Search results for: forecasting accuracy

Authors: Hoa Pham

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Code switching, which can be described as the use of students’ first language in second language classrooms, has long been a controversial topic in the area of language teaching and second language acquisition. While this has been widely investigated across different contexts, little empirical research has been undertaken in Vietnam. The findings of this study contribute to our understanding of bilingual discourse and code switching practices in content and language integrated classrooms, which has significant implications for language teaching and learning in general and in particular for language pedagogy at tertiary level in Vietnam. This study examines the accounts the teachers articulated for their code switching practices in content-based Business English in Vietnam. Data were collected from five teachers through the use of stimulated recall interviews facilitated by the video data to garner the teachers' cognitive reflection, and allowed them to vocalise the motivations behind their code switching behaviour in particular contexts. The literature has recommended that when participants are provided with a large amount of stimuli or cues, they will experience an original situation again in their imagination with great accuracy. This technique can also provide a valuable "insider" perspective on the phenomenon under investigation which complements the researcher’s "outsider" observation. This can create a relaxed atmosphere during the interview process, which in turn promotes the collection of rich and diverse data. Also, participants can be empowered by this technique as they can raise their own concerns and discuss instances which they find important or interesting. The data generated through this study were analysed using a constant comparative approach. The study found that the teachers indicated their support for the use of code switching in their pedagogical practices. Particularly, as a pedagogical resource, the teachers saw code switching to the L1 playing a key role in facilitating the students' comprehension of both content knowledge and the target language. They believed the use of the L1 accommodates the students' current language competence and content knowledge. They also expressed positive opinions about the role that code switching plays in stimulating students' schematic language and content knowledge, encouraging retention and interest in learning and promoting a positive affective environment in the classroom. The teachers perceived that their use of code switching to the L1 helps them meet the students' language needs and prepares them for their study in subsequent courses and addresses functional needs so that students can cope with English language use outside the classroom. Several factors shaped the teachers' perceptions of their code switching practices, including their accumulated teaching experience, their previous experience as language learners, their theoretical understanding of language teaching and learning, and their knowledge of the teaching context. Code switching was a typical phenomenon in the observed classes and was supported by the teachers in certain contexts. This study reinforces the call in the literature to recognise this practice as a useful instructional resource.

Keywords: codeswitching, language teaching, teacher beliefs, tertiary level

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Authors: James Gallagher, Phillip Benachour

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As location aware mobile technologies become ever more omnipresent, the prospect of exploiting their context awareness to enforce learning approaches thrives. Utilizing the growing acceptance of ubiquitous computing, and the steady progress both in accuracy and battery usage of pervasive devices, we present a case study of remote location viewing, how the application can be utilized to support mobile learning in situ using an existing scenario. Through the case study we introduce a new innovative application: Mobipeek based around a request/response protocol for the viewing of a remote location and explore how this can apply both as part of a teacher lead activity and informal learning situations. The system developed allows a user to select a point on a map, and send a request. Users can attach messages alongside time and distance constraints. Users within the bounds of the request can respond with an image, and accompanying message, providing context to the response. This application can be used alongside a structured learning activity such as the use of mobile phone cameras outdoors as part of an interactive lesson. An example of a learning activity would be to collect photos in the wild about plants, vegetation, and foliage as part of a geography or environmental science lesson. Another example could be to take photos of architectural buildings and monuments as part of an architecture course. These images can be uploaded then displayed back in the classroom for students to share their experiences and compare their findings with their peers. This can help to fosters students’ active participation while helping students to understand lessons in a more interesting and effective way. Mobipeek could augment the student learning experience by providing further interaction with other peers in a remote location. The activity can be part of a wider study between schools in different areas of the country enabling the sharing and interaction between more participants. Remote location viewing can be used to access images in a specific location. The choice of location will depend on the activity and lesson. For example architectural buildings of a specific period can be shared between two or more cities. The augmentation of the learning experience can be manifested in the different contextual and cultural influences as well as the sharing of images from different locations. In addition to the implementation of Mobipeek, we strive to analyse this application, and a subset of other possible and further solutions targeted towards making learning more engaging. Consideration is given to the benefits of such a system, privacy concerns, and feasibility of widespread usage. We also propose elements of “gamification”, in an attempt to further the engagement derived from such a tool and encourage usage. We conclude by identifying limitations, both from a technical, and a mobile learning perspective.

