Search results for: post processing kinematics

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Mirco Tarozzi, Giacomo Pignagnoli, Andrea Benedetti

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The design of planning maintenance of civil structures often requires the evaluation of their level of safety in order to be able to choose which structure, and in which measure, it needs a structural retrofit. This work deals with the evaluation of the stiffness reduction of a scaled steel deck due to the presence of localized damages. The dynamic tests performed on it have shown the variability of its main frequencies linked to the gradual reduction of its rigidity. This deck consists in a steel grillage of four secondary beams and three main beams linked to a concrete slab. This steel deck is 6 m long and 3 m wide and it rests on two abutments made of concrete. By processing the signals of the accelerations due to a random excitation of the deck, the main natural frequencies of this bridge have been extracted. In order to assign more reliable parameters to the numerical model of the deck, some load tests have been performed and the mechanical property of the materials and the supports have been obtained. The two external beams have been cut at one third of their length and the structural strength has been restored by the design of a bolted plate. The gradual loss of the bolts and the plates removal have made the simulation of localized damage possible. In order to define the relationship between frequency variation and loss in stiffness, the identification of its natural frequencies has been performed, before and after the occurrence of the damage, corresponding to each step. The study of the relationship between stiffness losses and frequency shifts has been reported in this paper: the square of the frequency variation due to the presence of the damage is proportional to the ratio between the rigidities. This relationship can be used to quantify the loss in stiffness of a real scale bridge in an efficient way.

Keywords: damage detection, dynamic test, frequency shifts, operational modal analysis, steel bridge

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Authors: Duishon Shamatov

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This presentation is about Kazakhstan’s PhD students’ experiences with impact-factor publication requirement. Since the break-up of the USSR, Kazakhstan has been attempting to improve its higher education system at undergraduate and graduate levels. From March, 2010 Kazakhstan joined Bologna process and entered European space of higher education. To align with the European system of higher education, three level of preparation of specialists (undergraduate, master and PhD) was adopted to replace the Soviet system. The changes were aimed at promoting high quality higher education that meets the demands of labor market and growing needs of the industrial-innovative development of the country, and meeting the international standards. The shift to the European system has brought many benefits, but there are also some serious challenges. One of those challenges is related to the requirements for the PhD candidates to publish in national and international journals. Thus, a PhD candidate should have 7 publications in total, out of which one has to be in an international impact factor journal. A qualitative research was conducted to explore the PhD students’ views of their experiences with impact-factor publications. With the help of purposeful sampling, 30 PhD students from seven universities across Kazakhstan were selected for individual and focus group interviews. The key findings of the study are as follows. While the Kazakh PhD students have no difficulties in publishing in local journals, they face great challenges in attempting to publish in impact-factor journals for a range of reasons. They include but not limited to lack of research and publication skills, poorer knowledge of academic English, not familiarity with the peer review publication processes and expectations, and very short time to get published due to their PhD programme requirements. This situation is pushing some these young scholars explore alternative ways to get published in impact factor journals and they seek to publish by any means and often by any costs (which means even paying large sum of money for a publication). This in turn, creates a myth in the scholars’ circles in Kazakhstan, that to get published in impact factor journals, one should necessarily pay much money. This paper offers some policy recommendations on how to improve preparation of future PhD candidates in Kazakhstan.

Keywords: Bologna process, impact-factor publications, post-graduate education, Kazakhstan

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

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Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Fedili Benamar, Belloulou Mohamed Lamine, Ouahes Hassane, Ghattas Samir

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Introduction: Perioperative blood loss is a frequent cause of complications in total hip replacement (THR). The present prospective study assessed the efficacy of tranexamic acid (Exacyl(®)) in reducing blood loss in primary THR. Hypothesis: Tranexamic acid reduces blood loss in THR. Material and method: -This is a prospective randomized study on the effectiveness of Exacyl (tranexamic acid) in total hip replacement surgery performed on a standardized technique between 2019 and September 2022. -It involved 154 patients, of which 84 received a single injection of Exacyl (group 1) at a dosage of 10 mg/kg over 20 minutes during the perioperative period. -All patients received postoperative thromboprophylaxis with enoxaparin 0.4 ml subcutaneously. -All patients were admitted to the post-interventional intensive care unit for a duration of 24 hours for monitoring and pain management as per the service protocol. Results: 154 patients, of which 84 received a single injection of Exacyl (group 1) and 70 patients patients who did not receive Exacyl perioperatively : (Group 2 ) The average age is 57 +/- 15 years The distribution by gender was nearly equal with 56% male and 44% female; "The distribution according to the ASA score was as follows: 20.2% ASA1, 82.3% ASA2, and 17.5% ASA3. "There was a significant difference in the average volume of intraoperative and postoperative bleeding during the 48 hours." The average bleeding volume for group 1 (received Exacyl) was 614 ml +/- 228, while the average bleeding volume for group 2 was 729 +/- 300, with a chi-square test of 6.35 and a p-value < 0.01, which is highly significant. The ANOVA test showed an F-statistic of 7.11 and a p-value of 0.008. A Bartlett test revealed a chi-square of 6.35 and a p-value < 0.01." "In Group 1 (patients who received Exacyl), 73% had bleeding less than 750 ml (Group A), and 26% had bleeding exceeding 750 ml (Group B). In Group 2 (patients who did not receive Exacyl perioperatively), 52% had bleeding less than 750 ml (Group A), and 47% had bleeding exceeding 750 ml (Group B). "Thus, the use of Exacyl reduced perioperative bleeding and specifically decreased the risk of severe bleeding exceeding 750 ml by 43% with a relative risk (RR) of 1.37 and a p-value < 0.01. The transfusion rate was 1.19% in the population of Group 1 (Exacyl), whereas it was 10% in the population of Group 2 (no Exacyl). It can be stated that the use of Exacyl resulted in a reduction in perioperative blood transfusion with an RR of 0.1 and a p-value of 0.02. Conclusions: The use of Exacyl significantly reduced perioperative bleeding in this type of surgery.

