Search results for: machine modelling

Authors: Elisa Santana-Monagas, Juan L. Núñez, Jaime León, Samuel Falcón, Celia Fernández, Rocío P. Solís

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Teaching practices have been widely studied in relation to students' outcomes, positioning themselves as one of their strongest catalysts and influencing students' emotional experiences. In the higher education context, teachers become even more crucial as many students ground their decisions on which courses to enroll in based on opinions and ratings of teachers from other students. Unfortunately, sometimes universities do not provide the personal, social, and academic stimulation students demand to be actively engaged. To evaluate their teachers, universities often rely on students' evaluations of teaching (SET) collected via Likert scale surveys. Despite its usefulness, such a method has been questioned in terms of validity and reliability. Alternatively, researchers can rely on qualitative answers to open-ended questions. However, the unstructured nature of the answers and a large amount of information obtained requires an overwhelming amount of work. The present work presents an alternative approach to analyse such data: sentiment analysis. To the best of our knowledge, no research before has included results from SA into an explanatory model to test how students' sentiments affect their emotional engagement in class. The sample of the present study included a total of 225 university students (Mean age = 26.16, SD = 7.4, 78.7 % women) from the Educational Sciences faculty of a public university in Spain. Data collection took place during the academic year 2021-2022. Students accessed an online questionnaire using a QR code. They were asked to answer the following open-ended question: "If you had to explain to a peer who doesn't know your teacher how he or she communicates in class, what would you tell them?". Sentiment analysis was performed using Microsoft's pre-trained model. The reliability of the measure was estimated between the tool and one of the researchers who coded all answers independently. The Cohen's kappa and the average pairwise percent agreement were estimated with ReCal2. Cohen's kappa was .68, and the agreement reached was 90.8%, both considered satisfactory. To test the hypothesis relations among SA and students' emotional engagement, a structural equation model (SEM) was estimated. Results demonstrated a good fit of the data: RMSEA = .04, SRMR = .03, TLI = .99, CFI = .99. Specifically, the results showed that student’s sentiment regarding their teachers’ teaching positively predicted their emotional engagement (β == .16 [.02, -.30]). In other words, when students' opinion toward their instructors' teaching practices is positive, it is more likely for students to engage emotionally in the subject. Altogether, the results show a promising future for sentiment analysis techniques in the field of education. They suggest the usefulness of this tool when evaluating relations among teaching practices and student outcomes.

Keywords: sentiment analysis, students' evaluation of teaching, structural-equation modelling, emotional engagement

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Authors: Mohammad M. Khan, Gajendra Dixit

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Partially lubricated sliding wear behaviour of a zinc-based alloy reinforced with 10wt% SiC particles has been studied as a function of applied load and solid lubricant particle size and has been compared with that of matrix alloy and conventionally used grey cast iron. The wear tests were conducted at the sliding velocities of 2.1m/sec in various partial lubricated conditions using pin on disc machine as per ASTM G-99-05. Base oil (SAE 20W-40) or mixture of the base oil with 5wt% graphite of particle sizes (7-10 µm) and (100 µm) were used for creating lubricated conditions. The matrix alloy revealed primary dendrites of a and eutectoid a + h and Î phases in the Inter dendritic regions. Similar microstructure has been depicted by the composite with an additional presence of the dispersoid SiC particles. In the case of cast iron, flakes of graphite were observed in the matrix; the latter comprised of (majority of) pearlite and (limited quantity of) ferrite. Results show a large improvement in wear resistance of the zinc-based alloy after reinforcement with SiC particles. The cast iron shows intermediate response between the matrix alloy and composite. The solid lubrication improved the wear resistance and friction behaviour of both the reinforced and base alloy. Moreover, minimum wear rate is obtained in oil+ 5wt % graphite (7-10 µm) lubricated environment for the matrix alloy and composite while for cast iron addition of solid lubricant increases the wear rate and minimum wear rate is obtained in case of oil lubricated environment. The cast iron experienced higher frictional heating than the matrix alloy and composite in all the cases especially at higher load condition. As far as friction coefficient is concerned, a mixed trend of behaviour was noted. The wear rate and frictional heating increased with load while friction coefficient was affected in an opposite manner. Test duration influenced the frictional heating and friction coefficient of the samples in a mixed manner.

Keywords: solid lubricant, sliding wear, grey cast iron, zinc based metal matrix composites

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Authors: Calorine Twebaze, Jesca Balinga

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Field X is located on the Eastern shores of L. Albert, Uganda, on the rift flank where the gross sedimentary fill is typically less than 2,000m. The field was discovered in 2006 and encountered about 20.4m of net pay across three (3) stratigraphic intervals within the discovery well. The field covers an area of 3 km2, with the structural configuration comprising a 3-way dip-closed hanging wall anticline that seals against the basement to the southeast along the bounding fault. Field X had been mapped on reprocessed 3D seismic data, which was originally acquired in 2007 and reprocessed in 2013. The seismic data quality is good across the field, and reprocessing work reduced the uncertainty in the location of the bounding fault and enhanced the lateral continuity of reservoir reflectors. The current study was a re-evaluation of Field X to refine fault interpretation and understand the structural uncertainties associated with the field. The seismic data, and three (3) wells datasets were used during the study. The evaluation followed standard workflows using Petrel software and structural attribute analysis. The process spanned from seismic- -well tie, structural interpretation, and structural uncertainty analysis. Analysis of three (3) well ties generated for the 3 wells provided a geophysical interpretation that was consistent with geological picks. The generated time-depth curves showed a general increase in velocity with burial depth. However, separation in curve trends observed below 1100m was mainly attributed to minimal lateral variation in velocity between the wells. In addition to Attribute analysis, three velocity modeling approaches were evaluated, including the Time-Depth Curve, Vo+ kZ, and Average Velocity Method. The generated models were calibrated at well locations using well tops to obtain the best velocity model for Field X. The Time-depth method resulted in more reliable depth surfaces with good structural coherence between the TWT and depth maps with minimal error at well locations of 2 to 5m. Both the NNE-SSW rift border fault and minor faults in the existing interpretation were reevaluated. However, the new interpretation delineated an E-W trending fault in the northern part of the field that had not been interpreted before. The fault was interpreted at all stratigraphic levels and thus propagates from the basement to the surface and is an active fault today. It was also noted that the entire field is less faulted with more faults in the deeper part of the field. The major structural uncertainties defined included 1) The time horizons due to reduced data quality, especially in the deeper parts of the structure, an error equal to one-third of the reflection time thickness was assumed, 2) Check shot analysis showed varying velocities within the wells thus varying depth values for each well, and 3) Very few average velocity points due to limited wells produced a pessimistic average Velocity model.

