Search results for: virtual machine migration

Authors: Rosemary R. Carlton, Roxane Caron

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Almost three decades ago, bell hooks (1994) identified the classroom as “the most radical space of possibility in the academy”. A feminist scholar, educator, and activist, hooks urged educators to transgress the boundaries of what might be customary or considered acceptable in teaching, thus encouraging the pursuit of new ways of knowing and different strategies for sharing knowledge. This paper reflects upon a particular response to hooks’ still relevant call for transgression in teaching. Specifically, this paper reports on the design, implementation, and preliminary analysis of a social work course integrating active pedagogy and transnational exchange to encourage students’ critical thinking and autonomous learning in their development as social workers in a global context. The bachelor’s level course, Pratiques spécifiques: Projet international, was developed collaboratively across three francophone institutions of higher learning in Belgium, Canada, and France: the Haute École de Namur-Liège-Luxembourg (Hénallux); the Université de Montréal; and, the Institut d’enseignement supérieur et professionnel, l’IRTS Paris Île-de-France. The driving aims of the course are to promote autonomous learning and critical thinking through a lens of transnational understandings of social problems -competencies indispensable to students’ development as social workers. The course is offered to two paired cohorts, one addressing the subject of “migrations” (Canada/France) and the other the subject of “sexual exploitation” (Canada/Belgium). Through the adaptation of a critical pedagogy of problem-based learning, students are called upon to actively engage in acquiring and applying knowledge to respond to “real life” social issues relating to migration or sexual exploitation. At the conclusion of the course, each cohort of students is brought together for a week-long intensive period of transnational exchange either at the Université de Montréal in Canada or at Hénallux in Belgium. Extending the bounds of the classroom across international borders allows students novel opportunities to deepen and expand their understandings of issues relating to predefined social issues and to critically examine associated social work practices. The paper opens with a presentation of the social work course. Specifically, the authors will outline their adaptation of a pedagogy of problem-based learning integrating transnational exchange in the design and implementation of the course. Returning to hooks’ notion of transgression in teaching, the paper offers a preliminary analysis of how and with what effect the course provides opportunities to transgress hierarchical student-teacher relationships; transgress conventional modes of learning to explore diverse sources of knowledge and transgress the walls of the university to engage with and learn from local and global partners. The paper concludes with a consideration of the potential influence of such transgressions in teaching for students’ development of critical thinking in their practice of social work in global context.

Keywords: active learning, critical pedagogy, social work intervention, transnational learning

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Authors: Betty Chiyangwa

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The subject of second-generation migrant youth is under-researched in the context of South Africa. Thus, their opinions and views have been marginalised in social science research. This paper addresses this gap by exploring the complexities of second-generation Mozambican migrant youth’s lived experiences in how they construct their identities and develop a sense of belonging in post-apartheid South Africa, specifically in Bushbuckridge. Bushbuckridge was among the earliest districts to accommodate Mozambican refugees to South Africa in the 1970s and remains associated with large numbers of Mozambicans. Drawing on Crenshaw’s (1989) intersectionality approach, the study contributes to knowledge on South-to-South migration by demonstrating how this approach is operationalised to understand the complex lived experiences of a disadvantaged group in life and possibly in death. In conceptualising the notion of identity among second-generation migrant youth, this paper explores the history and present of first and second-generation Mozambican migrants in South Africa to reveal how being born to migrant parents and raised in a hosting country poses life-long complications in one’s identity and sense of belonging. In the quest to form their identities and construct a sense of belonging, migrant youth employ precariously means to navigate the terrane. This is a case study informed by semi-structured interviews and narrative data gathered from 22 second-generation Mozambican migrant youth between 18 and 34 years who were born to at least one Mozambican parent living in Bushbuckridge and raised in South Africa. Views of two key informants from the South African Department of Home Affairs and the local tribal authority provided additional perspectives on second-generation migrant youth’s lived experiences in Bushbuckridge, which were explored thematically and narratively through Braun and Clarke’s (2012) six-step framework for analysing qualitative data. In exploring the interdependency and interconnectedness of social categories and social systems in Bushbuckridge, the findings revealed that participants’ experiences of identity formation and development of a sense of belonging were marginalised in complex, intersectional and precarious ways where they constantly (re)negotiated their daily experiences, which were largely shaped by their paradoxical migrant status in a host country. This study found that, in the quest for belonging, migrant youths were not a perfectly integrated category but evolved from almost daily lived experiences of creating a living that gave them an identity and a sense of belonging in South Africa. The majority of them shared feelings of living in limbo since childhood and fear of possibly dying in limbo with no clear (solid) sense of belonging to either South Africa or Mozambique. This study concludes that there is a strong association between feelings of identity, sense of belonging and levels of social integration. It recommends the development and adoption of a multilayer comprehensive model for understanding second-generation migrant youth identity and belonging in South Africa which encourages a collaborative effort among individual migrant youth, their family members, neighbours, society, and regional and national institutional structures for migrants to enhance and harness their capabilities and improve their wellbeing in South Africa.