Keywords: context aware, location aware, mobile learning, remote viewing

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Norazlan Mohmad Misnan, Maizatul Hasyima Omar, Mohd Isa Wasiman

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Papaya (Carica papaya L., Caricaceae) is a tree which mainly cultivated for its fruits in many tropical regions including Australia, Brazil, China, Hawaii, and Malaysia. Beside of fruits, its leaves, seeds, and latex have also been traditionally used for treating diseases, which also reported to possess anti-cancer and anti- malaria properties. Its leaves have been reported to consist of various chemical compounds such as alkaloids, flavonoids and phenolics. Clitorin and manghaslin are among major flavonoids presence. Thus, the aim of this study is to quantify the purity of these isolated compounds (clitorin and manghsalin) by using quantitative Nuclear Magnetic Resonance (qNMR) analysis. Only fresh C. papaya leaves were used for juice extraction procedure and subsequently was freeze-dried to obtain a dark green powdered form of the extract prior to Centrifugal Partition Chromatography (CPC) separation. The CPC experiments were performed using a two-phase solvent system comprising ethyl acetate/butanol/water (1:4:5, v/v/v/v) solvent. The upper organic phase was used as the stationary phase, and the lower aqueous phase was employed as the mobile phase. Ten fractions were obtained after an hour runtime analysis. Fraction 6 and fraction 8 has been identified as clitorin (m/z 739.21 [M-H]-) and manghaslin (m/z 755.21 [M-H]-), respectively, based on LCMS data and full analysis of NMR (1H NMR, 13C NMR, HMBC, and HSQC). The 1H-qNMR measurements were carried out using a 400 MHz NMR spectrometer (JEOL ECS 400MHz, Japan) and deuterated methanol was used as a solvent. Quantification was performed using the AQARI method (Accurate Quantitative NMR) with deuterated 1,4-Bis(trimethylsilyl)benzene (BTMSB) as an internal reference substances. This AQARI protocol includes not only NMR measurement but also sample preparation that provide highest precision and accuracy than other qNMR methods. The 90° pulse length and the T1 relaxation times for compounds and BTMSB were determined prior to the quantification to give the best signal-to-noise ratio. Regions containing the two downfield signals from aromatic part (6.00–6.89 ppm), and the singlet signal, (18H) arising from BTMSB (0.63-1.05ppm) were selected for integration. The purity of clitorin and manghaslin were calculated to be 52.22% and 43.36%, respectively. Further purification is needed in order to increase its purity. This finding has demonstrated the use of qNMR for quality control and standardization of various plant extracts and which can be applied for NMR fingerprinting of other plant-based products with good reproducibility and in the case where commercial standards is not readily available.

Keywords: Carica papaya, clitorin, manghaslin, quantitative Nuclear Magnetic Resonance, Centrifugal Partition Chromatography

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Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

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Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O₂ concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO₂/NOx ratio etc.

Keywords: diesel engine, machine learning, NOₓ emission, semi-empirical

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Authors: Claudia Norambuena, Myissa Weiss, Maria Ruiz Maya, Matthew Straley, Elijah Hammond, Benjamin Chesebrough, David Grow

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The purpose of this project is to develop a low-cost, portable sensor device that can be used at home for long-term analysis of gait and balance abnormalities. One area of particular concern involves the asymmetries in movement and balance that can accompany certain types of injuries and/or the associated devices used in the repair and rehabilitation process (e.g. the use of splints and casts) which can often increase chances of falls and additional injuries. This device has the capacity to monitor a patient during the rehabilitation process after injury or operation, increasing the patient’s access to healthcare while decreasing the number of visits to the patient’s clinician. The sensor device may thereby improve the quality of the patient’s care, particularly in rural areas where access to the clinician could be limited, while simultaneously decreasing the overall cost associated with the patient’s care. The device consists of nine interconnected accelerometer/ gyroscope/compass chips (9-DOF IMU, Adafruit, New York, NY). The sensors attach to and are used to determine the orientation and acceleration of the patient’s lower abdomen, C7 vertebra (lower neck), L1 vertebra (middle back), anterior side of each thigh and tibia, and dorsal side of each foot. In addition, pressure sensors are embedded in shoe inserts with one sensor (ESS301, Tekscan, Boston, MA) beneath the heel and three sensors (Interlink 402, Interlink Electronics, Westlake Village, CA) beneath the metatarsal bones of each foot. These sensors measure the distribution of the weight applied to each foot as well as stride duration. A small microntroller (Arduino Mega, Arduino, Ivrea, Italy) is used to collect data from these sensors in a CSV file. MATLAB is then used to analyze the data and output the hip, knee, ankle, and trunk angles projected on the sagittal plane. An open-source program Processing is then used to generate an animation of the patient’s gait. The accuracy of the sensors was validated through comparison to goniometric measurements (±2° error). The sensor device was also shown to have sufficient sensitivity to observe various gait abnormalities. Several patients used the sensor device, and the data collected from each represented the patient’s movements. Further, the sensors were found to have the ability to observe gait abnormalities caused by the addition of a small amount of weight (4.5 - 9.1 kg) to one side of the patient. The user-friendly interface and portability of the sensor device will help to construct a bridge between patients and their clinicians with fewer necessary inpatient visits.