Keywords: 3D seismic data interpretation, structural uncertainties, attribute analysis, velocity modelling approaches

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Authors: M. Cabrera, W. Tizani, J. Ninic, F. Wang

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Open section beam to concrete-filled tubular column structures has been increasingly utilized in construction over the past few decades due to their enhanced structural performance, as well as economic and architectural advantages. However, the use of this configuration in construction is limited due to the difficulties in connecting the structural members as there is no access to the inner part of the tube to install standard bolts. Blind-bolted systems are a relatively new approach to overcome this limitation as they only require access to one side of the tubular section to tighten the bolt. The performance of these connections in concrete-filled steel tubular sections remains uncharacterized due to the complex interactions between concrete, bolt, and steel section. Over the last years, research in structural performance has moved to a more sophisticated and efficient approach consisting of machine learning algorithms to generate metamodels. This method reduces the need for developing complex, and computationally expensive finite element models, optimizing the search for desirable design variables. Metamodels generated by a data fusion approach use numerical and experimental results by combining multiple models to capture the dependency between the simulation design variables and connection performance, learning the relations between different design parameters and predicting a given output. Fully characterizing this connection will transform high-rise and multistorey construction by means of the introduction of design guidance for moment-resisting blind-bolted connections, which is currently unavailable. This paper presents a review of the steps taken to develop metamodels generated by means of artificial neural network algorithms which predict the connection stress and stiffness based on the design parameters when using Extended Hollo-Bolt blind bolts. It also provides consideration of the failure modes and mechanisms that contribute to the deformability as well as the feasibility of achieving blind-bolted rigid connections when using the blind fastener.

Keywords: blind-bolted connections, concrete-filled tubular structures, finite element analysis, metamodeling

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: Haniyeh Parhizkar

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When Sarris came up with the "auteur theory" in 1962, he emphasized that the utmost premise of auteur theory is the inner meanings and concepts of a film and that a film is purely an art form. Today's mainstream movies are conceptually closer to what the Frankfurt School scholars regarded as "reproduced" and "mass culture" years ago. Hollywood goes on to be a huge movie-making machine that leads the dominant paradigms of films throughout the world and cinema is far from art. Although there are still movies, directors, and audiences who favor art cinema over Hollywood and mainstream movies, it's an almost undeniable fact that, for the most part, people's perception of movies is widely influenced by their American depiction and Hollywood's legacy of mass culture. With the uprising of Hollywood studios as the forerunners of the movie industry and cinema being largely dependent on economics rather than artistic values, this distinctive role of cinema has diminished and is replaced with a global standard. The Blockbuster 2022 film, 'Everything, Everywhere, All at Once' is now the most-awarded movie of all time, winning seven Oscars at the 95th Academy Awards. Despite its main cast being Asian, the movie is produced by American incorporation and is heavily influenced by Hollywood's dominant themes of superheroes, fantasy, action, and adventure. The New Yorker film critic, Richard Brody, called the movie "a pitch for a Marvel" and critiqued the film for being "universalized" and "empty of history and culture". Other critics of Variety pinpointed the movie's similarities to Marvel, particularly in their storylines of multi-universe which manifest traces of American legacy. As argued by these critics, 'Everything, Everywhere, All at Once' might appear as a unique and authentic film at first glance, but it can be argued that it is yet another version of a Marvel movie. While the movie's universal acclaim was regarded as recognition and an acknowledgment of its Asian cast, the issue that arises here is when the Hollywood influences and American themes are so robust in the film, is the movie industry honoring another culture or is it yet another celebration of Hollywood's dominant paradigm. This essay will employ a critical approach to Hollywood's dominance and mass-produced culture, which has deprived authenticity of non-American movies and is constantly reproducing the same formula of success.

Keywords: hollywoodization, universalization, blockbuster, dominant paradigm, marvel, authenticity, diversity

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Authors: Nader Mohamed

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The Late Cretaceous deposits are well developed through-out Egypt. This is due to a ‎transgression phase associated with the subsidence caused by the neo-Tethyan rift event that ‎took place across the northern margin of Africa, resulting in a period of dominantly marine ‎deposits in the Gulf of Suez. The Late Cretaceous Nezzazat Group represents the Cenomanian, ‎Turonian and clastic sediments of the Lower Senonian. The Nezzazat Group has been divided ‎into four formations namely, from base to top, the Raha Formation, the Abu Qada Formation, ‎the Wata Formation and the Matulla Formation. The Cenomanian Raha and the Lower Senonian ‎Matulla formations are the most important clastic sequence in the Nezzazat Group because they ‎provide the highest net reservoir thickness and the highest net/gross ratio. This study emphasis ‎on Matulla formation located in the eastern part of the Gulf of Suez. The three stratigraphic ‎surface sections (Wadi Sudr, Wadi Matulla and Gabal Nezzazat) which represent the exposed ‎Coniacian-Santonian sediments in Sinai are used for correlating Matulla sediments of Ras ‎Budran field. Cutting description, petrographic examination, log behaviors, biostratigraphy with ‎outcrops are used to identify the reservoir characteristics, lithology, facies environment logs and ‎subdivide the Matulla formation into three units. The lower unit is believed to be the main ‎reservoir where it consists mainly of sands with shale and sandy carbonates, while the other ‎units are mainly carbonate with some streaks of shale and sand. Reservoir modeling is an ‎effective technique that assists in reservoir management as decisions concerning development ‎and depletion of hydrocarbon reserves, So It was essential to model the Matulla reservoir as ‎accurately as possible in order to better evaluate, calculate the reserves and to determine the ‎most effective way of recovering as much of the petroleum economically as possible. All ‎available data on Matulla formation are used to build the reservoir structure model, lithofacies, ‎porosity, permeability and water saturation models which are the main parameters that describe ‎the reservoirs and provide information on effective evaluation of the need to develop the oil ‎potentiality of the reservoir. This study has shown the effectiveness of; 1) the integration of ‎geological data to evaluate and subdivide Matulla formation into three units. 2) Lithology and ‎facies environment interpretation which helped in defining the nature of deposition of Matulla ‎formation. 3) The 3D reservoir modeling technology as a tool for adequate understanding of the ‎spatial distribution of property and in addition evaluating the unlocked new reservoir areas of ‎Matulla formation which have to be drilled to investigate and exploit the un-drained oil. 4) This ‎study led to adding a new room of production and additional reserves to Ras Budran field. ‎

Keywords: geology, oil and gas, geoscience, sequence stratigraphy

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Authors: Hugo Lefeuvre, Jerôme Donnard, Michael Descostes, Sophie Billon, Samuel Duval, Tugdual Oger, Herve Toubon, Paul Sardini

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Spectroscopic autoradiography is a method of interest for geological sample analysis. Indeed, researchers may face different issues such as radioelement identification and quantification in the field of environmental studies. Imaging gaseous ionization detectors find their place in geosciences for conducting specific measurements of radioactivity to improve the monitoring of natural processes using naturally-occurring radioactive tracers, but also for the nuclear industry linked to the mining sector. In geological samples, the location and identification of the radioactive-bearing minerals at the thin-section scale remains a major challenge as the detection limit of the usual elementary microprobe techniques is far higher than the concentration of most of the natural radioactive decay products. The spatial distribution of each decay product in the case of uranium in a geomaterial is interesting for relating radionuclides concentration to the mineralogy. The present study aims to provide spectroscopic autoradiography analysis method for measuring the initial energy of alpha particles with a parallel ionization multiplier gaseous detector. The analysis method has been developed thanks to Geant4 modelling of the detector. The track of alpha particles recorded in the gas detector allow the simultaneous measurement of the initial point of emission and the reconstruction of the initial particle energy by a selection based on the linear energy distribution. This spectroscopic autoradiography method was successfully used to reproduce the alpha spectra from a 238U decay chain on a geological sample at the thin-section scale. The characteristics of this measurement are an energy spectrum resolution of 17.2% (FWHM) at 4647 keV and a spatial resolution of at least 50 µm. Even if the efficiency of energy spectrum reconstruction is low (4.4%) compared to the efficiency of a simple autoradiograph (50%), this novel measurement approach offers the opportunity to select areas on an autoradiograph to perform an energy spectrum analysis within that area. This opens up possibilities for the detailed analysis of heterogeneous geological samples containing natural alpha emitters such as uranium-238 and radium-226. This measurement will allow the study of the spatial distribution of uranium and its descendants in geo-materials by coupling scanning electron microscope characterizations. The direct application of this dual modality (energy-position) of analysis will be the subject of future developments. The measurement of the radioactive equilibrium state of heterogeneous geological structures, and the quantitative mapping of 226Ra radioactivity are now being actively studied.

