Search results for: deep drawing

Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Neşe Işık, Şenol Altıok, Galip Devrim Eryılmaz, Aydın durukan, Hasan Özgür Daş

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In this study, an active landslide zone affecting the road alignment on the Tortum-Uzundere (Erzurum/Turkey) highway was investigated. Due to the landslide movement, problems have occurred in the existing road pavement, which has caused both safety problems and reduced driving comfort in the operation of the road. In order to model the landslide, drilling, geophysical and inclinometer studies were carried out in the field within the scope of ground investigation. Laboratory tests were carried out on soil and rock samples obtained from the borings. When the drilling and geophysical studies were evaluated together, it was determined that the study area has a complex geological structure. In addition, according to the inclinometer results, the direction and speed of movement of the landslide mass were observed. In order to create an idealized geological profile, all field and laboratory studies were evaluated together and then the sliding surface of the landslide was determined by back analysis method. According to the findings obtained, it was determined that the landslide was massively large, and the movement occurred had a deep sliding surface. As a result of the numerical analyses, it was concluded that the Slope angle reduction is the most economical and environmentally friendly method for the control of the landslide mass.

Keywords: landslide, geotechnical methods, geophysics, monitoring, highway

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Authors: Hamidreza Torabi, Mohsen Moghtaderi

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Introduction: Currently, deep vitrectomy with silicone oil tamponade is the standard treatment method for patients with Rhegmatogenous Retinal Detachment (RRD). After retinal repair, it is necessary to remove silicone oil from the eye, but the appropriate time to remove the oil and complications related to that time has been less studied. The aim of this study was to compare the results of the early removal of silicone oil with the delayed removal of silicone oil in patients with RRD. Method & material: Patients who were referred to the Ophthalmology Clinic of Baqiyatallah Hospital, Tehran, Iran, due to RRD with detached macula in 2021 & 2022 were evaluated. These patients were treated with deep vitrectomy and silicone oil tamponade. Patients whose retinas were attached after the passage of time were candidates for silicone oil removal (SOR) surgery. For patients in the early SOR group, SOR surgery was performed 3-6 months after the initial vitrectomy surgery, and for the late SOR group, SOR was performed after 6 months after the initial vitrectomy surgery. Results: In this study, 60 patients with RRD were evaluated. 23 (38.3%) patients were in the early group, and 37 (61.7%) patients were in the late group. Based on our findings, it was seen that the mean visual acuity of patients based on the Snellen chart in the early group (0.48 ± 0.23 Decimal) was better than the late group (0.33 ± 0.18 Decimal) (P-value=0.009). Retinal re-detachment has happened only in one patient with early SOR. Conclusion: Early removal of silicone oil (less than 6 months) from the eyes of patients undergoing RRD surgery has been associated with better vision results compared to late removal.

Keywords: retinal detachment, vitrectomy, silicone oil, silicone oil removal, visual acuity

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Authors: Ali E. Abbas

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This paper summarizes some of the discussions that occurred in a workshop in West Virginia, U.S.A which was sponsored by the National Science Foundation (NSF) in February 2016. The goal of the workshop was to explore the opportunities and challenges for applying systems engineering in large enterprises, and some of the issues that still persist. The main topics of the discussion included challenges with elaboration and abstraction in large systems, interfacing physical and social systems, and the need for axiomatic frameworks for large enterprises. We summarize these main points of discussion drawing parallels with decision making in organizations to instigate research in these discussion areas.

Keywords: decision analysis, systems engineering, framing, value creation

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Authors: Jean Marriz M. Manzano, Marc D. Rosales, Magdaleno R. Vasquez Jr., Maria Theresa G. De Leon

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This paper discusses the effects of surface roughness on a cantilever beam vibrational energy harvester. A silicon sample was fabricated using MEMS fabrication processes. When etching silicon using deep reactive ion etching (DRIE) at large etch depths, rougher surfaces are observed as a result of increased response in process pressure, amount of coil power and increased helium backside cooling readings. To account for the effects of surface roughness on the characteristics of the cantilever beam, finite element method (FEM) modeling was performed using actual roughness data from fabricated samples. It was found that when etching about 550um of silicon, root mean square roughness parameter, Sq, varies by 1 to 3 um (at 100um thick) across a 6-inch wafer. Given this Sq variation, FEM simulations predict an 8 to148 Hz shift in the resonant frequency while having no significant effect on the output power. The significant shift in the resonant frequency implies that careful consideration of surface roughness from fabrication processes must be done when designing energy harvesters.