Keywords: biomedical sensing, gait analysis, outpatient, rehabilitation

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Authors: Sahar Heidary

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Radiography and radiotherapy have emerged as crucial pillars of modern medical practice, revolutionizing diagnostics and treatment for a myriad of health conditions. This abstract highlights the pivotal role of radiography and radiotherapy in favor of healthcare and society. Radiography, a non-invasive imaging technique, has significantly advanced medical diagnostics by enabling the visualization of internal structures and abnormalities within the human body. With the advent of digital radiography, clinicians can obtain high-resolution images promptly, leading to faster diagnoses and informed treatment decisions. Radiography plays a pivotal role in detecting fractures, tumors, infections, and various other conditions, allowing for timely interventions and improved patient outcomes. Moreover, its widespread accessibility and cost-effectiveness make it an indispensable tool in healthcare settings worldwide. On the other hand, radiotherapy, a branch of medical science that utilizes high-energy radiation, has become an integral component of cancer treatment and management. By precisely targeting and damaging cancerous cells, radiotherapy offers a potent strategy to control tumor growth and, in many cases, leads to cancer eradication. Additionally, radiotherapy is often used in combination with surgery and chemotherapy, providing a multifaceted approach to combat cancer comprehensively. The continuous advancements in radiotherapy techniques, such as intensity-modulated radiotherapy and stereotactic radiosurgery, have further improved treatment precision while minimizing damage to surrounding healthy tissues. Furthermore, radiography and radiotherapy have demonstrated their worth beyond oncology. Radiography is instrumental in guiding various medical procedures, including catheter placement, joint injections, and dental evaluations, reducing complications and enhancing procedural accuracy. On the other hand, radiotherapy finds applications in non-cancerous conditions like benign tumors, vascular malformations, and certain neurological disorders, offering therapeutic options for patients who may not benefit from traditional surgical interventions. In conclusion, radiography and radiotherapy stand as indispensable tools in modern medicine, driving transformative improvements in patient care and treatment outcomes. Their ability to diagnose, treat, and manage a wide array of medical conditions underscores their favor in medical practice. As technology continues to advance, radiography and radiotherapy will undoubtedly play an ever more significant role in shaping the future of healthcare, ultimately saving lives and enhancing the quality of life for countless individuals worldwide.

Keywords: radiology, radiotherapy, medical imaging, cancer treatment

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Authors: Ahmed Hossam ElMolla, Mohamed Hatem Saleh, Hamza Mostafa, Lara Mamdouh, Yassin Wael

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Artificial intelligence (AI) is reshaping industries, and its potential to revolutionize renewable energy and data center operations is immense. By harnessing AI's capabilities, we can optimize energy consumption, predict fluctuations in renewable energy generation, and improve the efficiency of data center infrastructure. This convergence of technologies promises a future where energy is managed more intelligently, sustainably, and cost-effectively. The integration of AI into renewable energy systems unlocks a wealth of opportunities. Machine learning algorithms can analyze vast amounts of data to forecast weather patterns, solar irradiance, and wind speeds, enabling more accurate energy production planning. AI-powered systems can optimize energy storage and grid management, ensuring a stable power supply even during intermittent renewable generation. Moreover, AI can identify maintenance needs for renewable energy infrastructure, preventing costly breakdowns and maximizing system lifespan. Data centers, which consume substantial amounts of energy, are prime candidates for AI-driven optimization. AI can analyze energy consumption patterns, identify inefficiencies, and recommend adjustments to cooling systems, server utilization, and power distribution. Predictive maintenance using AI can prevent equipment failures, reducing energy waste and downtime. Additionally, AI can optimize data placement and retrieval, minimizing energy consumption associated with data transfer. As AI transforms renewable energy and data center operations, modified Key Performance Indicators (KPIs) will emerge. Traditional metrics like energy efficiency and cost-per-megawatt-hour will continue to be relevant, but additional KPIs focused on AI's impact will be essential. These might include AI-driven cost savings, predictive accuracy of energy generation and consumption, and the reduction of carbon emissions attributed to AI-optimized operations. By tracking these KPIs, organizations can measure the success of their AI initiatives and identify areas for improvement. Ultimately, the synergy between AI, renewable energy, and data centers holds the potential to create a more sustainable and resilient future. By embracing these technologies, we can build smarter, greener, and more efficient systems that benefit both the environment and the economy.