Keywords: alpha spectroscopy, digital autoradiography, mining activities, natural decay products

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Authors: Emmanuel C. Ngerem

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The ever-increasing depletion of the dominant global form of energy (fossil fuels) calls for the development of sustainable and green alternative energy sources such as bioethanol, biohydrogen, and biodiesel. The production of the major biofuels relies on biomass feedstocks that are mainly derived from edible food crops and some inedible plants. One suitable feedstock with great potential as raw material for biofuel production is microalgal biomass. Despite the tremendous attributes of microalgae as a source of biofuel, their cultivation requires huge volumes of freshwater, thus posing a serious threat to commercial-scale production and utilization of algal biomass. In this study, a multi-media wastewater mixture for microalgae growth was formulated and optimized. Moreover, the obtained microalgae biomass was pre-treated to reduce sugar recovery and was compared with previous studies on microalgae biomass pre-treatment. The formulated and optimized mixed wastewater media for biomass and lipid accumulation was established using the simplex lattice mixture design. Based on the superposition approach of the potential results, numerical optimization was conducted, followed by the analysis of biomass concentration and lipid accumulation. The coefficients of regression (R²) of 0.91 and 0.98 were obtained for biomass concentration and lipid accumulation models, respectively. The developed optimization model predicted optimal biomass concentration and lipid accumulation of 1.17 g/L and 0.39 g/g, respectively. It suggested 64.69% dairy wastewater (DWW) and 35.31% paper and pulp wastewater (PWW) mixture for biomass concentration, 34.21% DWW, and 65.79% PWW for lipid accumulation. Experimental validation generated 0.94 g/L and 0.39 g/g of biomass concentration and lipid accumulation, respectively. The obtained microalgae biomass was pre-treated, enzymatically hydrolysed, and subsequently assessed for reducing sugars. The optimization of microwave pre-treatment of Chlorococcum sp. was achieved using response surface methodology (RSM). Microwave power (100 – 700 W), pre-treatment time (1 – 7 min), and acid-liquid ratio (1 – 5%) were selected as independent variables for RSM optimization. The optimum conditions were achieved at microwave power, pre-treatment time, and acid-liquid ratio of 700 W, 7 min, and 32.33:1, respectively. These conditions provided the highest amount of reducing sugars at 10.73 g/L. Process optimization predicted reducing sugar yields of 11.14 g/L on microwave-assisted pre-treatment of 2.52% HCl for 4.06 min at 700 watts. Experimental validation yielded reducing sugars of 15.67 g/L. These findings demonstrate that dairy wastewater and paper and pulp wastewater that could pose a serious environmental nuisance. They could be blended to form a suitable microalgae growth media, consolidating the potency of microalgae as a viable feedstock for fermentable sugars. Also, the outcome of this study supports the microalgal wastewater biorefinery concept, where wastewater remediation is coupled with bioenergy production.

Keywords: wastewater cultivation, mixture design, lipid, biomass, nutrient removal, microwave, Chlorococcum, raceway pond, fermentable sugar, modelling, optimization

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Authors: Nahid Parween Anwar, Sadia Muzaffar Bhutta, Takbir Ali

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Mentoring is a structured activity which is designed to facilitate engagement between mentor and mentee to enhance mentee’s professional capability as an effective teacher. Both mentor and mentee are important elements of the ‘mentoring equation’ and play important roles in nourishing this dynamic, collaborative and reciprocal relationship. Cluster-Based Mentoring Programme (CBMP) provides an indigenous example of a project which focused on development of primary school teachers in selected clusters with a particular focus on their classroom practice. A study was designed to examine the efficacy of CBMP as part of Strengthening Teacher Education in Pakistan (STEP) project. This paper presents results of one of the components of this study. As part of the larger study, a cross-sectional survey was employed to explore nature and patterns of mentoring process from mentees’ perspectives in the selected districts of Sindh and Balochistan. This paper focuses on the results of the study related to the question: What are mentees’ perceptions of their mentors’ support for enhancing their classroom practice during mentoring process? Data were collected from mentees (n=1148) using a 5-point scale -‘Mentoring for Effective Primary Teaching’ (MEPT). MEPT focuses on seven factors of mentoring: personal attributes, pedagogical knowledge, modelling, feedback, system requirement, development and use of material, and gender equality. Data were analysed using SPSS 20. Mentees perceptions of mentoring practice of their mentors were summarized using mean and standard deviation. Results showed that mean scale scores on mentees’ perceptions of their mentors’ practices fell between 3.58 (system requirement) and 4.55 (personal attributes). Mentees’ perceives personal attribute of the mentor as the most significant factor (M=4.55) towards streamlining mentoring process by building good relationship between mentor and mentees. Furthermore, mentees have shared positive views about their mentors efforts towards promoting gender impartiality (M=4.54) during workshop and follow up visit. Contrary to this, mentees felt that more could have been done by their mentors in sharing knowledge about system requirement (e.g. school policies, national curriculum). Furthermore, some of the aspects in high scoring factors were highlighted by the mentees as areas for further improvement (e.g. assistance in timetabling, written feedback, encouragement to develop learning corners). Mentees’ perceptions of their mentors’ practices may assist in determining mentoring needs. The results may prove useful for the professional development programme for the mentors and mentees for specific mentoring programme in order to enhance practices in primary classrooms in Pakistan. Results would contribute into the body of much-needed knowledge from developing context.

Keywords: cluster-based mentoring programme, mentoring for effective primary teaching (MEPT), professional development, survey

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Authors: Ayesha Sanjana Kawser Parsha

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S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

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Authors: Bahareh Sarmadi, Paul A. Kilmartin, Leandro D. Araújo, Brandt P. Bastow

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Aim: Sauvignon blanc is the most substantial variety cultivated in almost 62% of all producing vineyards of New Zealand. The popularity of New Zealand Sauvignon blanc is due to its unique taste. It is the most famous wine characterized by its aroma profile derived from mercaptans. 3-mercaptohexan-1-ol (3MH) and 3-mercaptohexyl acetate (3MHA) are two of the most important volatile mercaptans found in Sauvignon blanc wines. “Viticultural” and “Enological” factors such as machine-harvesting, the most common harvesting practice used in New Zealand, can be among the reasons for this distinct flavor. Elemental sulfur is commonly sprayed in the fields to protect berries against powdery mildew. Although it is not the only source of sulfur, this practice creates a source of elemental sulfur that can be transferred into the must and eventually into wines. Despite the clear effects of residual elemental sulfur present in the must on the quality and aroma of the final wines, its measurement before harvest or fermentation is not a regular practice in the wineries. This can be due to the lack of accessible and applicable methods for the equipment at most commercial wineries. This study aims to establish a relationship between the number and frequency of elemental sulfur applications and the concentration of polyfunctional mercaptans in the final wines. Methods: An apparatus was designed to reduce elemental sulfur to sulfide, then an ion-selective electrode to measure sulfide concentration. During harvest 2022, we explored a wider range of residual elemental sulfur levels than what typically applies in the vineyards. This has been done through later manual elemental sulfur applications in the vineyard. Additional sulfur applications were made 20, 10 and 5 days prior to harvesting the treated grapes, covering long and short pre-harvest intervals (PHI). The grapes were processed into juice and fermented into wine; then, they were analyzed to find the correlation between polyfunctional mercaptans concentrations in the wines and residual elemental sulfur in the juice samples. Results: The research showed that higher 3MH/3MHA was formed when elemental sulfur was applied more frequent in the vineyards and supported the proposed pathway in which elemental sulfur is a source of 3MH formation in wines.