Keywords: bushbuckridge, limbo, mozambican migrants, second-generation

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Authors: Vaisnavy Govindasamy, Saba Ishrat

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Introduction: The Dexamethasone Intravitreal Implant 0.7 mg (OzurdexTM, Allergan®) is a biodegradable corticosteroid implant approved by the FDA for managing diabetic macular edema (DMO), macular edema following branch retinal vein occlusion (BRVO) or central retinal vein occlusion (CRVO), and posterior segment non-infectious uveitis. This implant can release dexamethasone over a six-month period, exhibiting peak effectiveness between 60 and 90 days post-administration. The intravitreal injection should be performed under sterile conditions. At James Cook University Hospital (JCUH), Ozurdex injections are currently administered in the Vitreo-Retinal (VR) theatre. This study aimed to evaluate the feasibility and potential advantages of establishing a dedicated clinic for Ozurdex administration separate from the VR theatre setting. Method: Retrospectively, data of all Ozurdex injections administered between October 2021 to October 2022 was collected from operating theatre registers at JCUH. Data pertaining to the indications for Ozurdex; waiting times from referral date to date of injection; duration of theatre time consumed; and post-injection complications were collected from electronic notes. The resources needed to establish a dedicated Ozurdex clinic were evaluated. Over a six-month period from October 2023 to March 2024, we gathered data on utilization of theatre 28. Results: A total of 135 Ozurdex injections were administered. Among the indications, uveitis represented 47.3% of cases, DMO with 23.6% and RVO with 22.9%. Remaining cases lacked sufficient data. Each Ozurdex injection procedure consumed 15 minutes in the VR theatre list. Complications arose in 5% of injections, totaling 7 cases. These included glaucoma, ocular hypertension, subconjunctival haemorrhage and implant migration. Waiting times averaged 6 weeks from date for referral to procedure date. We also found that, on an average theatre 28 was offered but remained unused for 4 days, totalling eight sessions in a month. Analysis: Establishing a sperate Ozurdex clinic would improve the quality of patient care in following ways: 1.Decrease injection waiting times (currently averaging 6 weeks), leading to better visual outcomes. 2.Free up approximately three hours of theatre time in Vitreo-Retina theatres each month, allowing for 3-4 additional surgeries. Reduce waiting times for critical retinal surgeries and enhance visual outcomes. 3.Provide additional training opportunities for trainees and retina fellows, improving their skills. 4.Optimize the use of empty theatre slots (theatre 28) currently experiencing underutilization of resources. Conclusion: These findings support the implementation of a separate clinic for administering Ozurdex injections at JCUH. It is evident that introducing a dedicated clinic will enhance operational efficiency, optimise resource utilsation, and improve overall quality of care for patients undergoing this treatment.

Keywords: opthalmology, ozurdex, efficiency, complication

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Authors: Aura Maria Lopera Echavarria, Angela Maria Lema Perez, Daniela Medrano David, Pedronel Araque Marin, Marta Elena Londoño Lopez

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Bone regeneration is the process by which the formation of new bone is stimulated. Bone fractures can originate at any time due to trauma, infections, tumors, congenital malformations or skeletal diseases. Currently there are different strategies to treat bone defects that in some cases, regeneration does not occur on its own. That is why they are treated with bone substitutes, which provide a necessary environment for the cells to synthesize new bone. The Demineralized Bone Matrix (DBM) is widely used as a bone implant due to its good properties, such as osteoinduction and bioactivity. However, the use of DBM is limited, because its presentation is powder, which is difficult to implant with precision and is susceptible to migrating to other sites through blood flow. That is why the DBM is commonly incorporated into a variety of vehicles or carriers. The objective of this project is to evaluate the bioactive and confinement properties of a bone substitute based on demineralized bone matrix (DBM). Also, structural and morphological properties were evaluated. Bone substitute was obtained from EIA Biomaterials Laboratory of EIA University and the DBM was facilitated by Tissue Bank Foundation. Morphological and structural properties were evaluated by scanning electron microscopy (SEM), X-ray diffraction (DRX) and Fourier transform infrared spectroscopy with total attenuated reflection (FTIR-ATR). Water absorption capacity and degradation were also evaluated during three months. The cytotoxicity was evaluated by the MTT test. The bioactivity of the bone substitute was evaluated through immersion of the samples in simulated body fluid during four weeks. Confinement tests were performed on tibial fragments of a human donor with bone defects of determined size, to ensure that the substitute remains in the defect despite the continuous flow of fluid. According of the knowledge of the authors, the methodology for evaluating samples in a confined environment has not been evaluated before in real human bones. The morphology of the samples showed irregular surface and presented some porosity. DRX confirmed a semi-crystalline structure. The FTIR-ATR determined the organic and inorganic phase of the sample. The degradation and absorption measurements stablished a loss of 3% and 150% in one month respectively. The MTT showed that the system is not cytotoxic. Apatite clusters formed from the first week were visualized by SEM and confirmed by EDS. These calcium phosphates are necessary to stimulate bone regeneration and thanks to the porosity of the developed material, osteinduction and osteoconduction are possible. The results of the in vitro evaluation of the confinement of the material showed that the migration of the bone filling to other sites is negligible, although the samples were subjected to the passage of simulated body fluid. The bone substitute, putty type, showed stability, is bioactive, non-cytotoxic and has handling properties for specialists at the time of implantation. The obtained system allows to maintain the osteoinductive properties of DBM and it can fill completely fractures in any way; however, it does not provide a structural support, that is, it should only be used to treat fractures without requiring a mechanical load.