Keywords: data center, artificial intelligence, renewable energy, energy efficiency, sustainability, optimization, predictive analytics, energy consumption, energy storage, grid management, data center optimization, key performance indicators, carbon emissions, resiliency

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Authors: Recep Kesli, Huseyin Bilgin, Yasar Unlu, Gokhan Gungor

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Aim: The aim of this study was to compare diagnostic values of two different molecular-based tests (GenoType® HelicoDR ve Seeplex® H. pylori-ClaR- ACE Detection) in detection presence of the H. pylori from gastric biopsy specimens. In addition to this also was aimed to determine resistance ratios of H. pylori strains against to clarytromycine and quinolone isolated from gastric biopsy material cultures by using both the genotypic (GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection) and phenotypic (gradient strip, E-test) methods. Material and methods: A total of 266 patients who admitted to Konya Education and Research Hospital Department of Gastroenterology with dyspeptic complaints, between January 2011-June 2013, were included in the study. Microbiological and histopathological examinations of biopsy specimens taken from antrum and corpus regions were performed. The presence of H. pylori in all the biopsy samples was investigated by five differnt dignostic methods together: culture (C) (Portagerm pylori-PORT PYL, Pylori agar-PYL, GENbox microaer, bioMerieux, France), histology (H) (Giemsa, Hematoxylin and Eosin staining), rapid urease test (RUT) (CLOtest, Cimberly-Clark, USA), and two different molecular tests; GenoType® HelicoDR, Hain, Germany, based on DNA strip assay, and Seeplex ® H. pylori -ClaR- ACE Detection, Seegene, South Korea, based on multiplex PCR. Antimicrobial resistance of H. pylori isolates against clarithromycin and levofloxacin was determined by GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, and gradient strip (E-test, bioMerieux, France) methods. Culture positivity alone or positivities of both histology and RUT together was accepted as the gold standard for H. pylori positivity. Sensitivity and specificity rates of two molecular methods used in the study were calculated by taking the two gold standards previously mentioned. Results: A total of 266 patients between 16-83 years old who 144 (54.1 %) were female, 122 (45.9 %) were male were included in the study. 144 patients were found as culture positive, and 157 were H and RUT were positive together. 179 patients were found as positive with GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection together. Sensitivity and specificity rates of studied five different methods were found as follows: C were 80.9 % and 84.4 %, H + RUT were 88.2 % and 75.4 %, GenoType® HelicoDR were 100 % and 71.3 %, and Seeplex ® H. pylori -ClaR- ACE Detection were, 100 % and 71.3 %. A strong correlation was found between C and H+RUT, C and GenoType® HelicoDR, and C and Seeplex ® H. pylori -ClaR- ACE Detection (r:0.644 and p:0.000, r:0.757 and p:0.000, r:0.757 and p:0.000, respectively). Of all the isolated 144 H. pylori strains 24 (16.6 %) were detected as resistant to claritromycine, and 18 (12.5 %) were levofloxacin. Genotypic claritromycine resistance was detected only in 15 cases with GenoType® HelicoDR, and 6 cases with Seeplex ® H. pylori -ClaR- ACE Detection. Conclusion: In our study, it was concluded that; GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection was found as the most sensitive diagnostic methods when comparing all the investigated other ones (C, H, and RUT).

Keywords: Helicobacter pylori, GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, antimicrobial resistance

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Authors: S. Moustafa El Shanawany, I. Labib Salem, F. Mohamed Magdy Badr El Dine, H. Tag El Deen Abd Allah

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Thermal injuries remain a global health problem and a common issue encountered in forensic pathology. They are a devastating cause of morbidity and mortality in children and adults especially in developing countries, causing permanent disfigurement, scarring and grievous hurt. Burns have always been a matter of legal concern in cases of suicidal burns, self-inflicted burns for false accusation and homicidal attempts. Assessment of burn injuries as well as rating permanent disabilities and disfigurement following thermal injuries for the benefit of compensation claims represents a challenging problem. This necessitates the development of reliable scoring systems to yield an expected likelihood of permanent disability or fatal outcome following burn injuries. The study was designed to identify the risk factors of mortality in acute burn patients and to evaluate the applicability of FLAMES (Fatality by Longevity, APACHE II score, Measured Extent of burn, and Sex) and BOBI (Belgian Outcome in Burn Injury) model scores in predicting the outcome. The study was conducted on 100 adult patients with acute burn injuries admitted to the Burn Unit of Alexandria Main University Hospital, Egypt from October 2014 to October 2015. Victims were examined after obtaining informed consent and the data were collected in specially designed sheets including demographic data, burn details and any associated inhalation injury. Each burn patient was assessed using both BOBI and FLAMES scoring systems. The results of the study show the mean age of patients was 35.54±12.32 years. Males outnumbered females (55% and 45%, respectively). Most patients were accidently burnt (95%), whereas suicidal burns accounted for the remaining 5%. Flame burn was recorded in 82% of cases. As well, 8% of patients sustained more than 60% of total burn surface area (TBSA) burns, 19% of patients needed mechanical ventilation, and 19% of burnt patients died either from wound sepsis, multi-organ failure or pulmonary embolism. The mean length of hospital stay was 24.91±25.08 days. The mean BOBI score was 1.07±1.27 and that of the FLAMES score was -4.76±2.92. The FLAMES score demonstrated an area under the receiver operating characteristic (ROC) curve of 0.95 which was significantly higher than that of the BOBI score (0.883). A statistically significant association was revealed between both predictive models and the outcome. The study concluded that both scoring systems were beneficial in predicting mortality in acutely burnt patients. However, the FLAMES score could be applied with a higher level of accuracy.