Keywords: deep reactive ion etching, finite element method, microelectromechanical systems, multiphysics analysis, surface roughness, vibrational energy harvester

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Feng Yang

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Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: Junia Braga, Antonio carlos Martins, Marcos Racilan

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Drawing on the ecological approach, this paper reports a qualitative study that aims to understand how mobile technologies were integrated during the pandemic in the context of language teaching and the use of these technologies in post-pandemic times. Seventy-six teachers answered a questionnaire about their experiences. The findings show how the network with peers scaffolded this experience and played a crucial role in their appropriation of those technologies. They also suggest that this network may have contributed to the normalisation of digital technology use.

Keywords: ecological perspective, language teaching, mobile technologies, teacher education

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: M. Al Huwayz, A. Salhi, S. Alhassan, S. Alotaibi, A. Almalki, M.Almunyif, A. Alhassni, M. Henini

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Recently, there has been much research carried out to investigate quantum dots (QDs) lasers with the aim to increase the gain of quantum well lasers. However, one of the difficulties with these structures is that electrically active defects can lead to serious issues in the performance of these devices. It is therefore essential to fully understand the types of defects introduced during the growth and/or the fabrication process. In this study, the effects of Gamma radiation on the electrically active defects in p-i-n InAs/InGaAsQDs laser structures grown by Molecular Beam Epitaxy (MBE) technique on GaAs substrates were investigated. Deep Level Transient Spectroscopy (DLTS), current-voltage (I-V), and capacitance-voltage (C-V) measurements were performed to explore these effects on the electrical properties of these QDs lasers. I-V measurements showed that as-grown sample had better electrical properties than the irradiated sample. However, DLTS and Laplace DLTS measurements at different reverse biases revealed that the defects in the-region of the p-i-n structures were decreased in the irradiated sample. In both samples, a trap with an activation energy of ~ 0.21 eV was assigned to the well-known defect M1 in GaAs layers

Keywords: quantum dots laser structures, gamma radiation, DLTS, defects, nAs/IngaAs

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Authors: Harri Jalonen

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Social media has changed the ways we communicate, collaborate and connect with each other. It has also influenced our habits of consuming sports. Social media has allowed direct interaction between sponsoring companies, athletes/players and fans. Drawing on the service dominant logic of value co-creation, the conceptual paper identifies three operant resources which are beneficial for value co-creation: i) social identity and sense of community, ii) congruence and brand personality, and iii) participatory culture and fan activation. The paper contributes to the theoretical discussion on how social can be media used for value co-creation purposes in the sports industry.

Keywords: sports, value co-creation, social media, service

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Authors: Zeinab Jafari A., Ali Sharifnezhad B., Mohammad Razi C., Mohammad Haghpanahi D., Arash Maghsoudi

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Background and Objective: Sports-related rupture of the anterior cruciate ligament (ACL) and following injuries have been associated with various disorders, such as long-lasting changes in muscle activation patterns in athletes, which might last after ACL reconstruction (ACLR). The rupture of the ACL might result in abnormal patterns of movement execution, extending the treatment period and delaying athletes’ return to sports (RTS). As ACL injury is especially prevalent among athletes, the lengthy treatment process and athletes’ absence from sports are of great concern to athletes and coaches. Thus, estimating safe time of RTS is of crucial importance. Therefore, using a deep neural network (DNN) to classify the health levels of ACL in injured athletes, this study aimed to estimate the safe time for athletes to return to competitions. Methods: Ten athletes with ACLR and fourteen healthy controls participated in this study. Three health levels of ACL were defined: healthy, six-month post-ACLR surgery and nine-month post-ACLR surgery. Athletes with ACLR were tested six and nine months after the ACLR surgery. During the course of this study, surface electromyography (sEMG) signals were recorded from five knee muscles, namely Rectus Femoris (RF), Vastus Lateralis (VL), Vastus Medialis (VM), Biceps Femoris (BF), Semitendinosus (ST), during single-leg drop landing (SLDL) and forward hopping (SLFH) tasks. The Pseudo-Wigner-Ville distribution (PWVD) was used to produce three-dimensional (3-D) images of the energy distribution patterns of sEMG signals. Then, these 3-D images were converted to two-dimensional (2-D) images implementing the heat mapping technique, which were then fed to a deep convolutional neural network (DCNN). Results: In this study, we estimated the safe time of RTS by designing a DCNN classifier with an accuracy of 90 %, which could classify ACL into three health levels. Discussion: The findings of this study demonstrate the potential of the DCNN classification technique using sEMG signals in estimating RTS time, which will assist in evaluating the recovery process of ACLR in athletes.