Keywords: acid tranexamic, blood loss, anesthesia, total hip replacement, surgery

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Authors: Ali Aydin, Mert Sudagidan, Aysen Coban, Alparslan Kadir Devrim

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Pseudomonas spp. (specifically, P. fluorescens and P. fragi) are considered the principal spoilage microorganisms of refrigerated poultry meats. The higher the level psychrophilic spoilage Pseudomonas spp. on carcasses at the end of processing lead to decrease the shelf life of the refrigerated product. The aim of the study was the identification of psychrophilic Pseudomonas spp. having proteolytic and lipolytic activities from poultry meats by 16S rRNA and rpoB gene sequencing, investigation of protease and lipase related genes and determination of proteolytic activity of Pseudomonas spp. In the of isolation procedure, collected chicken meat samples from local markets and slaughterhouses were homogenized and the lysates were incubated on Standard method agar and Skim Milk agar for selection of proteolytic bacteria and tributyrin agar for selection of lipolytic bacteria at +4 °C for 7 days. After detection of proteolytic and lipolytic colonies, the isolates were firstly analyzed by biochemical tests such as Gram staining, catalase and oxidase tests. DNA gene sequencing analysis and comparison with GenBank revealed that 126 strong enzyme Pseudomonas spp. were identified as predominantly P. fluorescens (n=55), P. fragi (n=42), Pseudomonas spp. (n=24), P. cedrina (n=2), P. poae (n=1), P. koreensis (n=1), and P. gessardi (n=1). Additionally, protease related aprX gene was screened in the strains and it was detected in 69/126 strains, whereas, lipase related lipA gene was found in 9 Pseudomonas strains. Protease activity was determined using commercially available protease assay kit and 5 strains showed high protease activity. The results showed that psychrophilic Pseudomonas strains were present in chicken meat samples and they can produce important levels of proteases and lipases for food spoilage to decrease food quality and safety.

Keywords: Pseudomonas, chicken meat, protease, lipase

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Authors: Sofiya Rao, Sagnik Dey

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Aerosol-cloud-precipitation interaction continues to be one of the largest sources of uncertainty in quantifying the aerosol climate forcing. The uncertainty is increasing from global to regional scale. This problem remains unresolved due to the large discrepancy in the representation of cloud processes in the climate models. Most of the studies on aerosol-cloud-climate interaction and aerosol-cloud-precipitation over Indian Ocean (like INDOEX, CAIPEEX campaign etc.) are restricted to either particular to one season or particular to one region. Here we developed a theoretical framework to quantify aerosol indirect radiative forcing using Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol and cloud products of 15 years (2000-2015) period over the Indian Ocean. This framework relies on the observationally constrained estimate of the aerosol-induced change in cloud albedo. We partitioned the change in cloud albedo into the change in Liquid Water Path (LWP) and Effective Radius of Clouds (Reff) in response to an aerosol optical depth (AOD). Cloud albedo response to an increase in AOD is most sensitive in the range of LWP between 120-300 gm/m² for a range of Reff varying from 8-24 micrometer, which means aerosols are most sensitive to this range of LWP and Reff. Using this framework, aerosol forcing during a transition from indirect to semi-direct effect is also calculated. The outcome of this analysis shows best results over the Arabian Sea in comparison with the Bay of Bengal and the South Indian Ocean because of heterogeneity in aerosol spices over the Arabian Sea. Over the Arabian Sea during Winter Season the more absorbing aerosols are dominating, during Pre-monsoon dust (coarse mode aerosol particles) are more dominating. In winter and pre-monsoon majorly the aerosol forcing is more dominating while during monsoon and post-monsoon season meteorological forcing is more dominating. Over the South Indian Ocean, more or less same types of aerosol (Sea salt) are present. Over the Arabian Sea the Aerosol Indirect Radiative forcing are varying from -5 ± 4.5 W/m² for winter season while in other seasons it is reducing. The results provide observationally constrained estimates of aerosol indirect forcing in the Indian Ocean which can be helpful in evaluating the climate model performance in the context of such complex interactions.

Keywords: aerosol-cloud-precipitation interaction, aerosol-cloud-climate interaction, indirect radiative forcing, climate model

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Authors: Håkon Riiber

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This paper explores the extent to which Russian ‘Active Measures’ play a role in contemporary Russian foreign policy and in what way the legacy of the Soviet Union is still apparent in these practices. The analysis draws on a set of case studies from the 21st century to examine these aspects, showing which ‘Active Measures’ features are old and which are new in the post-Cold War era. The paper highlights that the topic has gained significant academic and political interest in recent years, largely due to the aggressive posture of the Russian Federation on the world stage, exemplified through interventions in Estonia, Georgia, and Ukraine and interference in several democratic elections in the West. However, the paper argues that the long-term impact of these measures may have unintended implications for Russia. While Russia is unlikely to stop using Active Measures, increased awareness of the exploitation of weaknesses, institutions, or other targets may lead to greater security measures and an ability to identify and defend against these activities. The paper contends that Soviet-style ‘Active Measures’ from the Cold War era have been modernized and are now utilized to create an advantageous atmosphere for further exploitation to support contemporary Russian foreign policy. It offers three key points to support this argument: the reenergized legacy of the Cold War era, the use of ‘Active Measures’ in a number of cases in the 21st century, and the applicability of AM to the Russian approach to foreign policy. The analysis reveals that while this is not a new Russian phenomenon, it is still oversimplified and inaccurately understood by the West, which may result in a decreased ability to defend against these activities and limit the unwarranted escalation of the ongoing security situation between the West and Russia. The paper concludes that the legacy of Soviet-era Active Measures continues to influence Russian foreign policy, and modern technological advances have only made them more applicable to the current political climate. Overall, this paper sheds light on the important issue of Russian ‘Active Measures’ and the role they play in contemporary Russian foreign policy. It emphasizes the need for increased awareness, understanding, and security measures to defend against these activities and prevent further escalation of the security situation between the West and Russia.