Keywords: sauvignon blanc, elemental sulfur, polyfunctional mercaptans, varietal thiols

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Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: Ali Al-Shehri, Abdulaziz Al-Qasim, Abdulkarim Sofi, Ali Yousef

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The oil and gas industry has played a major role in improving the prosperity of mankind and driving the world economy. According to the International Energy Agency (IEA) and Integrated Environmental Assessment (EIA) estimates, the world will continue to rely heavily on hydrocarbons for decades to come. This growing energy demand mandates taking sustainability measures to prolong the availability of reliable and affordable energy sources, and ensure lowering its environmental impact. Unlike any other industry, the oil and gas upstream operations are energy-intensive and scattered over large zonal areas. These challenging conditions require unique sustainability solutions. In recent years there has been a concerted effort by the oil and gas industry to develop and deploy innovative technologies to: maximize efficiency, reduce carbon footprint, reduce CO2 emissions, and optimize resources and material consumption. In the past, the main driver for research and development (R&D) in the exploration and production sector was primarily driven by maximizing profit through higher hydrocarbon recovery and new discoveries. Environmental-friendly and sustainable technologies are increasingly being deployed to balance sustainability and profitability. Analyzing technology and its sustainability impact is increasingly being used in corporate decision-making for improved portfolio management and allocating valuable resources toward technology R&D.This paper articulates and discusses a novel workflow to identify strategic sustainable technologies for improved portfolio management by addressing existing and future upstream challenges. It uses a systematic approach that relies on sustainability key performance indicators (KPI’s) including energy efficiency quotient, carbon footprint, and CO2 emissions. The paper provides examples of various technologies including CCS, reducing water cuts, automation, using renewables, energy efficiency, etc. The use of 4IR technologies such as Artificial Intelligence, Machine Learning, and Data Analytics are also discussed. Overlapping technologies, areas of collaboration and synergistic relationships are identified. The unique sustainability analyses provide improved decision-making on technology portfolio management.

Keywords: sustainability, oil& gas, technology portfolio, key performance indicator

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Authors: Harvey Sanchez-Restrepo, Jorge Louca

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Equity and education are major focus of government policies around the world due to its relevance for addressing the sustainable development goals launched by Unesco. In this research, we developed a primary analysis of a data set of more than one hundred educational and non-educational factors associated with learning, coming from a census-based large-scale assessment carried on in Ecuador for 1.038.328 students, their families, teachers, and school directors, throughout 2014-2018. Each participating student was assessed by a standardized computer-based test. Learning outcomes were calibrated through item response theory with two-parameters logistic model for getting raw scores that were re-scaled and synthetized by a learning index (LI). Our objective was to develop a network for modelling educational deprivation and analyze the structure of inequality gaps, as well as their relationship with socioeconomic status, school financing, and student's ethnicity. Results from the model show that 348 270 students did not develop the minimum skills (prevalence rate=0.215) and that Afro-Ecuadorian, Montuvios and Indigenous students exhibited the highest prevalence with 0.312, 0.278 and 0.226, respectively. Regarding the socioeconomic status of students (SES), modularity class shows clearly that the system is out of equilibrium: the first decile (the poorest) exhibits a prevalence rate of 0.386 while rate for decile ten (the richest) is 0.080, showing an intense negative relationship between learning and SES given by R= –0.58 (p < 0.001). Another interesting and unexpected result is the average-weighted degree (426.9) for both private and public schools attending Afro-Ecuadorian students, groups that got the highest PageRank (0.426) and pointing out that they suffer the highest educational deprivation due to discrimination, even belonging to the richest decile. The model also found the factors which explain deprivation through the highest PageRank and the greatest degree of connectivity for the first decile, they are: financial bonus for attending school, computer access, internet access, number of children, living with at least one parent, books access, read books, phone access, time for homework, teachers arriving late, paid work, positive expectations about schooling, and mother education. These results provide very accurate and clear knowledge about the variables affecting poorest students and the inequalities that it produces, from which it might be defined needs profiles, as well as actions on the factors in which it is possible to influence. Finally, these results confirm that network analysis is fundamental for educational policy, especially linking reliable microdata with social macro-parameters because it allows us to infer how gaps in educational achievements are driven by students’ context at the time of assigning resources.

Keywords: complex network, educational deprivation, evidence-based policy, large-scale assessments, policy informatics

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Authors: M. Hossain, A. Abdkader, C. Cherif, A. Berger, M. Sparing, R. Hühne, L. Schultz, K. Nielsch

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Due to the best quality of yarn and the flexibility of the machine, the ring spinning process is the most widely used spinning method for short staple yarn production. However, the productivity of these machines is still much lower in comparison to other spinning systems such as rotor or air-jet spinning process. The main reason for this limitation lies on the twisting mechanism of the ring spinning process. In the ring/traveler twisting system, each rotation of the traveler along with the ring inserts twist in the yarn. The rotation of the traveler at higher speed includes strong frictional forces, which in turn generates heat. Different ring/traveler systems concerning with its geometries, material combinations and coatings have already been implemented to solve the frictional problem. However, such developments can neither completely solve the frictional problem nor increase the productivity. The friction free superconducting magnetic bearing (SMB) system can be a right alternative replacing the existing ring/traveler system. The unique concept of SMB bearings is that they possess a self-stabilizing behavior, i.e. they remain fully passive without any necessity for expensive position sensing and control. Within the framework of a research project funded by German research foundation (DFG), suitable concepts of the SMB-system have been designed, developed, and integrated as a twisting device of ring spinning replacing the existing ring/traveler system. With the help of the developed mathematical model and experimental investigation, the physical limitations of this innovative twisting device in the spinning process have been determined. The interaction among the parameters of the spinning process and the superconducting twisting element has been further evaluated, which derives the concrete information regarding the new spinning process. Moreover, the influence of the implemented SMB twisting system on the yarn quality has been analyzed with respect to different process parameters. The presented work reveals the enormous potential of the innovative twisting mechanism, so that the productivity of the ring spinning process especially in case of thermoplastic materials can be at least doubled for the first time in a hundred years. The SMB ring spinning tester has also been presented in the international fair “International Textile Machinery Association (ITMA) 2015”.