Keywords: BOBI, burns, FLAMES, scoring systems, outcome

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Authors: Haidy Weliam Megaly Gouda

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Dermatology is often a neglected specialty in low-resource settings despite the high morbidity associated with skin disease. This becomes even more significant when associated with HIV infection, as dermatological conditions are more common and aggressive in HIV-positive patients. African countries have the highest HIV infection rates, and skin conditions are frequently misdiagnosed and mismanaged because of a lack of dermatological training and educational material. The frequent lack of diagnostic tests in the African setting renders basic clinical skills all the more vital. This project aimed to improve the diagnosis and treatment of skin disease in the HIV population in a district hospital in Malawi. A basic dermatological clinical tool was developed and produced in collaboration with local staff and based on available literature and data collected from clinics. The aim was to improve diagnostic accuracy and provide guidance for the treatment of skin disease in HIV-positive patients. A literature search within Embassy, Medline and Google Scholar was performed and supplemented through data obtained from attending 5 Antiretroviral clinics. From the literature, conditions were selected for inclusion in the resource if they were described as specific, more prevalent, or extensive in the HIV population or have more adverse outcomes if they develop in HIV patients. Resource-appropriate treatment options were decided using Malawian Ministry of Health guidelines and textbooks specific to African dermatology. After the collection of data and discussion with local clinical and pharmacy staff, a list of 15 skin conditions was included, and a booklet was created using the simple layout of a picture, a diagnostic description of the disease and treatment options. Clinical photographs were collected from local clinics (with full consent of the patient) or from the book ‘Common Skin Diseases in Africa’ (permission granted if fully acknowledged and used in a not-for-profit capacity). This tool was evaluated by the local staff alongside an educational teaching session on skin disease. This project aimed to reduce uncertainty in diagnosis and provide guidance for appropriate treatment in HIV patients by gathering information into one practical and manageable resource. To further this project, we hope to review the effectiveness of the tool in practice.

Keywords: prevalence and pattern of skin diseases, impact on quality of life, rural Nepal, interventions, quality switched ruby laser, skin color river blindness, clinical signs, circularity index, grey level run length matrix, grey level co-occurrence matrix, local binary pattern, object detection, ring detection, shape identification

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Authors: Indika Fernando, Jiangang Fei, Roger Stanely, Hossein Enshaei

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Ever increasing demand for quality fresh produce by the consumers, had increased the gravity on the post-harvest supply chains in multi-fold in the recent years. Mechanical injury to fresh produce was a critical factor for produce wastage, especially with the expansion of supply chains, physically extending to thousands of miles. The impact of vibration damages in transit was identified as a specific area of focus which results in wastage of significant portion of the fresh produce, at times ranging from 10% to 40% in some countries. Several studies were concentrated on quantifying the impact of vibration to fresh produce, and it was a challenge to collect vibration impact data continuously due to the limitations in battery life or the memory capacity in the devices. Therefore, the study samples were limited to a stretch of the transit passage or a limited time of the journey. This may or may not give an accurate understanding of the vibration impacts encountered throughout the transit passage, which limits the accuracy of the results. Consequently, an approach which can extend the capacity and ability of determining vibration signals in the transit passage would contribute to accurately analyze the vibration damage along the post-harvest supply chain. A mechanism was developed to address this challenge, which is capable of measuring the in transit vibration continuously through the transit passage subject to a minimum acceleration threshold (0.1g). A system, consisting six tri-axel vibration transducers installed in different locations inside the cargo (produce) pallets in the truck, transmits vibration signals through LORA (Long Range Radio) technology to a central device installed inside the container. The central device processes and records the vibration signals transmitted by the portable transducers, along with the GPS location. This method enables to utilize power consumption for the portable transducers to maximize the capability of measuring the vibration impacts in the transit passage extending to days in the distribution process. The trial tests conducted using the approach reveals that it is a reliable method to measure and quantify the in transit vibrations along the supply chain. The GPS capability enables to identify the locations in the supply chain where the significant vibration impacts were encountered. This method contributes to determining the causes, susceptibility and intensity of vibration impact damages to fresh produce in the post-harvest supply chain. Extensively, the approach could be used to determine the vibration impacts not limiting to fresh produce, but for products in supply chains, which may extend from few hours to several days in transit.