Keywords: bone regeneration, cytotoxicity, demineralized bone matrix, hydrogel

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Authors: W. Thepsuwan, N. Monmaturapoj

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Calcium phosphate cements (CPCs) is one of the most attractive bioceramics due to its moldable and shape ability to fill complicated bony cavities or small dental defect positions. In this study, CPCs were produced by using mixtures of tetracalcium phosphate (TTCP, Ca4O(PO4)2) and dicalcium phosphate anhydrous (DCPA, CaHPO4) in equimolar ratio (1/1) with aqueous solutions of acetic acid (C2H4O2) and disodium hydrogen phosphate dehydrate (Na2HPO4.2H2O) in combination with sodium alginate in order to improve theirs moldable characteristic. The concentrations of the aqueous solutions and sodium alginate were varied to investigate the effects of different aqueous solution and alginate on properties of the cements. The cement paste was prepared by mixing cement powder (P) with aqueous solution (L) in a P/L ratio of 1.0 g/ 0.35 ml. X-ray diffraction (XRD) was used to analyses phase formation of the cements. Setting times and compressive strength of the set CPCs were measured using the Gilmore apparatus and Universal testing machine, respectively. The results showed that CPCs could be produced by using both basic (Na2HPO4.2H2O) and acidic (C2H4O2) solutions. XRD results show the precipitation of hydroxyapatite in all cement samples. No change in phase formation among cements using difference concentrations of Na2HPO4.2H2O solutions. With increasing concentration of acidic solutions, samples obtained less hydroxyapatite with a high dicalcium phosphate dehydrate leaded to a shorter setting time. Samples with sodium alginate exhibited higher crystallization of hydroxyapatite than that of without alginate as a result of shorten setting time in basic solution but a longer setting time in acidic solution. The stronger cement was attained from samples using acidic solution with sodium alginate; however it was lower than using the basic solution.

Keywords: calcium phosphate cements, TTCP, DCPA, hydroxyapatite, properties

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Authors: Raymond Dominic Uzoh

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Starch fillers were extracted from three plant sources namely amora tuber (a wild variety of Irish potato), sweet potato and yam starch and their particle size, pH, amylose, and amylopectin percentage decomposition determined accordingly by high performance liquid chromatography (HPLC). The starch was introduced into natural rubber in liquid phase (through gelatinization) by the latex compounding method and compounded according to standard method. The prepared starch/natural rubber composites was characterized by Instron Universal testing machine (UTM) for tensile mechanical properties. The composites was further characterized by x-ray diffraction and crosslink density analysis. The particle size determination showed that amora starch granules have the highest particle size (156 × 47 μm) followed by yam starch (155× 40 μm) and then the sweet potato starch (153 × 46 μm). The pH test also revealed that amora starch has a near neutral pH of 6.9, yam 6.8, and sweet potato 5.2 respectively. Amylose and amylopectin determination showed that yam starch has a higher percentage of amylose (29.68), followed by potato (22.34) and then amora starch with the lowest value (14.86) respectively. The tensile mechanical properties testing revealed that yam starch produced the best tensile mechanical properties followed by amora starch and then sweet potato starch. The structure, crystallinity/amorphous nature of the product composite was confirmed by x-ray diffraction, while the nature of crosslinking was confirmed by swelling test in toluene solvent using the Flory-Rehner approach. This research study has rendered a workable strategy for enhancing interfacial interaction between a hydrophilic filler (starch) and hydrophobic polymeric matrix (natural rubber) yielding moderately good tensile mechanical properties for further exploitation development and application in the rubber processing industry.

Keywords: natural rubber, fillers, starch, amylose, amylopectin, crosslink density

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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: Amir Mirzaei

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The powertrain system is one of the most basic and essential components in a machine. The occurrence of motion is practically impossible without the presence of this system. When power is generated by the engine, it is transmitted by the powertrain system to the wheels, which are the last parts of the system. Powertrain system has different components according to the type of use and design. When the force generated by the engine reaches to the wheels, the amount of frictional force between the tire and the ground determines the amount of traction and non-slip or the amount of slip. At various levels, such as icy, muddy, and snow-covered ground, the amount of friction coefficient between the tire and the ground decreases dramatically and considerably, which in turn increases the amount of force loss and the vehicle traction decreases drastically. This condition is caused by the phenomenon of slipping, which, in addition to the waste of energy produced, causes the premature wear of driving tires. It also causes the temperature of the transmission oil to rise too much, as a result, causes a reduction in the quality and become dirty to oil and also reduces the useful life of the clutches disk and plates inside the transmission. this issue is much more important in road construction and mining machinery than passenger vehicles and is always one of the most important and significant issues in the design discussion, in order to overcome. One of these methods is the automatic spin regulator system which is abbreviated as ASR. The importance of this method and its structure and function have solved one of the biggest challenges of the powertrain system in the field of construction and mining machinery. That this research is examined.

Keywords: automatic spin regulator, ASR, methods of reducing slipping, methods of preventing the reduction of the useful life of clutches disk and plate, methods of preventing the premature dirtiness of transmission oil, method of preventing the reduction of the useful life of tires

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Authors: Joshna Rani S., Ahmadi Banu

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Artificial Intelligence (AI) has a significant task to carry out in the medical care contributions of things to come. As AI, it is the essential capacity behind the advancement of accuracy medication, generally consented to be a painfully required development in care. Albeit early endeavors at giving analysis and treatment proposals have demonstrated testing, we anticipate that AI will at last dominate that area too. Given the quick propels in AI for imaging examination, it appears to be likely that most radiology, what's more, pathology pictures will be inspected eventually by a machine. Discourse and text acknowledgment are now utilized for assignments like patient correspondence and catch of clinical notes, and their utilization will increment. The best test to AI in these medical services areas isn't regardless of whether the innovations will be sufficiently skilled to be valuable, but instead guaranteeing their appropriation in day by day clinical practice. For far reaching selection to happen, AI frameworks should be affirmed by controllers, coordinated with EHR frameworks, normalized to an adequate degree that comparative items work likewise, instructed to clinicians, paid for by open or private payer associations, and refreshed over the long haul in the field. These difficulties will, at last, be survived, yet they will take any longer to do as such than it will take for the actual innovations to develop. Therefore, we hope to see restricted utilization of AI in clinical practice inside 5 years and more broad use inside 10 years. It likewise appears to be progressively evident that AI frameworks won't supplant human clinicians for a huge scope, yet rather will increase their endeavors to really focus on patients. Over the long haul, human clinicians may advance toward errands and work plans that draw on remarkably human abilities like sympathy, influence, and higher perspective mix. Maybe the lone medical services suppliers who will chance their professions over the long run might be the individuals who will not work close by AI