Keywords: ring spinning, superconducting magnetic bearing, yarn properties, productivity

Procedia PDF Downloads 237

Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

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Authors: Mardin Abdalqadir, Paul Adzakro, Tannaz Pak, Sina Rezaei Gomari

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Climate change and global warming recently became significant concerns due to the massive emissions of greenhouse gases into the atmosphere, predominantly CO2 gases. Therefore, it is necessary to find sustainable and inexpensive methods to capture the greenhouse gasses and protect the environment for live species. The application of naturally available and cheap adsorbents of carbon such as clay minerals became a great interest. However, the minerals prone to low storage capacity despite their high affinity to adsorb carbon. This paper aims to explore ways to improve the pore volume and surface area of two selected clay minerals, ‘montmorillonite and kaolinite’ by acid treatment to overcome their low storage capacity. Montmorillonite and kaolinite samples were treated with different sulfuric acid concentrations (0.5, 1.2 and 2.5 M) at 40 °C for 8 hours to achieve the above aim. The grain size distribution and morphology of clay minerals before and after acid treatment were explored with Scanning Electron Microscope to evaluate surface area improvement. The ImageJ software was used to find the porosity and pore volume of treated and untreated clay samples. The structure of the clay minerals was also analyzed using an X-ray Diffraction machine. The results showed that the pore volume and surface area were increased substantially through acid treatment, which speeded up the rate of carbon dioxide adsorption. XRD pattern of kaolinite did not change after sulfuric acid treatment, which indicates that acid treatment would not affect the structure of kaolinite. It was also discovered that kaolinite had a higher pore volume and porosity than montmorillonite before and after acid treatment. For example, the pore volume of untreated kaolinite was equal to 30.498 um3 with a porosity of 23.49%. Raising the concentration of acid from 0.5 M to 2.5 M in 8 hours’ time reaction led to increased pore volume from 30.498 um3 to 34.73 um3. The pore volume of raw montmorillonite was equal to 15.610 um3 with a porosity of 12.7%. When the acid concentration was raised from 0.5 M to 2.5 M for the same reaction time, pore volume also increased from 15.610 um3 to 20.538 um3. However, montmorillonite had a higher specific surface area than kaolinite. This study concludes that clay minerals are inexpensive and available material sources to model the realistic conditions and apply the results of carbon capture to prevent global warming, which is one of the most critical and urgent problems in the world.

Keywords: acid treatment, kaolinite, montmorillonite, pore volume, porosity, surface area

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Authors: Kaviyarasan D., Satish Kumar S. R.

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India being vast in area and population with great scope of international business, roadways and railways network connection within the country is expected to have a big growth. There are numerous rail-cum-road bridges constructed across many major rivers in India and few are getting very old. So there is more possibility of repairing or coming up with such new bridges in India. Analysis and design of such bridges are practiced through conventional procedure and end up with heavy and uneconomical sections. Such heavy class steel bridges when subjected to high seismic shaking has more chance to fail by stability because the members are too much rigid and stocky rather than being flexible to dissipate the energy. This work is the collective study of the researches done in the truss bridge and steel concrete composite truss bridges presenting the method of analysis, tools for numerical and analytical modeling which evaluates its seismic behaviour and collapse mechanisms. To ascertain the inelastic and nonlinear behaviour of the structure, generally at research level static pushover analysis is adopted. Though the static pushover analysis is now extensively used for the framed steel and concrete buildings to study its lateral action behaviour, those findings by pushover analysis done for the buildings cannot directly be used for the bridges as such, because the bridges have completely a different performance requirement, behaviour and typology as compared to that of the buildings. Long span steel bridges are mostly the truss bridges. Truss bridges being formed by many members and connections, the failure of the system does not happen suddenly with single event or failure of one member. Failure usually initiates from one member and progresses gradually to the next member and so on when subjected to further loading. This kind of progressive collapse of the truss bridge structure is dependent on many factors, in which the live load distribution and span to length ratio are most significant. The ultimate collapse is anyhow by the buckling of the compression members only. For regular bridges, single step pushover analysis gives results closer to that of the non-linear dynamic analysis. But for a complicated bridge like heavy class steel bridge or the skewed bridges or complicated dynamic behaviour bridges, nonlinear analysis capturing the progressive yielding and collapse pattern is mandatory. With the knowledge of the postelastic behaviour of the bridge and advancements in the computational facility, the current level of analysis and design of bridges has moved to state of ascertaining the performance levels of the bridges based on the damage caused by seismic shaking. This is because the buildings performance levels deals much with the life safety and collapse prevention levels, whereas the bridges mostly deal with the extent damages and how quick it can be repaired with or without disturbing the traffic after a strong earthquake event. The paper would compile the wide spectrum of modeling to analysis of the steel concrete composite truss bridges in general.

Keywords: bridge engineering, performance based design of steel truss bridge, seismic design of composite bridge, steel-concrete composite bridge

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Authors: Seham A. S. Mohamed, Sarah R. Baker, Christopher Deery, Mario V. Vettore

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Background: The link between oral health conditions and school performance and attendance remain unclear among Middle Eastern children. The association has been studied extensively in the Western region; however, several concerns have been raised regarding the reliability and validity of measures, low quality of studies, inadequate inclusion of potential confounders, and the lack of a conceptual framework. These limitations have meant that, to date, there has been no detailed understanding of the association or of the key social, clinical, behavioural and parental factors which may impact the association. Aim: To examine the association between oral health conditions and children’s school performance and attendance at Grade 2 in Muharraq city in the Kingdom of Bahrain using Heilmann et al.’s (2015) life course framework for oral health. Objectives: To (1) describe the prevalence of oral health conditions among 7-8 years old schoolchildren in the city of Muharraq; (2) analyse the social, biological, behavioural, and parental pathways that link early and current life exposures with children’s current oral health status; (3) examine the association between oral health conditions and school performance and attendance among schoolchildren; (4) explore the early and current life course social, biological, behavioural and parental factors associated with children’s school outcomes. Design: A time-ordered-cross-sectional study was conducted with 466 schoolchildren aged 7-8 years and their parents from Muharraq city in KoB. Data were collected through parents’ self-administered questionnaires, children’s face-face interviews, and dental clinical examinations. Outcome variables, including school performance and school attendance data, were obtained from the parents and school records. The data were analysed using structural equation modelling (SEM). Results: Dental caries, the consequence of dental caries (PUFA/pufa), and enamel developmental defects (EDD) prevalence were 93.4%, 25.7%, and 17.2%, respectively. The findings from the SEM showed that children born in families with high SES were less likely to suffer from dentine dental caries (β= -0.248) and more likely to earn high school performance (β= 0.136) at 7-8 years of age in Muharraq. From the current life course of children, the dental plaque was associated significantly and directly with enamel caries (β= 0.094), dentine caries (β= 0.364), treated teeth (filled or extracted because of dental caries) (β= 0.121), and indirectly associated with dental pain (β= 0.057). Further, dentine dental caries was associated significantly and directly with low school performance (β= -0.155). At the same time, the dental plaque was indirectly associated with low school performance via dental caries (β = −0.044). Conversely, treated teeth were associated directly with high school performance (β= 0.100). Notably, none of the OHCs, biological, SES, behavioural, or parental conditions was related to school attendance in children. Conclusion: The life course approach was adequate to examine the role of OHCs on children’s school performance and attendance. Birth and current (7-8-year-olds) social factors were significant predictors of poor OH and poor school performance.

