the wind speed was reduced from 60% to 80%. Despite of that, for southern winds, the design of the barriers generated additional hot spots (high wind speed), namely, in the entrance to the auditorium. Thus, a changing in the location of the entrance would minimize these effects. The results obtained in the wind tunnel compared well with the numerical data, also revealing the high efficiency of the purposed measures (for both wind directions).

Keywords: urban microclimate, pedestrian comfort, numerical modelling, wind tunnel experiments

Procedia PDF Downloads 201

Authors: Jayshree P. Zend, Ashatai B. Pawar

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The study was carried out in Parbhani district of Maharashtra state, India with the objectives to study environmental problems faced by flour mill workers, prevalence of work-related health hazards and the physiological cost of workers while performing work in flour mill in traditional method as well as improved method. The use of flour presser, dust controlling bag and noise and dust controlling mask developed by AICRP College of Home Science, VNMKV, Parbhani was considered as an improved method. This investigation consisted survey and experiment which was conducted in the respective locations of flour mills. Healthy, non-smoking 30 flour mill workers ranged between the age group of 20-50 yrs comprising 16 female and 14 male working at flour mill for 4-8 hrs/ day and 6 days/ week and had minimum five years experience of work in flour mill were selected for the study. Pulmonary function test of flour mill workers was carried out by trained technician at Dr. ShankarraoChavan Government Medical College, Nanded by using Electronic Spirometer. The data regarding heart rate (resting, working and recovery), energy expenditure, musculoskeletal problems and occupational health hazards and accidents were recorded by using pretested questionnaire. Scientific equipment used in the experiment were polar sport test heart rate monitor, Hygrometer, Goniometer, Dialed Thermometer, Sound Level Meter, Lux Meter, Ambient Air Sampler and Air Quality Monitor. The collected data were subjected to appropriate statistical analysis such as 't' test and correlation coefficient test. Results indicated that improved method i.e. use of noise and dust controlling mask, flour presser and dust controlling bag were effective in reducing physiological cost of work of flour mill workers. Lung function test of flour mill workers showed decreased values of all parameters, hence the results of present study support paying attention to use of personal protective noise and dust controlling mask by flour mill workers and also to the working conditions in flour mill especially ventilation and illumination level needs to be enhanced in flour mill. The study also emphasizes the need to develop some mechanism for lifting load of grains and unloading in the hopper. It is also suggested that the flour mill workers should use flour presser suitable to their height to avoid frequent bending and should use dust controlling bag to flour outlet of machine to reduce inhalable flour dust level in the flour mill.

Keywords: physiological cost, energy expenditure, musculoskeletal problems

Procedia PDF Downloads 373

Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Shengnan Chen, Shuhua Wang

Abstract:

Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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