Keywords: post-harvest, supply chain, wireless transducers, LORA, fresh produce

Procedia PDF Downloads 265

Authors: Lívia Pinheiro Carvalho, Luciana Di Thommazo-Luporini, Rafael Luís Luporini, José Carlos Bonjorno Junior, Renata Pedrolongo Basso Vanelli, Manoel Carneiro de Oliveira Junior, Rodolfo de Paula Vieira, Renata Trimer, Renata G. Mendes, Mylène Aubertin-Leheudre, Audrey Borghi-Silva

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Rationale: Cardiorespiratory fitness (CRF) and functional capacity in young obese and normal-weight people are associated with metabolic and cardiovascular diseases and mortality. However, it remains unclear whether their metabolically healthy (MH) or at risk (AR) phenotype influences cardiorespiratory fitness in this vulnerable population such as obese adults but also in normal-weight people. HOMA insulin resistance index (HI) and leptin-adiponectin ratio (LA) are strong markers for characterizing those phenotypes that we hypothesized to be associated with physical fitness. We also hypothesized that an easy and feasible exercise test could identify a subpopulation at risk to develop metabolic and related disorders. Methods: Thirty-nine sedentary men and women (20-45y; 18.530 kg.m-2) underwent a clinical evaluation, including the six-minute step test (ST), a well-validated and reliable test for young people. Body composition assessment was done by a tetrapolar bioimpedance in a fasting state and in the folicular phase for women. A maximal cardiopulmonary exercise testing, as well as the ST, evaluated the oxygen uptake at the peak of the test (VO2peak) by an ergospirometer Oxycon Mobile. Lipids, glucose, insulin were analysed and the ELISA method quantified the serum leptin and adiponectin from blood samples. Volunteers were divided in two groups: AR or MH according to a HI cutoff of 1.95, which was previously determined in the literature. T-test for comparison between groups, Pearson´s test to correlate main variables and ROC analysis for discriminating AR from up-and-down cycles in ST (SC) were applied (p<0.05). Results: Higher LA, fat mass (FM) and lower HDL, SC, leg lean mass (LM) and VO2peak were found in AR than in MH. Significant correlations were found between VO2peak and SC (r= 0.80) as well as between LA and FM (r=0.87), VO2peak (r=-0.73), and SC (r=-0.65). Area under de curve showed moderate accuracy (0.75) of SC <173 to discriminate AR phenotype. Conclusion: Our study found that at risk obese and normal-weight subjects showed an unhealthy metabolism as well as a poor CRF and functional daily activity capacity. Additionally, a simple and less costly functional test associated with above-mentioned aspects is able to identify ‘at risk’ subjects for primary intervention with important clinical and health implications.

Keywords: aerobic capacity, exercise, fitness, metabolism, obesity, 6MST

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Authors: Jurriaan Gillissen

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This work is concerned with stabilizing the numerical integration of the Navier-Stokes equation (NSE), backwards in time. Applications involve the detection of sources of, e.g., sound, heat, and pollutants. Stable reverse numerical integration of parabolic differential equations is also relevant for image de-blurring. While the literature addresses the reverse integration problem of the advection-diffusion equation, the problem of numerical reverse integration of the NSE has, to our knowledge, not yet been addressed. Owing to the presence of viscosity, the NSE is irreversible, i.e., when going backwards in time, the fluid behaves, as if it had a negative viscosity. As an effect, perturbations from the perfect solution, due to round off errors or discretization errors, grow exponentially in time, and reverse integration of the NSE is inherently unstable, regardless of using an implicit time integration scheme. Consequently, some sort of filtering is required, in order to achieve a stable, numerical, reversed integration. The challenge is to find a filter with a minimal adverse affect on the accuracy of the reversed integration. In the present work, we explore an adjoint gradient method (AGM) to achieve this goal, and we apply this technique to two-dimensional (2D), decaying turbulence. The AGM solves for the initial velocity field u0 at t = 0, that, when integrated forward in time, produces a final velocity field u1 at t = 1, that is as close as is feasibly possible to some specified target field v1. The initial field u0 defines a minimum of a cost-functional J, that measures the distance between u1 and v1. In the minimization procedure, the u0 is updated iteratively along the gradient of J w.r.t. u0, where the gradient is obtained by transporting J backwards in time from t = 1 to t = 0, using the adjoint NSE. The AGM thus effectively replaces the backward integration by multiple forward and backward adjoint integrations. Since the viscosity is negative in the adjoint NSE, each step of the AGM is numerically stable. Nevertheless, when applied to turbulence, the AGM develops instabilities, which limit the backward integration to small times. This is due to the exponential divergence of phase space trajectories in turbulent flow, which produces a multitude of local minima in J, when the integration time is large. As an effect, the AGM may select unphysical, noisy initial conditions. In order to improve this situation, we propose two remedies. First, we replace the integration by a sequence of smaller integrations, i.e., we divide the integration time into segments, where in each segment the target field v1 is taken as the initial field u0 from the previous segment. Second, we add an additional term (regularizer) to J, which is proportional to a high-order Laplacian of u0, and which dampens the gradients of u0. We show that suitable values for the segment size and for the regularizer, allow a stable reverse integration of 2D decaying turbulence, with accurate results for more then O(10) turbulent, integral time scales.