Keywords: Russian espionage, active measures, disinformation, Russian intelligence

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Authors: Guilherme Giacomini, Ana Luiza Menegatti Pavan, Allan Felipe Fattori Alves, Marcela de Oliveira, Fernando Antonio Bacchim Neto, José Ricardo de Arruda Miranda, Seizo Yamashita, Diana Rodrigues de Pina

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The maxillary sinus (MS), part of the paranasal sinus complex, is one of the most enigmatic structures in modern humans. The literature has suggested that MSs function as olfaction accessories, to heat or humidify inspired air, for thermoregulation, to impart resonance to the voice and others. Thus, the real function of the MS is still uncertain. Furthermore, the MS anatomy is complex and varies from person to person. Many diseases may affect the development process of sinuses. The incidence of rhinosinusitis and other pathoses in the MS is comparatively high, so, volume analysis has clinical value. Providing volume values for MS could be helpful in evaluating the presence of any abnormality and could be used for treatment planning and evaluation of the outcome. The computed tomography (CT) has allowed a more exact assessment of this structure, which enables a quantitative analysis. However, this is not always possible in the clinical routine, and if possible, it involves much effort and/or time. Therefore, it is necessary to have a convenient, robust, and practical tool correlated with the MS volume, allowing clinical applicability. Nowadays, the available methods for MS segmentation are manual or semi-automatic. Additionally, manual methods present inter and intraindividual variability. Thus, the aim of this study was to develop an automatic tool to quantity the MS volume in CT scans of paranasal sinuses. This study was developed with ethical approval from the authors’ institutions and national review panels. The research involved 30 retrospective exams of University Hospital, Botucatu Medical School, São Paulo State University, Brazil. The tool for automatic MS quantification, developed in Matlab®, uses a hybrid method, combining different image processing techniques. For MS detection, the algorithm uses a Support Vector Machine (SVM), by features such as pixel value, spatial distribution, shape and others. The detected pixels are used as seed point for a region growing (RG) segmentation. Then, morphological operators are applied to reduce false-positive pixels, improving the segmentation accuracy. These steps are applied in all slices of CT exam, obtaining the MS volume. To evaluate the accuracy of the developed tool, the automatic method was compared with manual segmentation realized by an experienced radiologist. For comparison, we used Bland-Altman statistics, linear regression, and Jaccard similarity coefficient. From the statistical analyses for the comparison between both methods, the linear regression showed a strong association and low dispersion between variables. The Bland–Altman analyses showed no significant differences between the analyzed methods. The Jaccard similarity coefficient was > 0.90 in all exams. In conclusion, the developed tool to quantify MS volume proved to be robust, fast, and efficient, when compared with manual segmentation. Furthermore, it avoids the intra and inter-observer variations caused by manual and semi-automatic methods. As future work, the tool will be applied in clinical practice. Thus, it may be useful in the diagnosis and treatment determination of MS diseases. Providing volume values for MS could be helpful in evaluating the presence of any abnormality and could be used for treatment planning and evaluation of the outcome. The computed tomography (CT) has allowed a more exact assessment of this structure which enables a quantitative analysis. However, this is not always possible in the clinical routine, and if possible, it involves much effort and/or time. Therefore, it is necessary to have a convenient, robust and practical tool correlated with the MS volume, allowing clinical applicability. Nowadays, the available methods for MS segmentation are manual or semi-automatic. Additionally, manual methods present inter and intraindividual variability. Thus, the aim of this study was to develop an automatic tool to quantity the MS volume in CT scans of paranasal sinuses. This study was developed with ethical approval from the authors’ institutions and national review panels. The research involved 30 retrospective exams of University Hospital, Botucatu Medical School, São Paulo State University, Brazil. The tool for automatic MS quantification, developed in Matlab®, uses a hybrid method, combining different image processing techniques. For MS detection, the algorithm uses a Support Vector Machine (SVM), by features such as pixel value, spatial distribution, shape and others. The detected pixels are used as seed point for a region growing (RG) segmentation. Then, morphological operators are applied to reduce false-positive pixels, improving the segmentation accuracy. These steps are applied in all slices of CT exam, obtaining the MS volume. To evaluate the accuracy of the developed tool, the automatic method was compared with manual segmentation realized by an experienced radiologist. For comparison, we used Bland-Altman statistics, linear regression and Jaccard similarity coefficient. From the statistical analyses for the comparison between both methods, the linear regression showed a strong association and low dispersion between variables. The Bland–Altman analyses showed no significant differences between the analyzed methods. The Jaccard similarity coefficient was > 0.90 in all exams. In conclusion, the developed tool to automatically quantify MS volume proved to be robust, fast and efficient, when compared with manual segmentation. Furthermore, it avoids the intra and inter-observer variations caused by manual and semi-automatic methods. As future work, the tool will be applied in clinical practice. Thus, it may be useful in the diagnosis and treatment determination of MS diseases.