Keywords: artificial intellogence, health care, breast cancer, AI applications

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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: Theoni Soublis

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The COVID-19 pandemic created a severe interruption in teaching and learning in K-12 schools. It is essential that educational researchers, teachers, and administrators understand the long term effects that COVID-19 had on a variety of stakeholders in education. This investigation aims to analyze the research since the beginning of the pandemic that focuses specifically on teacher retention, resilience, and training. The results of this investigation will help to inform future research in order to better understand how the institution of education can continue to be prepared and to better prepare for future significant shifts in the modalities of instruction. The results of this analysis will directly impact the field of education as it will broaden the scope of understanding regarding how COVID- 19 impacted teaching and learning. The themes that will emerge from the data analysis will directly inform policy makers, administrators, and researchers about how to best implement training and curriculum design in order to support teacher effectiveness this in the classroom. Educational researchers have written about how teacher morale plummeted and how many teachers reported early burnout and higher stress levels. Teachers’ stress and anxiety soared during the COVID-19 pandemic, but so has their resilience and dedication to the field of education. This research aims to understand how public-school teachers overcame teaching obstacles presented to them during COVID-19. Research has been conducted to identify a variety of information regarding the impact the pandemic has had on K-12 teachers, students, and families. This research aims to understand how teachers continued to pursue their teaching objectives without significant training of effective online instruction methods. Not many educators even heard of the video conferencing platform Zoom before the spring of 2020. Researchers are interested in understanding how teachers used their expertise, prior knowledge, and training to institute immediate and effective online learning environments, what types of relationships did teachers build with students while teaching 100% remotely, and how did relationships change with students while teaching remotely? Furthermore, did the teacher-student relationship propel teacher resolve to be successful while teaching during a pandemic. Recent world events have significantly impacted the field of public-school teaching. The pandemic forced teachers to shift their paradigm about how to maintain high academic expectations, meet state curriculum standards, and assess students learning gains to make data-informed decisions while simultaneously adapting modes of instruction through multiple outlets with little to no training on remote, synchronous, asynchronous, virtual, and hybrid teaching. While it would be very interesting to study how teaching positively impacted students learning during the pandemic, I am more interested in understanding how teaches stayed the course and maintained their mental health while dealing with the stress and pressure of teaching during COVID-19.

Keywords: teacher retention, COVID-19, teacher education, teacher moral

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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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: 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: Bing–Jing Zhao, Chang-Kui Liu, Hong Wang, Min Hu

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Electron beam rapid manufacturing (EBRM) or Selective laser melting is an additive manufacturing process that uses 3D CAD data as a digital information source and energy in the form of a high-power laser beam or electron beam to create three-dimensional metal parts by fusing fine metallic powders together.Object:The present study was conducted to evaluate the mechanical properties ,the phase transformation,the corrosivity and the biocompatibility of Ti-6Al-4V by EBRM,SLM and forging technique.Method: Ti-6Al-4V alloy standard test pieces were manufactured by EBRM, SLM and forging technique according to AMS4999,GB/T228 and ISO 10993.The mechanical properties were analyzed by universal test machine. The phase transformation was analyzed by X-ray diffraction and scanning electron microscopy. The corrosivity was analyzed by electrochemical method. The biocompatibility was analyzed by co-culturing with mesenchymal stem cell and analyzed by scanning electron microscopy (SEM) and alkaline phosphatase assay (ALP) to evaluate cell adhesion and differentiation, respectively. Results: The mechanical properties, the phase transformation, the corrosivity and the biocompatibility of Ti-6Al-4V by EBRM、SLM were similar to forging and meet the mechanical property requirements of AMS4999 standard. a­phase microstructure for the EBM production contrast to the a’­phase microstructure of the SLM product. Mesenchymal stem cell adhesion and differentiation were well. Conclusion: The property of the Ti-6Al-4V alloy manufactured by EBRM and SLM technique can meet the medical standard from this study. But some further study should be proceeded in order to applying well in clinical practice.

Keywords: 3D printing, Electron Beam Rapid Manufacturing (EBRM), Selective Laser Melting (SLM), Computer Aided Design (CAD)

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Authors: B. Sudhakara Reddy

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This study provides a snapshot of the sustainability of selected Indian cities by employing 70 indicators in four dimensions to develop an overall city sustainability index. In recent years, the concept of ‘urban sustainability’ has become prominent due to its complexity. Urban areas propel growth and at the same time poses a lot of ecological, social and infrastructural problems and risks. In case of developing countries, the high population density of and the continuous in-migration run the highest risk in natural and man-made disasters. These issues combined with the inability of policy makers in providing basic services makes the cities unsustainable. To assess whether any given policy is moving towards or against urban sustainability it is necessary to consider the relationships among its various dimensions. Hence, in recent years, while preparing the sustainability index, an integral approach involving indicators of different dimensions such as ‘economic’, ‘environmental’ and 'social' is being used. It is also important for urban planners, social analysts and other related institutions to identify and understand the relationships in this complex system. The objective of the paper is to develop a city performance index (CPI) to measure and evaluate the urban regions in terms of sustainable performances. The objectives include: i) Objective assessment of a city’s performance, ii) setting achievable goals iii) prioritise relevant indicators for improvement, iv) learning from leaders, iv) assessment of the effectiveness of programmes that results in achieving high indicator values, v) Strengthening of stakeholder participation. Using the benchmark approach, a conceptual framework is developed for evaluating 25 Indian cities. We develop City Sustainability index (CSI) in order to rank cities according to their level of sustainability. The CSI is composed of four dimensions: Economic, Environment, Social, and Institutional. Each dimension is further composed of multiple indicators: (1) Economic that considers growth, access to electricity, and telephone availability; (2) environmental that includes waste water treatment, carbon emissions, (3) social that includes, equity, infant mortality, and 4) institutional that includes, voting share of population, urban regeneration policies. The CSI, consisting of four dimensions disaggregate into 12 categories and ultimately into 70 indicators. The data are obtained from public and non-governmental organizations, and also from city officials and experts. By ranking a sample of diverse cities on a set of specific dimensions the study can serve as a baseline of current conditions and a marker for referencing future results. The benchmarks and indices presented in the study provide a unique resource for the government and the city authorities to learn about the positive and negative attributes of a city and prepare plans for a sustainable urban development. As a result of our conceptual framework, the set of criteria we suggest is somewhat different to any already in the literature. The scope of our analysis is intended to be broad. Although illustrated with specific examples, it should be apparent that the principles identified are relevant to any monitoring that is used to inform decisions involving decision variables. These indicators are policy-relevant and, hence they are useful tool for decision-makers and researchers.