Keywords: anterior cruciate ligament reconstruction, return to sports, surface electromyography, deep convolutional neural network

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Jingting Luo, Yong Wang, Ying Wang

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: Fazli Idris, Jihad Mohammad

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The study aims to determine the impact of technology and service operations flexibility, which is divided into external flexibility and internal robustness, on operations costs. A mediation model is proposed that links technology to operations costs via operation flexibility. Drawing on a sample of 475 of operations managers of various service sectors in Malaysia and South Africa, Structural Equation Modeling (SEM) was employed to test the relationship using Smart-PLS procedures. It was found that a significant relationship was established between technologies to operations costs via both operations flexibility dimensions. Theoretical and managerial implications are offered to explain the results.

Keywords: Operations flexibility, technology, costs, mediation

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Bashar Tarawneh, Yasser Hakam

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Rapid Impact Compaction (RIC) is a modern dynamic compaction device mainly used to compact sandy soils, where silt and clay contents are low. The device uses the piling hammer technology to increase the bearing capacity of soils through controlled impacts. The RIC device uses "controlled impact compaction" of the ground using a 9-ton hammer dropped from the height between 0.3 m to 1.2 m onto a 1.5 m diameter steel patent foot. The delivered energy is about 26,487 to 105,948 Joules per drop. To evaluate the performance of this technique, three project sites in the United Arab Emirates were improved using RIC. In those sites, a loose to very loose fine to medium sand was encountered at a depth ranging from 1.0m to 4.0m below the ground level. To evaluate the performance of the RIC, Cone Penetration Tests (CPT) were carried out before and after improvement. Also, load tests were carried out post-RIC work to assess the settlements and bearing capacity. The soil was improved to a depth of about 5.0m below the ground level depending on the CPT friction ratio (the ratio between sleeve friction and tip resistance). CPT tip resistance was significantly increased post ground improvement work. Load tests showed enhancement in the soil bearing capacity and reduction in the potential settlements. This study demonstrates the successful application of the RIC for middle-deep improvement and compaction of the ground. Foundation design criteria were achieved in all site post-RIC work.

Keywords: compaction, RIC, ground improvement, CPT

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Authors: Lin Li, Farshad Amini

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The flipped classroom has been introduced to promote collaborative learning and higher-order learning objectives. In contrast to the traditional classroom, the flipped classroom has students watch prerecorded lecture videos before coming to class and then “class becomes the place to work through problems, advance concepts, and engage in collaborative learning”. In this paper, the active flipped learning combines flipped classroom with active learning that is to establish an active flipped learning (AFL) model, aiming to promote active learning, stress deep learning, encourage student engagement and highlight data-driven personalized learning. Because students have watched the lecture prior to class, contact hours can be devoted to problem-solving and gain a deeper understanding of the subject matter. The instructor is able to provide students with a wide range of learner-centered opportunities in class for greater mentoring and collaboration, increasing the possibility to engage students. Currently, little is known about the extent to which AFL improves engineering students’ performance. This paper presents the preliminary study on the core course of sophomore students in Engineering Mechanics. A series of survey and interviews have been conducted to compare students’ learning engagement, empowerment, self-efficacy, and satisfaction with the AFL. It was found that the AFL model taking advantage of advanced technology is a convenient and professional avenue for engineering students to strengthen their academic confidence and self-efficacy in the Engineering Mechanics by actively participating in learning and fostering their deep understanding of engineering statics and dynamics