Keywords: maxillary sinus, support vector machine, region growing, volume quantification

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Authors: S. Koul, Joshua Adedamola

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Currently, the global concerning fact about electromagnetic pollution (EMP) is that it not only adversely affects human health but rather projects the malfunctioning of sensitive equipment both locally and at a global level. The market offers many incumbent technologies to solve the issues, but still, a processable sustainable material solution with acceptable limits for GHG emission is still at an exploratory stage. The present work offers a sustainable material solution with a wide range of processability in terms of a polymeric resin matrix and shielding operational efficiency across the electromagnetic spectrum, covering both ionizing and non-ionizing electromagnetic radiations. The present work offers an in-situ synthesized conducting polyaniline (PANI) in the presence of the hybrid dual dopant system with tuned conductivity and high shielding efficiency between 89 to 92 decibels, depending upon the EMI frequency range. The conductive polymer synthesized in the presence of a hybrid dual dopant system via the in-situ emulsion polymerization method offers a higher surface resistance of 1.0 ohms/cm with thermal stability up to 2450C in their powder form. This conductive polymer with a hybrid dual dopant system was used as a filler material with different polymeric thermoplastic resin systems for the preparation of conductive composites. Intrinsically Conductive polymeric (ICP) composites based on hybrid dual dopant systems were prepared using melt blending, extrusion, and finally by, compression molding processing techniques. ICP composites with hybrid dual dopant systems offered good mechanical, thermal, structural, weathering, and stable surface resistivity properties over a period of time. The preliminary shielding behavior for ICP composites between frequency levels of 10 GHz to 24GHZ offered a shielding efficiency of more than 90 dB.

Keywords: ICP, dopant, EMI, shielding

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Authors: Ava Simone Arteaga

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This research explores the portrayal of drug use in U.K. digital news media, a topic of critical importance due to its influence on addiction treatment, recovery efforts, and public perceptions. Substance use disorder (SUD) as one of the most stigmatized health conditions globally, with media representations playing a crucial role in shaping societal attitudes. Despite the impact of media portrayals, there has been no comprehensive analysis of drug-related representations in U.K. digital news media for over thirteen years. This study aims to fill this gap by analyzing contemporary digital news depictions of drug use, focusing on how these portrayals influence public perception and contribute to stigma. This research will examine tabloid, national, and regional East Midlands press sites to understand current trends in drug-related reporting. The study will build on previous research, such as the 2010 UKDPC study, which revealed that drug users were often vilified, and that coverage was predominantly focused on criminal justice rather than recovery. Given the rise in drug-related deaths in the U.K. and the exacerbation of the drug crisis post-Brexit, this analysis is timely and crucial. The findings are expected to reveal how digital media continues to perpetuate stigma and misinformation about drug use. By comparing these findings with U.S. studies, the research will contribute to a better understanding of cross-cultural differences in drug-related media representations and inform policy discussions. The U.K. Government's ten-year plan to combat illegal drugs, which emphasizes reducing stigma, will benefit from this research by highlighting the need for improved media representations. Additionally, the study will engage with recent U.K. and international research on media stigma towards SUD to provide a broader context and comparative perspective. Ultimately, this study aims to drive changes in media reporting and contribute to the development of more effective public policies and interventions. By addressing current gaps in research and providing evidence-based recommendations, this work seeks to support the U.K. Government’s objectives and improve the media’s role in addressing drug-related issues.

Keywords: addiction, UK news media, media representations, depiction of drug use

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Authors: Fei Ye, Åsa Barrefelt, Manuchehr Abedi-Valugerdi, Khalid M. Abu-Salah, Salman A. Alrokayan, Mamoun Muhammed, Moustapha Hassan

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With appropriate encapsulation in functional nanoparticles drugs are more stable in physiological environment and the kinetics of the drug can be more carefully controlled and monitored. Furthermore, targeted drug delivery can be developed to improve chemotherapy in cancer treatment, not only by enhancing intracellular uptake by target cells but also by reducing the adverse effects in non-target organs. Inorganic imaging agents, delivered together with anti-cancer drugs, enhance the local imaging contrast and provide precise diagnosis as well as evaluation of therapy efficacy. We have developed biodegradable polymeric vesicles as a nanocarrier system for multimodal bio-imaging and anticancer drug delivery. The poly (lactic-co-glycolic acid) PLGA) vesicles were fabricated by encapsulating inorganic imaging agents of superparamagnetic iron oxide nanoparticles (SPION), manganese-doped zinc sulfide (MN:ZnS) quantum dots (QDs) and the anticancer drug busulfan into PLGA nanoparticles via an emulsion-evaporation method. T2-weighted magnetic resonance imaging (MRI) of PLGA-SPION-Mn:ZnS phantoms exhibited enhanced negative contrast with r2 relaxivity of approximately 523 s-1 mM-1 Fe. Murine macrophage (J774A) cellular uptake of PLGA vesicles started fluorescence imaging at 2 h and reached maximum intensity at 24 h incubation. The drug delivery ability PLGA vesicles was demonstrated in vitro by release of busulfan. PLGA vesicles degradation was studied in vitro, showing that approximately 32% was degraded into lactic and glycolic acid over a period of 5 weeks. The biodistribution of PLGA vesicles was investigated in vivo by MRI in a rat model. Change of contrast in the liver could be visualized by MRI after 7 min and maximal signal loss detected after 4 h post-injection of PLGA vesicles. Histological studies showed that the presence of PLGA vesicles in organs was shifted from the lungs to the liver and spleen over time.

Keywords: biodegradable polymers, multifunctional nanoparticles, quantum dots, anticancer drugs

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Authors: Neslihan Sahin Celik, Ali Eraslan

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Mathematical model and modeling are one of the topics that have been intensively discussed in recent years. In line with the results of the PISA studies, researchers in many countries have begun to question how much students in school-education system are prepared to solve the real-world problems they encounter in their future professional lives. As a result, many mathematics educators have begun to emphasize the importance of new skills and understanding such as constructing, Hypothesizing, Describing, manipulating, predicting, working together for complex and multifaceted problems for success in beyond the school. When students increasingly face this kind of situations in their daily life, it is important to make sure that students have enough experience to work together and interpret mathematical situations that enable them to think in different ways and share their ideas with their peers. Thus, model eliciting activities are one of main tools that help students to gain experiences and the new skills required. This research study was carried on the town center of a big city located in the Black Sea region in Turkey. The participants were eighth-grade students in a middle school. After a six-week preliminary study, three students in an eighth-grade classroom were selected using criterion sampling technique and placed in a focus group. The focus group of three students was videotaped as they worked on a model eliciting activity, the Summer Job Problem. The conversation of the group was transcribed, examined with students’ written work and then qualitatively analyzed through the lens of Blum’s (1996) modeling processing cycle. The study results showed that eighth grade students can successfully work with the model eliciting, develop a model based on the two parameters and review the whole process. On the other hand, they had difficulties to relate parameters to each other and take all parameters into account to establish the model.