Keywords: dental caries, life course, Bahrain, school outcomes

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Authors: Ahmad Haidar

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This research paper investigates the concept of modern management, concentrating on the development of managerial practices and the adoption of innovative strategies in response to the fast-changing business landscape caused by Artificial Intelligence (AI). The study begins by examining the historical context of management theories, tracing the progression from classical to contemporary models, and identifying key drivers of change. Through a comprehensive review of existing literature and case studies, this paper provides valuable insights into the principles and practices of modern management, offering a roadmap for organizations aiming to navigate the complexities of the contemporary business world. The paper examines the growing role of digital technology in modern management, focusing on incorporating AI, machine learning, and data analytics to streamline operations and facilitate informed decision-making. Moreover, the research highlights the emergence of new principles, such as adaptability, flexibility, public participation, trust, transparency, and digital mindset, as crucial components of modern management. Also, the role of business leaders is investigated by studying contemporary leadership styles, such as transformational, situational, and servant leadership, emphasizing the significance of emotional intelligence, empathy, and collaboration in fostering a healthy organizational culture. Furthermore, the research delves into the crucial role of environmental sustainability, corporate social responsibility (CSR), and corporate digital responsibility (CDR). Organizations strive to balance economic growth with ethical considerations and long-term viability. The primary research question for this study is: "What are the key principles, practices, and innovations that define modern management, and how can organizations effectively implement these strategies to thrive in the rapidly changing business landscape?." The research contributes to a comprehensive understanding of modern management by examining its historical context, the impact of digital technologies, the importance of contemporary leadership styles, and the role of CSR and CDR in today's business landscape.

Keywords: modern management, digital technology, leadership styles, adaptability, innovation, corporate social responsibility, organizational success, corporate digital responsibility

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Authors: Al. I. Diwedar, Moheb Iskander, Mohamed Yossef, Ahmed ElKut, Noha Fouad, Radwa Fathy, Mustafa M. Almaghraby, Amira Samir, Ahmed Romya, Nourhan Hassan, Asmaa Abo Zed, Bas Reijmerink, Julien Groenenboom

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Coastal problems are stressing the coastal environment due to its complexity. The dynamic interaction between the sea and the land results in serious problems that threaten coastal areas worldwide, in addition to human interventions and activities. This makes the coastal environment highly vulnerable to natural processes like flooding, erosion, and the impact of human activities as pollution. Protecting and preserving this vulnerable coastal zone with its valuable ecosystems calls for addressing the coastal problems. This, in the end, will support the sustainability of the coastal communities and maintain the current and future generations. Consequently applying suitable management strategies and sustainable development that consider the unique characteristics of the coastal system is a must. The coastal management philosophy aims to solve the conflicts of interest between human development activities and this dynamic nature. Modeling emerges as a successful tool that provides support to decision-makers, engineers, and researchers for better management practices. Modeling tools proved that it is accurate and reliable in prediction. With its capability to integrate data from various sources such as bathymetric surveys, satellite images, and meteorological data, it offers the possibility for engineers and scientists to understand this complex dynamic system and get in-depth into the interaction between both the natural and human-induced factors. This enables decision-makers to make informed choices and develop effective strategies for sustainable development and risk mitigation of the coastal zone. The application of modeling tools supports the evaluation of various scenarios by affording the possibility to simulate and forecast different coastal processes from the hydrodynamic and wave actions and the resulting flooding and erosion. The state-of-the-art application of modeling tools in coastal management allows for better understanding and predicting coastal processes, optimizing infrastructure planning and design, supporting ecosystem-based approaches, assessing climate change impacts, managing hazards, and finally facilitating stakeholder engagement. This paper emphasizes the role of hydraulic models in enhancing the management of coastal problems by discussing the diverse applications of modeling in coastal management. It highlights the modelling role in understanding complex coastal processes, and predicting outcomes. The importance of informing decision-makers with modeling results which gives technical and scientific support to achieve sustainable coastal development and protection.

Keywords: coastal problems, coastal management, hydraulic model, numerical model, physical model

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Authors: M. Invigorito, G. Elia, M. Panelli

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Hybrid propulsion combines beneficial properties of both solid and liquid rockets, such as multiple restarts, throttability as well as simplicity and reduced costs. A nitrous oxide (N2O)/paraffin-based hybrid rocket engine demonstrator is currently under development at the Italian Aerospace Research Center (CIRA) within the national research program HYPROB, funded by the Italian Ministry of Research. Nitrous oxide belongs to the class of self-pressurizing propellants that exhibit a high vapor pressure at standard ambient temperature. This peculiar feature makes those fluids very attractive for space rocket applications because it avoids the use of complex pressurization systems, leading to great benefits in terms of weight savings and reliability. To avoid feed-system-coupled instabilities, the phase change is required to occur through the injectors. In this regard, the oxidizer is stored in liquid condition while target chamber pressures are designed to lie below vapor pressure. The consequent cavitation and flash vaporization constitute a remarkably complex phenomenology that arises great modelling challenges. Thus, it is clear that the design of the injection system is fundamental for the full exploitation of hybrid rocket engine throttability. The Analytical Hierarchy Process has been used to select the injection architecture as best compromise among different design criteria such as functionality, technology innovation and cost. The impossibility to use engineering simplified relations for the dimensioning of the injectors led to the needs of applying a numerical approach based on OpenFOAM®. The numerical tool has been validated with selected experimental data from literature. Quantitative, as well as qualitative comparisons are performed in terms of mass flow rate and pressure drop across the injector for several operating conditions. The results show satisfactory agreement with the experimental data. Modeling assumptions, together with their impact on numerical predictions are discussed in the paper. Once assessed the reliability of the numerical tool, the injection plate has been designed and sized to guarantee the required amount of oxidizer in the combustion chamber and therefore to assure high combustion efficiency. To this purpose, the plate has been designed with multiple injectors whose number and diameter have been selected in order to reach the requested mass flow rate for the two operating conditions of maximum and minimum thrust. The overall design has been finally verified through three-dimensional computations in cavitating non-reacting conditions and it has been verified that the proposed design solution is able to guarantee the requested values of mass flow rates.

Keywords: hybrid rocket, injection system design, OpenFOAM®, cavitation

Procedia PDF Downloads 216

Authors: Sarayu Vanga, Jorge Galeano-Cabral, Kaya Wei

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Cerebral hypoxia refers to a condition in which there is a decrease in oxygen supply to the brain. Patients suffering from this condition experience a decrease in their body temperature. While there isn't any cure to treat cerebral hypoxia as of date, certain procedures are utilized to help aid in the treatment of the condition. Regulating the body temperature is an example of one of those procedures. Hypoxia is well known to reduce the body temperature of mammals, although the neural origins of this response remain uncertain. In order to speed recovery from this condition, it is necessary to maintain a stable body temperature. In this study, we present an approach to regulating body temperature for patients who suffer from cerebral hypoxia or other similar conditions. After a thorough literature study, we propose the use of thermoelectric blankets, which are temperature-controlled thermal blankets based on thermoelectric devices. These blankets are capable of heating up and cooling down the patient to stabilize body temperature. This feature is possible through the reversible effect that thermoelectric devices offer while behaving as a thermal sensor, and it is an effective way to stabilize temperature. Thermoelectricity is the direct conversion of thermal to electrical energy and vice versa. This effect is now known as the Seebeck effect, and it is characterized by the Seebeck coefficient. In such a configuration, the device has cooling and heating sides with temperatures that can be interchanged by simply switching the direction of the current input in the system. This design integrates various aspects, including a humidifier, ventilation machine, IV-administered medication, air conditioning, circulation device, and a body temperature regulation system. The proposed design includes thermocouples that will trigger the blanket to increase or decrease a set temperature through a medical temperature sensor. Additionally, the proposed design allows an efficient way to control fluctuations in body temperature while being cost-friendly, with an expected cost of 150 dollars. We are currently working on developing a prototype of the design to collect thermal and electrical data under different conditions and also intend to perform an optimization analysis to improve the design even further. While this proposal was developed for treating cerebral hypoxia, it can also aid in the treatment of other related conditions, as fluctuations in body temperature appear to be a common symptom that patients have for many illnesses.