Keywords: active learning, engineering mechanics, flipped classroom, performance

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Authors: A. U. Ugwu

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Post-Apartheid South African education system had witnessed waves of curriculum reforms. Accordingly, there have been evidences of responsiveness towards local and global challenges of sustainable development over the past decade. In other words, the curriculum shows sensitivity towards issues of Sustainable Development (SD). Moreover, the paradigm of Sustainable Development Goals (SDGs) was introduced by the UNESCO in year 2015. The SDGs paradigm is essentially a vision towards actualizing sustainability in all aspects of the global society. Education for Sustainable Development (ESD) in retrospect entails teaching and learning to actualize the intended UNESCO 2030 SDGs. This paper explores how teaching and learning of ESD can be improved, by drawing from local context of the South African schooling system. Preservice natural sciences and technology teachers in their 2nd to 4th years of study at a university’s college of education in South Africa were contacted as participants of the study. Using qualitative case study research design, the study drew from the views and experiences of five (5) purposively selected participants from a broader study, aiming to closely understating how ESD is implemented pedagogically in teaching and learning. The inquiry employed questionnaires and a focus group discussion as qualitative data generation tools. A qualitative data analysis of generated data was carried out using content and thematic analysis, underpinned by interpretive paradigm. The result of analyzed data, suggests that ESD pedagogy at the location where this research was conducted is largely influenced by contextual factors. Furthermore, the result of the study shows that there is a critical need to employ/adopt local experiences or occurrences while teaching sustainable development. Certain pedagogical approaches such as the use of videos relative to local context should also be considered in order to achieve a more realistic application. The paper recommends that educational institutions through teaching and learning should implement ESD by drawing on local contexts and problems, thereby foregrounding constructivism, appreciating and fostering students' prior knowledge and lived experiences.

Keywords: context, education for sustainable development, natural sciences and technology preservice teachers, qualitative research, sustainable development goals

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Authors: Sohaib Khaliq

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This paper pursues a systematic analysis of the broad range of theories and studies relevant to Islam and democracy, in general and as they have been developed from and applied to the Indonesian and Pakistani cases. The analysis finds that an Islamic society’s potential to assimilate democratic political institutions is contingent on either an unconstrained 'political participation' or its ability to 'reinterpret' religious text. Drawing on a comparison of Indonesia and Pakistan, the present study favors a route that passes through the religious gates of theoretical reinterpretation. In doing so, the study brings Muslim reformation theory into focus by clarifying the mechanism by which reformation takes place.

Keywords: Islam, democratization, political Islam, reformation

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Authors: Kishore Kumar R., Kaustav Sengupta, Shalini Sood Sehgal, Poornima Santhanam

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Fashion is a human expression that is constantly changing. One of the prime factors that consistently influences fashion is the change in colour preferences. The role of colour in our everyday lives is very significant. It subconsciously explains a lot about one’s mindset and mood. Analyzing the colours by extracting them from the outfit images is a critical study to examine the individual’s/consumer behaviour. Several research works have been carried out on extracting colours from images, but to the best of our knowledge, there were no studies that extract colours to specific apparel and identify colour patterns geographically. This paper proposes a framework for accurately extracting colours from T-shirt images and predicting dominant colours geographically. The proposed method consists of two stages: first, a U-Net deep learning model is adopted to segment the T-shirts from the images. Second, the colours are extracted only from the T-shirt segments. The proposed method employs the iMaterialist (Fashion) 2019 dataset for the semantic segmentation task. The proposed framework also includes a mechanism for gathering data and analyzing India’s general colour preferences. From this research, it was observed that black and grey are the dominant colour in different regions of India. The proposed method can be adapted to study fashion’s evolving colour preferences.

Keywords: colour analysis in t-shirts, convolutional neural network, encoder-decoder, k-means clustering, semantic segmentation, U-Net model

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Authors: Bheki R. Mngomezulu

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The present paper cogently and systematically traces the history of Umkhonto Wesizwe (MK) and identifies its important role in shaping South Africa’s post-apartheid foreign policy and international relations under black leadership. It provides the political and historical contexts within which we can interpret and better understand South Africa’s controversial ‘Quiet Diplomacy’ approach to Zimbabwe’s endemic political and economic crises, which have dragged for too long. On 16 December 1961, the African National Congress (ANC) officially launched the MK as its military wing. The main aim was to train liberation fighters outside South Africa who would return into the country to topple the apartheid regime. Subsequently, the ANC established links with various countries across Africa and the globe in order to solicit arms, financial resources and military training for its recruits into the MK. Drawing from archival research and empirical data obtained through oral interviews that were conducted with some of the former MK cadres, this paper demonstrates how the ANC forged relations with a number of countries that were like-minded in order to ensure that its dream of removing the apartheid government became a reality. The findings reveal that South Africa’s foreign policy posture and international relations after the demise of apartheid in 1994 built on these relations. As such, even former and current socialist countries that were frowned upon by the Western world became post-apartheid South Africa’s international partners. These include countries such as Cuba and China, among others. Even countries that were not recognized by the Western world as independent states received good reception in post-apartheid South Africa’s foreign policy agenda. One of these countries is Palestine. Within Africa, countries with questionable human rights records such as Nigeria and Zimbabwe were accommodated in South Africa’s foreign policy agenda after 1994. Drawing from this history, the paper concludes that it would be difficult to fully understand and appreciate South Africa’s foreign policy direction and international relations after 1994 without bringing the history and the politics of the MK into the equation. Therefore, the paper proposes that the utilitarian role of history should never be undermined in the analysis of a country’s foreign policy direction and international relations. Umkhonto Wesizwe and South Africa are used as examples to demonstrate how such a link could be drawn through archival and empirical evidence.