Keywords: middle school, modeling, mathematical modeling, summer job problem

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Authors: Areej Alosimi, Heather Wharrad, Katharine Whittingham

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Palliative care is a crucial element in nursing, especially with the steep increase in non-communicable diseases. Providing education in palliative care can help elevate the standards of care and address the growing need for it. However, palliative care has not been introduced into nursing curricula, specifically in Saudi Arabia, evidenced by students' inadequate understanding of the subject. Digital learning has been identified as a persuasive and effective method to improve education. The study aims to assess the feasibility and accessibility of implementing digital learning in palliative care education in Saudi Arabia by investigating the potential of delivering palliative care nurse education via distance learning. The study will utilize a sequential exploratory mixed-method approach. Phase one will entail identifying needs, developing a web-based program in phase two, and intervention implementation with a pre-post-test in phase three. Semi-structured interviews will be conducted to explore participant perceptions and thoughts regarding the intervention. Data collection will incorporate questionnaires and interviews with nursing students. Data analysis will use SPSS to analyze quantitative measurements and NVivo to analyze qualitative aspects. The study aims to provide insights into the feasibility of implementing digital learning in palliative care education. The results will serve as a foundation to investigate the effectiveness of e-learning interventions in palliative care education among nursing students. This study addresses a crucial gap in palliative care education, especially in nursing curricula, and explores the potential of digital learning to improve education. The results have broad implications for nursing education and the growing need for palliative care globally. The study assesses the feasibility and accessibility of implementing digital learning in palliative care education in Saudi Arabia. The research investigates whether palliative care nurse education can be effectively delivered through distance learning to improve students' understanding of the subject. The study's findings will lay the groundwork for a larger investigation on the efficacy of e-learning interventions in improving palliative care education among nursing students. The study can potentially contribute to the overall advancement of nursing education and the growing need for palliative care.

Keywords: undergraduate nursing students, E-Learning, Palliative care education, Knowledge

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Authors: Abdulraaof H. Alqaili, Hamad A. Alsoliman

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Through progress in pavement design developments, a pavement design method was developed, which is titled the Mechanistic Empirical Pavement Design Guide (MEPDG). Nowadays, the evolution in roads network and highways is observed in Saudi Arabia as a result of increasing in traffic volume. Therefore, the MEPDG currently is implemented for flexible pavement design by the Saudi Ministry of Transportation. Implementation of MEPDG for local pavement design requires the calibration of distress models under the local conditions (traffic, climate, and materials). This paper aims to prepare data for calibration of MEPDG in Central Saudi Arabia. Thus, the first goal is data collection for the design of flexible pavement from the local conditions of the Riyadh region. Since, the modifying of collected data to input data is needed; the main goal of this paper is the analysis of collected data. The data analysis in this paper includes processing each: Trucks Classification, Traffic Growth Factor, Annual Average Daily Truck Traffic (AADTT), Monthly Adjustment Factors (MAFi), Vehicle Class Distribution (VCD), Truck Hourly Distribution Factors, Axle Load Distribution Factors (ALDF), Number of axle types (single, tandem, and tridem) per truck class, cloud cover percent, and road sections selected for the local calibration. Detailed descriptions of input parameters are explained in this paper, which leads to providing of an approach for successful implementation of MEPDG. Local calibration of MEPDG to the conditions of Riyadh region can be performed based on the findings in this paper.

Keywords: mechanistic-empirical pavement design guide (MEPDG), traffic characteristics, materials properties, climate, Riyadh

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Authors: Pajaree Naramitpanich, Natchanok Jansawang, Panwilai Chomchid

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The aims of this are compared between the students’ logical thinking abilities of their learning for designing the 5-lesson instructional plans of the 2-instructional methods, namely; the STEM Education and the Creative Learning Process (CLP) for developing students’ logical thinking abilities that a sample consisted of 90 students from two chemistry classes of different learning outcomes in Wapi Phathum School with the cluster random sampling technique was used at the 11th grade level. To administer of their learning environments with the 45-experimenl student group by the STEM Education method and the 45-controlling student group by the Creative Learning Process. These learning different groups were obtained using the 5 instruments; the 5-lesson instructional plans of the STEM Education and the Creative Learning Process to enhance the logical thinking tests on Mineral issue were used. The efficiency of the Creative Learning Processes (CLP) Model and the STEM Education’s innovations of these each five instructional lesson plans based on criteria are higher than of 80/80 standard level with the IOC index from the expert educators. The averages mean scores of students’ learning achievement motives were assessed with the Pre and Post Techniques and Logical Thinking Ability Test (LTAT) and dependent t-test analysis were differentiated between the CLP and the STEM, significantly. Students’ perceptions of their chemistry classroom environment inventories with the MCI with the CLP and the STEM methods also were found, differently. Associations between students’ perceptions of their chemistry classroom learning environment inventories on the CLP Model and the STEM Education learning designs toward their logical thinking abilities toward chemistry, the predictive efficiency of R2 values indicate that 68% and 76% of the variances in students’ logical thinking abilities toward chemistry to their controlling and experimental chemistry classroom learning environmental groups with the MCI were correlated at .05 levels, significantly. Implementations of this result are showed the students’ learning by the CLP of the potential thinking life-changing roles in most their logical thinking abilities that it is revealed that the students perceive their abilities to be highly learning achievement in chemistry group are differentiated with the STEM education of students’ outcomes.