Keywords: body temperature regulation, cerebral hypoxia, thermoelectric, blanket design

Procedia PDF Downloads 156

Authors: Lina E. Homman, Alexis C. Edwards, Seung Bin Cho, Danielle M. Dick, Kenneth S. Kendler

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Research supports an association between alcohol problems and internalising symptoms, but the understanding of how the two phenotypes relate to each other is poor. It has been hypothesized that the relationship between the phenotypes is causal; however investigations in regards to direction are inconsistent. Clarity of the relationship between the two phenotypes may be provided by investigating the phenotypes developmental inter-relationships longitudinally. The objective of the study was to investigate a) changes in alcohol problems and internalising symptoms in college students across time and b) the direction of effect of growth between alcohol problems and internalising symptoms from late adolescent to emerging adulthood c) possible gender differences. The present study adds to the knowledge of comorbidity of alcohol problems and internalising symptoms by examining a longitudinal sample of college students and by examining the simultaneous development of the symptoms. A sample of college students is of particular interest as symptoms of both phenotypes often have their onset around this age. A longitudinal sample of college students from a large, urban, public university in the United States was used. Data was collected over a time period of 2 years at 3 time points. Latent growth models were applied to examine growth trajectories. Parallel process growth models were used to assess whether initial level and rate of change of one symptom affected the initial level and rate of change of the second symptom. Possible effects of gender and ethnicity were investigated. Alcohol problems significantly increased over time, whereas internalizing symptoms remained relatively stable. The two phenotypes were significantly correlated in each wave, correlations were stronger among males. Initial level of alcohol problems was significantly positively correlated with initial level of internalising symptoms. Rate of change of alcohol problems positively predicted rate of change of internalising symptoms for females but not for males. Rate of change of internalising symptoms did not predict rate of change of alcohol problems for either gender. Participants of Black and Asian ethnicities indicated significantly lower levels of alcohol problems and a lower increase of internalising symptoms across time, compared to White participants. Participants of Black ethnicity also reported significantly lower levels of internalising symptoms compared to White participants. The present findings provide additional support for a positive relationship between alcohol problems and internalising symptoms in youth. Our findings indicated that both internalising symptoms and alcohol problems increased throughout the sample and that the phenotypes were correlated. The findings mainly implied a bi-directional relationship between the phenotypes in terms of significant associations between initial levels as well as rate of change. No direction of causality was indicated in males but significant results were found in females where alcohol problems acted as the main driver for the comorbidity of alcohol problems and internalising symptoms; alcohol may have more detrimental effects in females than in males. Importantly, our study examined a population-based longitudinal sample of college students, revealing that the observed relationships are not limited to individuals with clinically diagnosed mental health or substance use problems.

Keywords: alcohol, comorbidity, internalising symptoms, longitudinal modelling

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Authors: Aleksandra Zieminska-Stolarska, Ireneusz Zbicinski

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Introduction: The objective of the present work was to develop and validate a 3D CFD numerical model for simulating flow through 17 kilometers long dam reservoir of a complex bathymetry. In contrast to flowing waters, dam reservoirs were not emphasized in the early years of water quality modeling, as this issue has never been the major focus of urban development. Starting in the 1970s, however, it was recognized that natural and man-made lakes are equal, if not more important than estuaries and rivers from a recreational standpoint. The Sulejow Reservoir (Central Poland) was selected as the study area as representative of many lowland dam reservoirs and due availability of a large database of the ecological, hydrological and morphological parameters of the lake. Method: 3D, 2-phase and 1-phase CFD models were analysed to determine hydrodynamics in the Sulejow Reservoir. Development of 3D, 2-phase CFD model of flow requires a construction of mesh with millions of elements and overcome serious convergence problems. As 1-phase CFD model of flow in relation to 2-phase CFD model excludes from the simulations the dynamics of waves only, which should not change significantly water flow pattern for the case of lowland, dam reservoirs. In 1-phase CFD model, the phases (water-air) are separated by a plate which allows calculations of one phase (water) flow only. As the wind affects velocity of flow, to take into account the effect of the wind on hydrodynamics in 1-phase CFD model, the plate must move with speed and direction equal to the speed and direction of the upper water layer. To determine the velocity at which the plate will move on the water surface and interacts with the underlying layers of water and apply this value in 1-phase CFD model, the 2D, 2-phase model was elaborated. Result: Model was verified on the basis of the extensive flow measurements (StreamPro ADCP, USA). Excellent agreement (an average error less than 10%) between computed and measured velocity profiles was found. As a result of work, the following main conclusions can be presented: •The results indicate that the flow field in the Sulejow Reservoir is transient in nature, with swirl flows in the lower part of the lake. Recirculating zones, with the size of even half kilometer, may increase water retention time in this region •The results of simulations confirm the pronounced effect of the wind on the development of the water circulation zones in the reservoir which might affect the accumulation of nutrients in the epilimnion layer and result e.g. in the algae bloom. Conclusion: The resulting model is accurate and the methodology develop in the frame of this work can be applied to all types of storage reservoir configurations, characteristics, and hydrodynamics conditions. Large recirculating zones in the lake which increase water retention time and might affect the accumulation of nutrients were detected. Accurate CFD model of hydrodynamics in large water body could help in the development of forecast of water quality, especially in terms of eutrophication and water management of the big water bodies.

Keywords: CFD, mathematical modelling, dam reservoirs, hydrodynamics

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Authors: Mohd Javaid, Shahbaz Khan, Abid Haleem

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Purpose: Large numbers of 3D printing technologies are now available for sophisticated applications in different fields. Additive manufacturing has established its dominance in design, development, and customisation of the product. In the era of developing technologies, there is a need to identify the appropriate technology for different application. In order to fulfil this need, two widely used printing technologies such as Fused Deposition Modeling (FDM), and Binding-Jet 3D Printing are compared for effective utilisation in the current scenario for different applications. Methodology: Systematic literature review conducted for both technologies with applications and associated factors enabling for the same. Appropriate MCDM tool is used to compare critical factors for both the technologies. Findings: Both technologies have their potential and capabilities to provide better direction to the industry. Additionally, this paper is helpful to develop a decision support system for the proper selection of technologies according to their continuum of applications and associated research and development capability. The vital issue is raw materials, and research-based material development is key to the sustainability of the developed technologies. FDM is a low-cost technology which provides high strength product as compared to binding jet technology. Researcher and companies can take benefits of this study to achieve the required applications in lesser resources. Limitations: Study has undertaken the comparison with the opinion of experts, which may not always be free from bias, and some own limitations of each technology. Originality: Comparison between these technologies will help to identify best-suited technology as per the customer requirements. It also provides development in this different field as per their extensive capability where these technologies can be successfully adopted. Conclusion: FDM and binding jet technology play an active role in industrial development. These help to assist the customisation and production of personalised parts cost-effectively. So, there is a need to understand how these technologies can provide these developments rapidly. These technologies help in easy changes or in making revised versions of the product, which is not easily possible in the conventional manufacturing system. High machine cost, the requirement of skilled human resources, low surface finish, and mechanical strength of product and material changing option is the main limitation of this technology. However, these limitations vary from technology to technology. In the future, these technologies are to be commercially viable for efficient usage in direct manufacturing of varied parts.