Keywords: time reversed integration, parabolic differential equations, adjoint gradient method, two dimensional turbulence

Procedia PDF Downloads 224

Authors: Adhimoolam Saravana Kumar, K. N. Govindarajan, B. Devanand, R. Rajakumar

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The quality of Computed Tomography (CT) procedures has improved in recent years due to technological developments and increased diagnostic ability of CT scanners. Due to the fact that CT doses are the peak among diagnostic radiology practices, it is of great significance to be aware of patient’s CT radiation dose whenever a CT examination is preferred. CT radiation dose delivered to patients in the form of volume CT dose index (CTDIvol) values, is displayed on scanner monitors at the end of each examination and it is an important fact to assure that this information is accurate. The objective of this study was to estimate the CTDIvol values for great number of patients during the most frequent CT examinations, to study the comparison between CT dose monitor values and measured ones, as well as to highlight the fluctuation of CTDIvol values for the same CT examination at different centres and scanner models. The output CT dose indices measurements were carried out on single and multislice scanners for available kV, 5 mm slice thickness, 100 mA and FOV combination used. The 100 CT scanners were involved in this study. Data with regard to 15,000 examinations in patients, who underwent routine head, chest and abdomen CT were collected using a questionnaire sent to a large number of hospitals. Out of the 15,000 examinations, 5000 were head CT examinations, 5000 were chest CT examinations and 5000 were abdominal CT examinations. Comprehensive quality assurance (QA) was performed for all the machines involved in this work. Followed by QA, CT phantom dose measurements were carried out in South India using actual scanning parameters used clinically by the hospitals. From this study, we have measured the mean divergence between the measured and displayed CTDIvol values were 5.2, 8.4, and -5.7 for selected head, chest and abdomen procedures for protocols as mentioned above, respectively. Thus, this investigation revealed an observable change in CT practices, with a much wider range of studies being performed currently in South India. This reflects the improved capacity of CT scanners to scan longer scan lengths and at finer resolutions as permitted by helical and multislice technology. Also, some of the CT scanners have used smaller slice thickness for routine CT procedures to achieve better resolution and image quality. It leads to an increase in the patient radiation dose as well as the measured CTDIv, so it is suggested that such CT scanners should select appropriate slice thickness and scanning parameters in order to reduce the patient dose. If these routine scan parameters for head, chest and abdomen procedures are optimized than the dose indices would be optimal and lead to the lowering of the CT doses. In South Indian region all the CT machines were routinely tested for QA once in a year as per AERB requirements.

Keywords: CT dose index, weighted CTDI, volumetric CTDI, radiation dose

Procedia PDF Downloads 257

Authors: Nico Fuchs, Fabian Wüllhorst, Laura Maier, Dirk Müller

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The decarbonization of building stocks necessitates the modernization of existing buildings. Key measures for this include reducing energy demands through insulation of the building envelope, replacing heat generators, and installing solar systems. Given limited financial resources, it is impractical to modernize all buildings in a portfolio simultaneously; instead, prioritization of buildings and modernization measures for a given planning horizon is essential. Optimization models for modernization pathways can assist portfolio managers in this prioritization. However, modeling and solving these large-scale optimization problems, often represented as mixed-integer problems (MIP), necessitates simplifying the operation of building energy systems particularly with respect to system dynamics and transient behavior. This raises the question of which level of simplification remains sufficient to accurately account for realistic costs and emissions of building energy systems, ensuring a fair comparison of different modernization measures. This study addresses this issue by comparing a two-stage simulation-based optimization approach with a single-stage mathematical optimization in a mixed-integer linear programming (MILP) formulation. The simulation-based approach serves as a benchmark for realistic energy system operation but requires a restriction of the solution space to discrete choices of modernization measures, such as the sizing of heating systems. After calculating the operation of different energy systems in terms of the resulting final energy demands in simulation models on a first stage, the results serve as input for a second stage MILP optimization, where the design of each building in the portfolio is optimized. In contrast to the simulation-based approach, the MILP-based approach can capture a broader variety of modernization measures due to the efficiency of MILP solvers but necessitates simplifying the building energy system operation. Both approaches are employed to determine the cost-optimal design and dimensioning of several buildings in a portfolio to meet climate targets within limited yearly budgets, resulting in a modernization pathway for the entire portfolio. The comparison reveals that the MILP formulation successfully captures design decisions of building energy systems, such as the selection of heating systems and the modernization of building envelopes. However, the results regarding the optimal dimensioning of heating technologies differ from the results of the two-stage simulation-based approach, as the MILP model tends to overestimate operational efficiency, highlighting the limitations of the MILP approach.