Keywords: design, the lesson instructional plans, the stem education, the creative learning process, logical thinking ability, different, learning outcome, student, chemistry class

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Authors: L. Piñeiro, N. Cobas, L. Gómez-Limia, S. Martínez, I. Franco

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The swordfish (Xiphias gladius) is a large oceanic fish of high commercial value, which is widely distributed in waters of the world’s oceans. They are considered to be an important source of high quality proteins, vitamins and essential fatty acids, although only half of the population follows the recommendation of nutritionists to consume fish at least twice a week. Swordfish is consumed worldwide because of its low fat content and high protein content. It is generally sold as fresh, frozen, and as pieces or slices. The aim of this study was to evaluate the effect of salting and frying on the composition of the water-soluble vitamins (B₂, B₃, B₉ and B₁₂) and fat-soluble vitamins (A, D, and E) of swordfish. Three loins of swordfish from Pacific Ocean were analyzed. All the fishes had a weight between 50 and 70 kg and were transported to the laboratory frozen (-18 ºC). Before the processing, they were defrosted at 4 ºC. Each loin was sliced and salted in brine. After cleaning the slices, they were divided into portions (10×2 cm) and fried in olive oil. The identification and quantification of vitamins were carried out by high-performance liquid chromatography (HPLC), using methanol and 0.010% trifluoroacetic acid as mobile phases at a flow-rate of 0.7 mL min^-1. The UV-Vis detector was used for the detection of the water- and fat-soluble vitamins (A and D), as well as the fluorescence detector for the detection of the vitamin E. During salting, water and fat-soluble vitamin contents remained constant, observing an evident decrease in the values of vitamin B₂. The diffusion of salt into the interior of the pieces and the loss of constitution water that occur during this stage would be related to this significant decrease. In general, after frying water-soluble and fat-soluble vitamins showed a great thermolability with high percentages of retention with values among 50–100%. Vitamin B₃ is the one that exhibited higher percentages of retention with values close to 100%. However, vitamin B₉ presented the highest losses with a percentage of retention of less than 20%.

Keywords: frying, HPLC, salting, swordfish, vitamins

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Authors: Abdallah Al-ZoubI, Mamoun Dmour

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The spectrum of IoT devices is becoming widely diversified, entering almost all possible fields and finding applications in industry, health, finance, logistics, education, to name a few. The IoT active endpoint sensors and devices exceeded the 12 billion mark in 2021 and are expected to reach 27 billion in 2025, with over $34 billion in total market value. This sheer rise in numbers and use of IoT devices bring with it considerable concerns regarding data storage, analysis, manipulation and protection. IoT Blockchain-based systems have recently been proposed as a decentralized solution for large-scale data storage and protection. COVID-19 has actually accelerated the desire to utilize IoT devices as it impacted both demand and supply and significantly affected several regions due to logistic reasons such as supply chain interruptions, shortage of shipping containers and port congestion. An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in solidity to execute and manage data generated by IoT sensors and devices. such as Raspberry Pi 4, Rasbpian, and add-on hardware security modules. The proposed system will run a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem run by blockchain where a number of distributed IoT devices can communicate and interact, thus forming a closed, controlled environment. A prototype has been deployed with three IoT handling units distributed over a wide geographical space in order to examine its feasibility, performance and costs. Initial results indicated that big IoT data retrieval and storage is feasible and interactivity is possible, provided that certain conditions of cost, speed and thorough put are met.

Keywords: IoT devices, blockchain, Ethereum, big data

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Authors: Abdul-Rahman Al-Ali

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As the number of mobile devices is growing exponentially, it is estimated to connect about 50 million devices to the Internet by the year 2020. At the end of this decade, it is expected that an average of eight connected devices per person worldwide. The 50 billion devices are not mobile phones and data browsing gadgets only, but machine-to-machine and man-to-machine devices. With such growing numbers of devices the Internet of Things (I.o.T) concept is one of the emerging technologies as of recently. Within the smart grid technologies, smart home appliances, Intelligent Electronic Devices (IED) and Distributed Energy Resources (DER) are major I.o.T objects that can be addressable using the IPV6. These objects are called the smart grid internet of things (SG-I.o.T). The SG-I.o.T generates big data that requires high-speed computing infrastructure, widespread computer networks, big data storage, software, and platforms services. A company’s utility control and data centers cannot handle such a large number of devices, high-speed processing, and massive data storage. Building large data center’s infrastructure takes a long time, it also requires widespread communication networks and huge capital investment. To maintain and upgrade control and data centers’ infrastructure and communication networks as well as updating and renewing software licenses which collectively, requires additional cost. This can be overcome by utilizing the emerging computing paradigms such as cloud computing. This can be used as a smart grid enabler to replace the legacy of utilities data centers. The talk will highlight the role of I.o.T, cloud computing services and their development models within the smart grid technologies.

Keywords: intelligent electronic devices (IED), distributed energy resources (DER), internet, smart home appliances

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Authors: Edison A. Bonifaz

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In this work, a numerical procedure is proposed to design dense multi-material structures using the Directed Energy Deposition (DED) process. A thermo-mechanical finite element model to predict thermal cycles and residual stresses is presented. A numerical layer build-up procedure coupled with a moving heat flux was constructed to minimize strains and residual stresses that result in the multi-layer deposition of an AISI 316 austenitic steel on an AISI 304 austenitic steel substrate. To simulate the DED process, the automated interface of the ABAQUS AM module was used to define element activation and heat input event data as a function of time and position. Of this manner, the construction of ABAQUS user-defined subroutines was not necessary. Thermal cycles and thermally induced stresses created during the multi-layer deposition metal AM pool crystallization were predicted and validated. Results were analyzed in three independent metal layers of three different experiments. The one-way heat and material deposition toolpath used in the analysis was created with a MatLab path script. An optimal combination of feedstock and heat input printing parameters suitable for fabricating multi-material dense structures in the directed energy deposition metal AM process was established. At constant power, it can be concluded that the lower the heat input, the lower the peak temperatures and residual stresses. It means that from a design point of view, the one-way heat and material deposition processing toolpath with the higher welding speed should be selected.