Keywords: 3D printing, comparison, fused deposition modeling, FDM, binding jet technology

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Authors: Rajkumar Ghosh

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Earthquakes are natural disasters that pose a significant risk to human life and infrastructure. Effective earthquake mitigation measures require a thorough understanding of the dynamics of seismic occurrences, including thrust movement. Traditionally, estimating thrust movement has relied on typical techniques that may not capture the full complexity of these events. Therefore, investigating alternative approaches, such as incorporating out-of-sequence thrust movement data, could enhance earthquake mitigation strategies. This review aims to provide an overview of the applications of out-of-sequence thrust movement in earthquake mitigation. By examining existing research and studies, the objective is to understand how precise estimation of thrust movement can contribute to improving structural design, analyzing infrastructure risk, and developing early warning systems. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources, including GPS measurements, satellite imagery, and seismic recordings. By analyzing and synthesizing these diverse datasets, researchers can gain a more comprehensive understanding of thrust movement dynamics during seismic occurrences. The review identifies potential advantages of incorporating out-of-sequence data in earthquake mitigation techniques. These include improving the efficiency of structural design, enhancing infrastructure risk analysis, and developing more accurate early warning systems. By considering out-of-sequence thrust movement estimates, researchers and policymakers can make informed decisions to mitigate the impact of earthquakes. This study contributes to the field of seismic monitoring and earthquake risk assessment by highlighting the benefits of incorporating out-of-sequence thrust movement data. By broadening the scope of analysis beyond traditional techniques, researchers can enhance their knowledge of earthquake dynamics and improve the effectiveness of mitigation measures. The study collects data from various sources, including GPS measurements, satellite imagery, and seismic recordings. These datasets are then analyzed using appropriate statistical and computational techniques to estimate out-of-sequence thrust movement. The review integrates findings from multiple studies to provide a comprehensive assessment of the topic. The study concludes that incorporating out-of-sequence thrust movement data can significantly enhance earthquake mitigation measures. By utilizing diverse data sources, researchers and policymakers can gain a more comprehensive understanding of seismic dynamics and make informed decisions. However, challenges exist, such as data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and improve the accuracy of estimates, further research and advancements in methodology are recommended. Overall, this review serves as a valuable resource for researchers, engineers, and policymakers involved in earthquake mitigation, as it encourages the development of innovative strategies based on a better understanding of thrust movement dynamics.

Keywords: earthquake, out-of-sequence thrust, disaster, human life

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Authors: Renu Saini, Deepak Agrawal

Abstract:

Background: Patients with head and spinal cord injury frequently have deranged coagulation profiles and require blood products transfusion perioperatively. Thromboelastography (TEG) is a ‘bedside’ global test of coagulation which may have role in deciding the need of transfusion in such patients. Aim: To assess the usefulness of TEG in department of neurosurgery in decreasing transfusion rates and coagulation-related mortality in traumatic head and spinal cord injury. Method and Methodology: A retrospective comparative study was carried out in the department of neurosurgery over a period of 1 year. There are two groups in this study. ‘Control’ group constitutes the patients in whom data was collected over 6 months (1/6/2009-31/12/2009) prior to installation of TEG machine. ‘Test’ group includes patients in whom data was collected over 6months (1/1/2013-30/6/2013) post TEG installation. Total no. of platelet, FFP, and cryoprecipitate transfusions were noted in both groups along with in hospital mortality and length of stay. Result: Both groups were matched in age and sex of patients, number of head and spinal cord injury cases, number of patients with thrombocytopenia and number of patients who underwent operation. Total 178 patients (135 head injury and 43 spinal cord injury patents) were admitted in neurosurgery department during time period June 2009 to December 2009 i.e. prior to TEG installation and after TEG installation a total of 243 patients(197 head injury and 46 spinal cord injury patents) were admitted. After TEG introduction platelet transfusion significantly reduced (p=0.000) compare to control group (67 units to 34 units). Mortality rate was found significantly reduced after installation (77 patients to 57 patients, P=0.000). Length of stay was reduced significantly (Prior installation 1-211days and after installation 1-115days, p=0.02). Conclusion: Bedside TEG can dramatically reduce platelet transfusion components requirement in department of neurosurgery. TEG also lead to a drastic decrease in mortality rate and length of stay in patients with traumatic head and spinal cord injuries. We recommend its use as a standard of care in the patients with traumatic head and spinal cord injuries.

Keywords: blood component transfusion, mortality, neurosurgery ICU, thromboelastography

Procedia PDF Downloads 325

Authors: Nihar Bheda

Abstract:

Protecting digital assets such as networks, systems, and data from advanced cyber threats is the aim of Digital Immunity Systems (DIS), which are a subset of cybersecurity. With features like continuous monitoring, coordinated reactions, and long-term adaptation, DIS seeks to mimic biological immunity. This minimizes downtime by automatically identifying and eliminating threats. Traditional security measures, such as firewalls and antivirus software, are insufficient for enterprises, such as healthcare providers, given the rapid evolution of cyber threats. The number of medical record breaches that have occurred in recent years is proof that attackers are finding healthcare data to be an increasingly valuable target. However, obstacles to enhancing security include outdated systems, financial limitations, and a lack of knowledge. DIS is an advancement in cyber defenses designed specifically for healthcare settings. Protection akin to an "immune system" is produced by core capabilities such as anomaly detection, access controls, and policy enforcement. Coordination of responses across IT infrastructure to contain attacks is made possible by automation and orchestration. Massive amounts of data are analyzed by AI and machine learning to find new threats. After an incident, self-healing enables services to resume quickly. The implementation of DIS is consistent with the healthcare industry's urgent requirement for resilient data security in light of evolving risks and strict guidelines. With resilient systems, it can help organizations lower business risk, minimize the effects of breaches, and preserve patient care continuity. DIS will be essential for protecting a variety of environments, including cloud computing and the Internet of medical devices, as healthcare providers quickly adopt new technologies. DIS lowers traditional security overhead for IT departments and offers automated protection, even though it requires an initial investment. In the near future, DIS may prove to be essential for small clinics, blood banks, imaging centers, large hospitals, and other healthcare organizations. Cyber resilience can become attainable for the whole healthcare ecosystem with customized DIS implementations.

Keywords: digital immunity system, cybersecurity, healthcare data, emerging technology

Procedia PDF Downloads 67