Keywords: benchmark, city, indicator, performance, sustainability

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Authors: Sharda Kumari, Saiman Shetty

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Autonomous trucking is just one of the numerous significant shifts impacting fleet management services. The Society of Automotive Engineers (SAE) has defined six levels of vehicle automation that have been adopted internationally, including by the United States Department of Transportation. On public highways in the United States, organizations are testing driverless vehicles with at least Level 4 automation which indicates that a human is present in the vehicle and can disable automation, which is usually done while the trucks are not engaged in highway driving. However, completely driverless vehicles are presently being tested in the state of California. While autonomous trucking can increase safety, decrease trucking costs, provide solutions to trucker shortages, and improve efficiencies, logistics, too, requires advancements to keep up with trucking innovations. Given that artificial intelligence, machine learning, and automated procedures enable people to do their duties in other sectors with fewer resources, CRM (Customer Relationship Management) can be applied to the autonomous trucking business to provide the same level of efficiency. In a society witnessing significant digital disruptions, fleet management is likewise being transformed by technology. Utilizing strategic alliances to enhance core services is an effective technique for capitalizing on innovations and delivering enhanced services. Utilizing analytics on CRM systems improves cost control of fuel strategy, fleet maintenance, driver behavior, route planning, road safety compliance, and capacity utilization. Integration of autonomous trucks with automated fleet management, yard/terminal management, and customer service is possible, thus having significant power to redraw the lines between the public and private spheres in autonomous trucking logistics.

Keywords: autonomous vehicles, customer relationship management, customer experience, autonomous trucking, digital transformation

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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: Alaa Yehia, Ayatollah Yehia, Sherif Yehia

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There is a recurring issue of low enrollments across many civil engineering departments in postsecondary institutions. While there have been moments where enrollments begin to increase, civil engineering departments find themselves facing low enrollments at around 60% over the last five years across the Middle East. There are many reasons that could be attributed to this decline, such as low entry-level salaries, over-saturation of civil engineering graduates in the job market, and a lack of construction projects due to the impending or current recession. However, this recurring problem alludes to an intrinsic issue of the curriculum. The societal shift to the usage of high technology such as machine learning (ML) and artificial intelligence (AI) demands individuals who are proficient at utilizing it. Therefore, existing curriculums must adapt to this change in order to provide an education that is suitable for potential and current students. In this paper, In order to provide potential solutions for this issue, the analysis considers two possible implementations of high technology into the civil engineering curriculum. The first approach is to implement a course that introduces applications of high technology in Civil Engineering contexts. While the other approach is to intertwine applications of high technology throughout the degree. Both approaches, however, should meet requirements of accreditation agencies. In addition to the proposed improvement in civil engineering curriculum, a different pedagogical practice must be adapted as well. The passive learning approach might not be appropriate for Gen Z students; current students, now more than ever, need to be introduced to engineering topics and practice following different learning methods to ensure they will have the necessary skills for the job market. Different learning methods that incorporate high technology applications, like AI, must be integrated throughout the curriculum to make the civil engineering degree more attractive to prospective students. Moreover, the paper provides insight on the importance and approach of adapting the Civil Engineering curriculum to address the current low enrollment crisis that civil engineering departments globally, but specifically in the Middle East, are facing.

Keywords: artificial intelligence (AI), civil engineering curriculum, high technology, low enrollment, pedagogy

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Authors: Fabienne Samyn, Pauline Tranchard, Sophie Duquesne, Emilie Goncalves, Bruno Estebe, Serge Boubigot