Keywords: event series, thermal cycles, residual stresses, multi-pass welding, abaqus am modeler

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Authors: Mahnaz Seyedoshohadaee, Azadeh Nematolahi, Parsa Rahimi

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Introduction: Rehabilitation training after hip joint surgery is one of the priorities of nursing, which can be helpful in today's world with the advancement of technology. This study was conducted with the aim of the effect of Tele rehabilitation Education on outcomes of hip osteoarthritis. Methods: The present study was a semi-experimental study that was conducted on patients after hip replacement in the first half of 2023. To perform the work, 70 patients who were available were included in the study and were divided into two intervention and control groups by a nonrandom method. Inclusion criteria included: a maximum of 6 months had passed since the hip joint replacement, age between 30-70 years, the ability to follow instructions by the subject, the absence of accompanying orthopedic lesions such as fractures, and having access to the Internet, a smartphone, and the Skype program. Exclusion criteria were severe speech disorder and non-participation in a training session. The research tool included a demographic profile form and Hip disability and osteoarthritis outcome score (HOOS), which were completed by the patients before and after the training. Training for people in the intervention group in 4 sessions, including introduction of the disease, risk factors, symptoms, management of disease symptoms, medication, diet, appropriate exercises and pain relief methods, one session per week for 30 to 45 minutes in the groups 4 to 6 people were offered through Skype software. SPSS version 22 statistical software was used to analyze the data. Results: The average score of osteoarthritis outcomes in the patients before the intervention was 112.74±29.64 in the test group and 110.41±16.34 in the control group, which had no significant difference (P=0.682). After the intervention, it reached 85.25±21.43 and 109.94±15.74, respectively, and this difference was significant (P<0.001). The comparison of the average scores of osteoarthritis results in the test group indicated a significant difference from the pre-test to the post-test time (p<0.001). But in the control group, this difference was not significant (p=0.130). Conclusion: The results showed that Tele rehabilitation Education has a positive effect on reducing the outcomes of hip osteoarthritis, so it is recommended that nurses use Tele rehabilitation Education in their training in order to empower patients.

Keywords: training, rehabilitation, hip osteoarthritides, patient, complications

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Grazia Disciglio, Francesco Lops, Annalisa Tarantino, Emanuele Tarantino

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The parasitic weed specie Phelipanche ramosa (L) Pomel is one of the major constraints in tomato crop in Apulia region (southern Italy). The experimental was considered to investigate the effect of six organic compounds (Olive miller wastewater, Allil isothiocyanate®, Alfa plus K®, Radicon®, Rizosum Max®, Kendal Nem®) on the naturally infested field of tomato growing season in 2016. The randomized block design with 3 replicates was adopted. Tomato seedling were transplant on 19 May 2016. During the growing cycle of the tomato at 74, 81, 93 and 103 days after transplantation (DAT), the number of parasitic shoots (branched plants) that had emerged in each plot was determined. At harvesting on 13 September 2016 the major quanti-qualitative yield parameters were determined, including marketable yield, mean weight, dry matter, soluble solids, fruit colour, pH and titratable acidity. The treatments provided the results show that none of treatments provided complete control against P. ramosa. However, among the products tested Olive miller wastewater, Alfa plus K®, Rizosum Max® and Kendal Nem® products applied to the soil show the number of emerged shoots significantly lower than Radicon® and especially than the Allil isothiocyanate® treatment and the untreated control. Regarding the effect of different treatments on the tomato productive parameters, the marketable yield resulted significantly higher in the same mentioned treatments which gave the lower P. ramosa infestation. No significative differences for the other fruit characteristics were observed.

Keywords: processing tomato crop, Phelipanche ramosa, olive-mill wastewater, organic fertilizers

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Authors: Safieh Javadinejad

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In order to make well-informed decisions respecting long-term system planning, resource managers and policy creators necessitate to comprehend the interconnections among energy and water utilization and manufacture—and also the energy-water nexus. Planning and assessment issues contain the enhancement of strategies for declining the water and energy system’s vulnerabilities to climate alteration with also emissions of decreasing greenhouse gas. In order to deliver beneficial decision support for climate adjustment policy and planning, understanding the regionally-specific features of the energy-water nexus, and the history-future of the water and energy source systems serving is essential. It will be helpful for decision makers understand the nature of current water-energy system conditions and capacity for adaptation plans for future. This research shows an integrated hydrology/energy modeling platform which is able to extend water-energy examines based on a detailed illustration of local circumstances. The modeling links the Water Evaluation and Planning (WEAP) and the Long Range Energy Alternatives Planning (LEAP) system to create full picture of water-energy processes. This will allow water managers and policy-decision makers to simply understand links between energy system improvements and hydrological processing and realize how future climate change will effect on water-energy systems. The Zayandeh Rud river basin in Iran is selected as a case study to show the results and application of the analysis. This region is known as an area with large integration of both the electric power and water sectors. The linkages between water, energy and climate change and possible adaptation strategies are described along with early insights from applications of the integration modeling system.