Keywords: African National Congress, apartheid, foreign policy, international relations

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Authors: Oday A. Al-Juhaishi, Sa`ad M. Ismail, Hung-Hsun Yen, Christina M. Murray, Helen M. S. Davies

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The functional anatomy of the fascia in the greyhound is still poorly understood, and incompletely described. The basic knowledge of fascia stems mainly from anatomical, histological and ultrastructural analyses. In this study, twelve specimens of hindlimbs from six fresh greyhound cadavers (3 male, 3 female) were used to examine the topographical relationships of the superficial fascia with the skin and underlying tissue. The first incision was made along the dorsal midline from the level of the thoracolumbar junction caudally to the level of the mid sacrum. The second incision was begun at the level of the first incision and extended along the midline of the lateral aspect of the hindlimb distally, to just proximal to the tarsus, and, the skin margins carefully separated to observe connective tissue links between the skin and superficial fascia, attachment points of the fascia and the relationships of the fascia with blood vessels that supply the skin. A digital camera was used to record the anatomical features as they were revealed. The dissections identified fibrous septa connecting the skin with the superficial fascia and deep fascia in specific areas. The presence of the adipose tissue was found to be very rare within the superficial fascia in these specimens. On the extensor aspects of some joints, a fusion between the superficial fascia and deep fascia was observed. This fusion created a subcutaneous bursa in the following areas: a prepatellar bursa of the stifle, a tarsal bursa caudal to the calcaneus bone, and an ischiatic bursa caudal to the ischiatic tuberosity. The evaluation of blood vessels showed that the perforating vessels passed through fibrous septa in a perpendicular direction to supply the skin, with the largest branch noted in the gluteal area. The attachment points between the superficial fascia and skin were mainly found in the region of the flexor aspect of the joints, such as caudal to the stifle joint. The numerous fibrous septa between the superficial fascia and skin that have been identified in some areas, may create support for the blood vessels that penetrate fascia and into the skin, while allowing for movement between the tissue planes. The subcutaneous bursae between the skin and the superficial fascia where it is fused with the deep fascia may be useful to decrease friction between moving areas. The adhesion points may be related to the integrity and loading of the skin. The attachment points fix the skin and appear to divide the hindlimb into anatomical compartments.

Keywords: attachment points, fibrous septa, greyhound, subcutaneous bursa, superficial fascia

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Authors: Hansraj Riteesh Bookun, Cassandra Monique Hidajat

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This pilot study investigated the efficacy of a newly designed shoe which will act as an intermittent pneumatic compression device to augment venous flow in the lower limb. The aim was to assess the degree with which a wearable intermittent compression device can increase the venous flow in the popliteal vein. Background: Deep venous thrombosis and chronic venous insufficiency are relatively common problems with significant morbidity and mortality. While mechanical and chemical thromboprophylaxis measures are in place in hospital environments (in the form of TED stockings, intermittent pneumatic compression devices, analgesia, antiplatelet and anticoagulant agents), there are limited options in a community setting. Additionally, many individuals are poorly tolerant of graduated compression stockings due to the difficulty in putting them on, their constant tightness and increased associated discomfort in warm weather. These factors may hinder the management of their chronic venous insufficiency. Method: The device is lightweight, easy to wear and comfortable, with a self-contained power source. It features a Bluetooth transmitter and can be controlled with a smartphone. It is externally almost indistinguishable from a normal shoe. During activation, two bladders are inflated -one overlying the metatarsal heads and the second at the pedal arch. The resulting cyclical increase in pressure squeezes blood into the deep venous system. This will decrease periods of stasis and potentially reduce the risk of deep venous thrombosis. The shoe was fitted to 2 healthy participants and the peak systolic velocity of flow in the popliteal vein was measured during and prior to intermittent compression phases. Assessments of total flow volume were also performed. All haemodynamic assessments were performed with ultrasound by a licensed sonographer. Results: Mean peak systolic velocity of 3.5 cm/s with standard deviation of 1.3 cm/s were obtained. There was a three fold increase in mean peak systolic velocity and five fold increase in total flow volume. Conclusion: The device augments venous flow in the leg significantly. This may contribute to lowered thromboembolic risk during periods of prolonged travel or immobility. This device may also serve as an adjunct in the treatment of chronic venous insufficiency. The study will be replicated on a larger scale in a multi—centre trial.