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Stringent fire safety regulations are enforced in the aeronautical industry due to the consequences that potential fire event on an aircraft might imply. This is so much true that the fire issue is considered right from the design of the aircraft structure. Due to the incorporation of an increasing amount of polymer matrix composites in replacement of more conventional materials like metals, the nature of the fire risks is changing. The choice of materials used is consequently of prime importance as well as the evaluation of its resistance to fire. The fire testing is mostly done using the so-called certification tests according to standards such as the ISO2685:1998(E). The latter describes a protocol to evaluate the fire resistance of structures located in fire zone (ability to withstand fire for 5min). The test consists in exposing an at least 300x300mm² sample to an 1100°C propane flame with a calibrated heat flux of 116kW/m². This type of test is time-consuming, expensive and gives access to limited information in terms of fire behavior of the materials (pass or fail test). Consequently, it can barely be used for material development purposes. In this context, the laboratory UMET in collaboration with industrial partners has developed a horizontal and a vertical small-scale instrumented fire benches for the characterization of the fire behavior of composites. The benches using smaller samples (no more than 150x150mm²) enables to cut downs costs and hence to increase sampling throughput. However, the main added value of our benches is the instrumentation used to collect useful information to understand the behavior of the materials. Indeed, measurements of the sample backside temperature are performed using IR camera in both configurations. In addition, for the vertical set up, a complete characterization of the degradation process, can be achieved via mass loss measurements and quantification of the gasses released during the tests. These benches have been used to characterize and study the fire behavior of aeronautical carbon/epoxy composites. The horizontal set up has been used in particular to study the performances and durability of protective intumescent coating on 2mm thick 2D laminates. The efficiency of this approach has been validated, and the optimized coating thickness has been determined as well as the performances after aging. Reductions of the performances after aging were attributed to the migration of some of the coating additives. The vertical set up has enabled to investigate the degradation process of composites under fire. An isotropic and a unidirectional 4mm thick laminates have been characterized using the bench and post-fire analyses. The mass loss measurements and the gas phase analyses of both composites do not present significant differences unlike the temperature profiles in the thickness of the samples. The differences have been attributed to differences of thermal conductivity as well as delamination that is much more pronounced for the isotropic composite (observed on the IR-images). This has been confirmed by X-ray microtomography. The developed benches have proven to be valuable tools to develop fire safe composites.

Keywords: aeronautical carbon/epoxy composite, durability, intumescent coating, small-scale ‘ISO 2685 like’ fire resistance test, X-ray microtomography

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Authors: A. Saha Chaudhuri, D. Sarkar

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In steel construction, there are two families of structural members. One is hot rolled steel and another is cold formed steel. Cold formed steel section includes steel sheet, strip, plate or flat bar. Cold formed steel section is manufactured in roll forming machine by press brake or bending operation. Cold formed steel (CFS), also known as Light Gauge Steel (LGS). As cold formed steel is a sustainable material, it is widely used in green building. Cold formed steel can be recycled and reused with no degradation in structural properties. Cold formed steel structures can earn credits for green building ratings such as LEED and similar programs. Cold formed steel construction satisfies international demand for better, more efficient and affordable buildings. Cold formed steel sections are used in building, car body, railway coach, various types of equipment, storage rack, grain bin, highway product, transmission tower, transmission pole, drainage facility, bridge construction etc. Various shapes of cold formed steel sections are available, such as C section, Z section, I section, T section, angle section, hat section, box section, square hollow section (SHS), rectangular hollow section (RHS), circular hollow section (CHS) etc. In building construction cold formed steel is used as eave strut, purlin, girt, stud, header, floor joist, brace, diaphragm and covering for roof, wall and floor. Cold formed steel has high strength to weight ratio and high stiffness. Cold formed steel is non shrinking and non creeping at ambient temperature, it is termite proof and rot proof. CFS is durable, dimensionally stable and non combustible material. CFS is economical in transportation and handling. At present days cold formed steel becomes a competitive building material. In this paper all these applications related present research work are described and how the CFS can be used as blast resistant structural system that is examined.

Keywords: cold form steel sections, applications, present research review, blast resistant design

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Osvaldo N. Oliveira Jr.

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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

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Authors: Magdalena Suárez-Ortega, M. Fe. Sánchez-García

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This paper focuses on the analysis of the motivations that lead people to undertake and consolidate their business. It is addressed from the framework of planned behavior theory, which recognizes the importance of the social environment and cultural values, both in the decision to undertake business and in business consolidation. Similarly, it is also based on theories of career development, which emphasize the importance of career management competencies and their connections to other vital aspects of people, including their roles within their families and other personal activities. This connects directly with the impact of entrepreneurship on the career and the professional-personal project of each individual. This study is part of the project titled Career Design and Talent Management (Ministry of Economy and Competitiveness of Spain, State Plan 2013-2016 Excellence Ref. EDU2013-45704-P). The aim of the study is to identify and describe entrepreneurial competencies and motivational profiles in a sample of 248 Spanish entrepreneurs, considering the consolidated profile and the profile in transition (n = 248).In order to obtain the information, the Questionnaire of Motivation and conditioners of the entrepreneurial career (MCEC) has been applied. This consists of 67 items and includes four scales (E1-Conflicts in conciliation, E2-Satisfaction in the career path, E3-Motivations to undertake, E4-Guidance Needs). Cluster analysis (mixed method, combining k-means clustering with a hierarchical method) was carried out, characterizing the groups profiles according to the categorical variables (chi square, p = 0.05), and the quantitative variables (ANOVA). The results have allowed us to characterize three motivational profiles relevant to the motivation, the degree of conciliation between personal and professional life, and the degree of conflict in conciliation, levels of career satisfaction and orientation needs (in the entrepreneurial project and life-career). The first profile is formed by extrinsically motivated entrepreneurs, professionally satisfied and without conflict of vital roles. The second profile acts with intrinsic motivation and also associated with family models, and although it shows satisfaction with their professional career, it finds a high conflict in their family and professional life. The third is composed of entrepreneurs with high extrinsic motivation, professional dissatisfaction and at the same time, feel the conflict in their professional life by the effect of personal roles. Ultimately, the analysis has allowed us to line the kinds of entrepreneurs to different levels of motivation, satisfaction, needs and articulation in professional and personal life, showing characterizations associated with the use of time for leisure, and the care of the family. Associations related to gender, age, activity sector, environment (rural, urban, virtual), and the use of time for domestic tasks are not identified. The model obtained and its implications for the design of training actions and orientation to entrepreneurs is also discussed.

Keywords: motivation, entrepreneurial career, guidance needs, life-work balance, job satisfaction, assessment

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Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer

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New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.

Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics

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Authors: Bagireanu Astrid, Bros-Williamson Julio, Duncheva Mila, Currie John

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Digital Twins (DT) are an innovative concept of cyber-physical integration of data between an asset and its virtual replica. They have originated in established industries such as manufacturing and aviation and have garnered increasing attention as a potentially transformative technology within the built environment. With the potential to support decision-making, real-time simulations, forecasting abilities and managing operations, DT do not fall under a singular scope. This makes defining and leveraging the potential uses of DT a potential missed opportunity. Despite its recognised potential in established industries, literature on DT in the built environment remains limited. Inadequate attention has been given to the implementation of DT in construction projects, as opposed to its operational stage applications. Additionally, the absence of a standardised definition has resulted in inconsistent interpretations of DT in both industry and academia. There is a need to consolidate research to foster a unified understanding of the DT. Such consolidation is indispensable to ensure that future research is undertaken with a solid foundation. This paper aims to present a comprehensive systematic literature review on the role of DT in the built environment. To accomplish this objective, a review and thematic analysis was conducted, encompassing relevant papers from the last five years. The identified papers are categorised based on their specific areas of focus, and the content of these papers was translated into a through classification of DT. In characterising DT and the associated data processes identified, this systematic literature review has identified 6 DT opportunities specifically relevant to the built environment: Facilitating collaborative procurement methods, Supporting net-zero and decarbonization goals, Supporting Modern Methods of Construction (MMC) and off-site manufacturing (OSM), Providing increased transparency and stakeholders collaboration, Supporting complex decision making (real-time simulations and forecasting abilities) and Seamless integration with Internet of Things (IoT), data analytics and other DT. Finally, a discussion of each area of research is provided. A table of definitions of DT across the reviewed literature is provided, seeking to delineate the current state of DT implementation in the built environment context. Gaps in knowledge are identified, as well as research challenges and opportunities for further advancements in the implementation of DT within the built environment. This paper critically assesses the existing literature to identify the potential of DT applications, aiming to harness the transformative capabilities of data in the built environment. By fostering a unified comprehension of DT, this paper contributes to advancing the effective adoption and utilisation of this technology, accelerating progress towards the realisation of smart cities, decarbonisation, and other envisioned roles for DT in the construction domain.

Keywords: built environment, design, digital twins, literature review

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Authors: Niklas Ravn-Boess, Nainita Bhowmick, Takamitsu Hattori, Shohei Koide, Christopher Park, Dimitris Placantonakis

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Background: Glioblastoma (GBM) is the most common and deadly primary brain malignancy in adults. Tumor propagation, brain invasion, and resistance to therapy critically depend on GBM stem-like cells (GSCs); however, the mechanisms that regulate GSC self-renewal are incompletely understood. Given the aggressiveness and poor prognosis of GBM, it is imperative to find biomarkers that could also translate into novel drug targets. Along these lines, we have identified a cell surface antigen, CD97 (ADGRE5), an adhesion G protein-coupled receptor (GPCR), that is expressed on GBM cells but is absent from non-neoplastic brain tissue. CD97 has been shown to promote invasiveness, angiogenesis, and migration in several human cancers, but its frequency of expression and functional role in regulating GBM growth and survival, and its potential as a therapeutic target has not been investigated. Design: We assessed CD97 mRNA and protein expression in patient derived GBM samples and cell lines using publicly available RNA-sequencing datasets and flow cytometry, respectively. To assess CD97 function, we generated shRNA lentiviral constructs that target a sequence in the CD97 extracellular domain (ECD). A scrambled shRNA (scr) with no predicted targets in the genome was used as a control. We evaluated CD97 shRNA lentivirally transduced GBM cells for Ki67, Annexin V, and DAPI. We also tested CD97 KD cells for their ability to self-renew using clonogenic tumorsphere formation assays. Further, we utilized synthetic Abs (sAbs) generated against the ECD of CD97 to test for potential antitumor effects using patient-derived GBM cell lines. Results: CD97 mRNA expression was expressed at high levels in all GBM samples available in the TCGA cohort. We found high levels of surface CD97 protein expression in 6/6 patient-derived GBM cell cultures, but not human neural stem cells. Flow cytometry confirmed downregulation of CD97 in CD97 shRNA lentivirally transduced cells. CD97 KD induced a significant reduction in cell growth in 3 independent GBM cell lines representing mesenchymal and proneural subtypes, which was accompanied by reduced (~20%) Ki67 staining and increased (~30%) apoptosis. Incubation of GBM cells with sAbs (20 ug/ ml) against the ECD of CD97 for 3 days induced GSC differentiation, as determined by the expression of GFAP and Tubulin. Using three unique GBM patient derived cultures, we found that CD97 KD attenuated the ability of GBM cells to initiate sphere formation by over 300 fold, consistent with an impairment in GSC self-renewal. Conclusion: Loss of CD97 expression in patient-derived GBM cells markedly decreases proliferation, induces cell death, and reduces tumorsphere formation. sAbs against the ECD of CD97 reduce tumorsphere formation, recapitulating the phenotype of CD97 KD, suggesting that sAbs that inhibit CD97 function exhibit anti-tumor activity. Collectively, these findings indicate that CD97 is necessary for the proliferation and survival of human GBM cells and identify CD97 as a promising therapeutically targetable vulnerability in GBM.