Keywords: climate impacts, hydrology, water systems, adaptation planning, electricity, integrated modeling

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Authors: Yebeen Ji, Jimin Yun, Kwonhoo Kim

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Magnesium alloys were limited for industrial applications due to having a limited slip system and high plastic anisotropy. It has been known that specific textures were formed during processing (rolling, etc.), and These textures cause poor formability. To solve these problems, many researchers have studied controlling texture by adding rare-earth elements. However, the high cost limits their use; therefore, alternatives are needed to replace them. Although Pb addition doesn’t directly improve magnesium properties, it has been known to suppress the diffusion of other alloying elements and reduce grain boundary energy. These characteristics are similar to the additions of rare-earth elements, and a similar texture behavior is expected as well. However, there is insufficient research on this. Therefore, this study investigates the behavior of texture and microstructure development after adding Pb to magnesium. This study compared and analyzed AZ61 alloy and Mg-15wt%Pb alloy to determine the effect of adding solute elements. The alloy was hot rolled and annealed to form a single phase and initial texture. Afterward, the specimen was set to contraction and elongate parallel to the rolling surface and the rolling direction and then subjected to high-temperature plane strain compression under the conditions of 723K and 0.05/s. Microstructural analysis and texture measurements were performed by SEM-EBSD. The peak stress in the true strain-stress curve after compression was higher in AZ61, but the shape of the flow curve was similar for both alloys. For both alloys, continuous dynamic recrystallization was confirmed to occur during the compression process. The basal texture developed parallel to the compressed surface, and the pole density was lower in the Mg-15wt%Pb alloy. It is confirmed that this change in behavior is because the orientation distribution of recrystallized grains has a more random orientation compared to the parent grains when Pb is added.

Keywords: Mg, texture, Pb, DRX

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: Abdesselem Dakhli, Wajdi Bellil, Chokri Ben Amar

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DNA Barcode, a short mitochondrial DNA fragment, made up of three subunits; a phosphate group, sugar and nucleic bases (A, T, C, and G). They provide good sources of information needed to classify living species. Such intuition has been confirmed by many experimental results. Species classification with DNA Barcode sequences has been studied by several researchers. The classification problem assigns unknown species to known ones by analyzing their Barcode. This task has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. To make this type of analysis feasible, heuristics, like progressive alignment, have been developed. Another tool for similarity search against a database of sequences is BLAST, which outputs shorter regions of high similarity between a query sequence and matched sequences in the database. However, all these methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. This method permits to avoid the complex problem of form and structure in different classes of organisms. On empirical data and their classification performances are compared with other methods. Our system consists of three phases. The first is called transformation, which is composed of three steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. The second is called approximation, which is empowered by the use of Multi Llibrary Wavelet Neural Networks (MLWNN).The third is called the classification of DNA Barcodes, which is realized by applying the algorithm of hierarchical classification.

Keywords: DNA barcode, electron-ion interaction pseudopotential, Multi Library Wavelet Neural Networks (MLWNN)

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Authors: S. D. El Wakil, M. Pladsen

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Fiber reinforced polymeric (FRP) composites are finding wide-spread industrial applications because of their exceptionally high specific strength and specific modulus of elasticity. Nevertheless, it is very seldom to get ready-for-use components or products made of FRP composites. Secondary processing by machining, particularly drilling, is almost always required to make holes for fastening components together to produce assemblies. That creates problems since the FRP composites are neither homogeneous nor isotropic. Some of the problems that are encountered include the subsequent damage in the region around the drilled hole and the drilling – induced delamination of the layer of ply, that occurs both at the entrance and the exit planes of the work piece. Evidently, the functionality of the work piece would be detrimentally affected. The current work was carried out with the aim of eliminating or at least minimizing the work piece damage associated with drilling of FPR composites. Each test specimen involves a woven reinforced graphite fiber/epoxy composite having a thickness of 12.5 mm (0.5 inch). A large number of test specimens were subjected to drilling operations with different combinations of feed rates and cutting speeds. The drilling induced damage was taken as the absolute value of the difference between the drilled hole diameter and the nominal one taken as a percentage of the nominal diameter. The later was determined for each combination of feed rate and cutting speed, and a matrix comprising those values was established, where the columns indicate varying feed rate while and rows indicate varying cutting speeds. Next, the analysis of variance (ANOVA) approach was employed using Minitab software, in order to obtain the combination that would improve the drilling induced damage. Experimental results show that low feed rates coupled with low cutting speeds yielded the best results.

Keywords: drilling of composites, dimensional accuracy of holes drilled in composites, delamination and charring, graphite-epoxy composites

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Authors: Gaurav Kumar Sinha

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In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

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Authors: Soumya Mishra, Elena Neiterman

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Aligned with the growing interest in the impact of the pandemic on academic experiences of university students, this study aimed to examine the challenges Canadian undergraduate students experienced during the university closures due to COVID-19. Using qualitative methodological approach, the study utilized semi-structured interviews conducted with 20 undergraduate students enrolled in an Ontario university to explore their thoughts and experience regarding online learning during the peak of the COVID-19 pandemic, from January 2021 to March 2021. The interviews yielded four major themes with the following associated subthemes: Personal Challenges Associated with Adapting to the Pandemic (Change in the Type of Stress Experienced, Unique Impact on Certain Groups of Students, Decreased Motivation, Crucial Role of Resilience), Social Challenges Associated with Adapting to the Pandemic (Increased Loneliness, Challenges Faced while Communicating, Perception of Group work, Role of Living Conditions), Challenges associated with Accessing University Resources (Crucial Role of Professors, Perception of Virtual Events, Importance of Physical Spaces). Overall, the analysis showed that the COVID-19 pandemic fostered resilience and psychological flexibility amongst all students. However, the mental health and social wellbeing of students deteriorated during the COVID-19 pandemic and they reported experiencing chronic stress, anxiety and loneliness. International students, first year and final year students experienced a unique set of challenges. It was hard for participants in our study to make strong new connections with their classmates and maintain existing friendships with their peers. The importance of professors in facilitating learning was amplified in the online environment due to the lack of in-person interaction with other students. Despite these challenges, most participants reported that they received high grades during online learning. The findings from this study could be helpful for organizations and individuals working towards fostering the wellbeing of undergraduate students. They can also help in making post-secondary institutions more resilient to future emergencies by creating contingency plans regarding online instructions and risk management techniques.

Keywords: Canadian, COVID-19, university students, wellbeing

Procedia PDF Downloads 100