Keywords: venous, intermittent compression, shoe, wearable device

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Authors: Severino Gervacio, Velimor Almonte, Emmanuel Natalio

Abstract:

A simple graph is planar if it there is a way of drawing it in the plane without edge crossings. A planar graph which is not a proper spanning subgraph of another planar graph is a maximal planar graph. We prove that for complete tripartite graphs of order at most 9, the only ones that contain a spanning maximal planar subgraph are K1,1,1, K2,2,2, K2,3,3, and K3,3,3. The main result gives a necessary and sufficient condition for the complete tripartite graph Kx,y,z to contain a spanning maximal planar subgraph.

Keywords: complete tripartite graph, graph, maximal planar graph, planar graph, subgraph

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Authors: Tom O'Mahony

Abstract:

This article considers the design of a student-centered learning environment in engineering education. The learning environment integrates a number of components, including project-based learning, collaborative learning, two-stage assignments, active learning lectures, and a flipped-classroom. Together these elements place the individual learner and their learning at the center of the environment by focusing on understanding, enhancing relevance, applying learning, obtaining rich feedback, making choices, and taking responsibility. The evolution of this environment from 2014 to the present day is outlined. The impact of this environment on learners and their learning is evaluated via student questionnaires that consist of both open and closed-ended questions. The closed questions indicate that students found the learning environment to be really interesting and enjoyable (rated as 4.7 on a 5 point scale) and encouraged students to adopt a deep approach towards studying the course materials (rated as 4.0 on a 5 point scale). A content analysis of the open-ended questions provides evidence that the project, active learning lectures, and flipped classroom all contribute to the success of this environment. Furthermore, this analysis indicates that the two-stage assessment process, in which feedback is provided between a draft and final assignment, is the key component and the dominant theme. A limitation of the study is the small class size (less than 20 learners per year), but, to some degree, this is compensated for by the longitudinal nature of the study.

Keywords: deep approaches, formative assessment, project-based learning, student-centered learning

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Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

Abstract:

Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

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Authors: Neritan Shkodrani, Klearta Rrushi, Anxhela Shaha

Abstract:

Generally, the design of axial load capacity of deep foundations is based on the data provided from field tests, such as SPT (Standard Penetration Test) and CPT (Cone Penetration Test) tests. This paper reports the results of axial load capacity analysis of drilled shafts at a construction site at Semani, in Fier county, Fier prefecture in Albania. In this case, the axial load capacity analyses are based on the data of 416 SPT tests and 12 CPTU tests, which are carried out in this site construction using 12 boreholes (10 borings of a depth 30.0 m and 2 borings of a depth of 80.0m). The considered foundation widths range from 0.5m to 2.5 m and foundation embedment lengths is fixed at a value of 25m. SPT – based analytical methods from the Japanese practice of design (Building Standard Law of Japan) and CPT – based analytical Eslami and Fellenius methods are used for obtaining axial ultimate load capacity of drilled shafts. The considered drilled shaft (25m long and 0.5m - 2.5m in diameter) is analyzed for the soil conditions of each borehole. The values obtained from sets of calculations are shown in different charts. Then the reported axial load capacity values acquired from SPT and CPTU data are compared and some conclusions are found related to the mentioned methods of calculations.

Keywords: deep foundations, drilled shafts, axial load capacity, ultimate load capacity, allowable load capacity, SPT test, CPTU test

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Authors: Chandu Rathnayake, Isuri Anuradha

Abstract:

Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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