Keywords: adhesion GPCR, CD97, GBM stem cell, glioblastoma

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Authors: Abderrahim Elmoataz

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More and more contemporary applications involve data in the form of functions defined on irregular and topologically complicated domains (images, meshs, points clouds, networks, etc). Such data are not organized as familiar digital signals and images sampled on regular lattices. However, they can be conveniently represented as graphs where each vertex represents measured data and each edge represents a relationship (connectivity or certain affinities or interaction) between two vertices. Processing and analyzing these types of data is a major challenge for both image and machine learning communities. Hence, it is very important to transfer to graphs and networks many of the mathematical tools which were initially developed on usual Euclidean spaces and proven to be efficient for many inverse problems and applications dealing with usual image and signal domains. Historically, the main tools for the study of graphs or networks come from combinatorial and graph theory. In recent years there has been an increasing interest in the investigation of one of the major mathematical tools for signal and image analysis, which are Partial Differential Equations (PDEs) variational methods on graphs. The normalized p-laplacian operator has been recently introduced to model a stochastic game called tug-of-war-game with noise. Part interest of this class of operators arises from the fact that it includes, as particular case, the infinity Laplacian, the mean curvature operator and the traditionnal Laplacian operators which was extensiveley used to models and to solve problems in image processing. The purpose of this paper is to introduce and to study a new class of normalized p-Laplacian on graphs. The introduction is based on the extension of p-harmonious function introduced in as discrete approximation for both infinity Laplacian and p-Laplacian equations. Finally, we propose to use these operators as a framework for solving many inverse problems in image processing.

Keywords: normalized p-laplacian, image processing, stochastic game, inverse problems

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Authors: Wallyson Thomas, Zsombor Fulop, Attila Szilagyi

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Impact dampers are mainly used in the metal-mechanical industry in operations that generate too much vibration in the machining system. Internal turning processes become unstable during the machining of deep holes, in which the tool holder is used with long overhangs (high length-to-diameter ratios). The devices coupled with active dampers, are expensive and require the use of advanced electronics. On the other hand, passive impact dampers (PID – Particle Impact Dampers) are cheaper alternatives that are easier to adapt to the machine’s fixation system, once that, in this last case, a cavity filled with particles is simply added to the structure of the tool holder. The cavity dimensions and the diameter of the spheres are pre-determined. Thus, when passive dampers are employed during the machining process, the vibration is transferred from the tip of the tool to the structure of the boring bar, where it is absorbed by the fixation system. This work proposes to compare the behaviors of a conventional solid boring bar and a boring bar with a passive impact damper in turning while using the highest possible L/D (length-to-diameter ratio) of the tool and an Easy Fix fixation system (also called: Split Bushing Holding System). It is also intended to optimize the impact absorption parameters, as the filling percentage of the cavity and the diameter of the spheres. The test specimens were made of hardened material and machined in a Computer Numerical Control (CNC) lathe. The laboratory tests showed that when the cavity of the boring bar is totally filled with minimally spaced spheres of the largest diameter, the gain in absorption allowed of obtaining, with an L/D equal to 6, the same surface roughness obtained when using the solid boring bar with an L/D equal to 3.4. The use of the passive particle impact damper resulted in, therefore, increased static stiffness and reduced deflexion of the tool.

Keywords: active damper, fixation system, hardened material, passive damper

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Authors: A. Sottomayor, M. Perez Duque, M. C. Henriques

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Introduction: Migration is not a new phenomenon; nomads often traveled, seeking better living conditions, including food and water. The increase of migrations affects all countries, rising health-related challenges. In Portugal, we have had migrant movements in the last decades, pairing with economic behavior. Irregular immigrants are detained in Santo António detention center from Portuguese Immigration and Borders Service (USHA-SEF) in Porto until court decision for a maximum of 60 days. It is the only long stay officially designated detention center for immigrants in Portugal. Immigrant health is important for public health (PH). It affects and is affected by the community. The XXVII Portuguese Government considered immigrant integration, including access to health, health promotion, protection and reduction of inequities a political priority. Many curative, psychological and legal services are provided for detainees, but until 2015, no structured health promotion or prevention actions were being held at USHA-SEF. That year, Porto Occidental PH Local Unit started to provide vaccination and health literacy on this theme for detainees and SEF workers. Our activities include a vaccine lecture, a medical consultation with vaccine prescription and administration, along with documented proof of vaccination. All vaccines are volunteer and free of charge. This action reduces the risk of importation and transmission of diseases, contributing to world eradication and elimination programs. We aimed to characterize the demography of irregular immigrant detained at UHSA-SEF and describe our activity. Methods: All data was provided by Porto Occidental Public Health Unit. All paper registers of vaccination were uploaded to MicrosoftExcel®. We included all registers and collected demographic variables, nationality, vaccination date, category, and administered vaccines. Descriptive analysis was performed using MicrosoftExcel®. Results: From 2015 to 2018, we delivered care to 256 individuals (179 immigrants; 77 workers). Considering immigrants, 72% were male, and 8 (16%) women were pregnant. 85% were between 20-54 years (ᵡ=30,8y; 2-71y), and 11 didn’t report any age. Migrants came from 48 countries, and India had the highest number (9%). MMR and Tetanus vaccines had > 90% vaccination rate and Poliomyelitis, hepatitis B and flu vaccines had around 85% vaccination rates. We had a consistent number of refusals. Conclusion: Our irregular migrant population comes from many different countries, which increases the risk of disease importation. Pregnant women are present as a particular subset of irregular migrants, and vaccination protects them and the baby. Vaccination of migrant is valuable for them and for the countries in which they pass. It contributes to universal health coverage, for eradication programmes and accomplishment of the Sustainable Development Goals. Peer influence may present as a determinant of refusals so we must consistently educate migrants before vaccination. More studies would be valuable, particularly on the migrant trajectory, duration of stay, destiny after court decision and health impact.

Keywords: migrants, public health, universal health coverage, vaccination

Procedia PDF Downloads 123