Keywords: building energy system optimization, model accuracy in optimization, modernization pathways, building stock decarbonization

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Authors: Raphael Palucci Rosa, Giuseppe Rosace

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Stereolithography (SLA) is one of the 3D-printing technologies that has been steadily growing in popularity for both industrial and personal applications due to its versatility, high accuracy, and low cost. Its printing process consists of using a light emitter to solidify photosensitive liquid resins layer-by-layer to produce solid objects. However, the majority of the resins used in SLA are derived from petroleum and characterized by toxicity, stability, and recalcitrance to degradation in natural environments. Aiming to develop an eco-friendly resin, in this work, different combinations of a standard commercial SLA resin (Peopoly UV professional) with a vegetable-based resin were investigated. To reach this goal, different mass concentrations (varying from 10 to 50 wt%) of acrylated epoxidized soybean oil (AESO), a vegetable resin produced from soyabean oil, were mixed with a commercial acrylate-based resin. 1.0 wt% of Diphenyl(2,4,6-trimethylbenzoyl) phosphine oxide (TPO) was used as photo-initiator, and the samples were printed using a Peopoly moai 130. The machine was set to operate at standard configurations when printing commercial resins. After the print was finished, the excess resin was drained off, and the samples were washed in isopropanol and water to remove any non-reacted resin. Finally, the samples were post-cured for 30 min in a UV chamber. FT-IR analysis was used to confirm the UV polymerization of the formulated resin with different AESO/Peopoly ratios. The signals from 1643.7 to 1616, which corresponds to the C=C stretching of the AESO acrylic acids and Peopoly acrylic groups, significantly decreases after the reaction. The signal decrease indicates the consumption of the double bonds during the radical polymerization. Furthermore, the slight change of the C-O-C signal from 1186.1 to 1159.9 decrease of the signals at 809.5 and 983.1, which corresponds to unsaturated double bonds, are both proofs of the successful polymerization. Mechanical analyses showed a decrease of 50.44% on tensile strength when adding 10 wt% of AESO, but it was still in the same range as other commercial resins. The elongation of break increased by 24% with 10 wt% of AESO and swelling analysis showed that samples with a higher concentration of AESO mixed absorbed less water than their counterparts. Furthermore, high-resolution prototypes were printed using both resins, and visual analysis did not show any significant difference between both products. In conclusion, the AESO resin was successful incorporated into a commercial resin without affecting its printability. The bio-based resin showed lower tensile strength than the Peopoly resin due to network loosening, but it was still in the range of other commercial resins. The hybrid resin also showed better flexibility and water resistance than Peopoly resin without affecting its resolution. Finally, the development of new types of SLA resins is essential to provide new sustainable alternatives to the commercial petroleum-based ones.

Keywords: 3D-printing, bio-based, resin, soybean, stereolithography

Procedia PDF Downloads 128

Authors: Ioanna Taouki, Marie Lallier, David Soto

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Metacognition refers to the capacity to reflect upon our own cognitive processes. Although there is an ongoing discussion in the literature on the role of metacognition in learning and academic achievement, little is known about its neurodevelopmental trajectories in early childhood, when children begin to receive formal education in reading. Here, we evaluate the metacognitive ability, estimated under a recently developed Signal Detection Theory model, of a cohort of children aged between 6 and 7 (N=60), who performed three two-alternative-forced-choice tasks (two linguistic: lexical decision task, visual attention span task, and one non-linguistic: emotion recognition task) including trial-by-trial confidence judgements. Our study has three aims. First, we investigated how metacognitive ability (i.e., how confidence ratings track accuracy in the task) relates to performance in general standardized tasks related to students' reading and general cognitive abilities using Spearman's and Bayesian correlation analysis. Second, we assessed whether or not young children recruit common mechanisms supporting metacognition across the different task domains or whether there is evidence for domain-specific metacognition at this early stage of development. This was done by examining correlations in metacognitive measures across different task domains and evaluating cross-task covariance by applying a hierarchical Bayesian model. Third, using robust linear regression and Bayesian regression models, we assessed whether metacognitive ability in this early stage is related to the longitudinal learning of children in a linguistic and a non-linguistic task. Notably, we did not observe any association between students’ reading skills and metacognitive processing in this early stage of reading acquisition. Some evidence consistent with domain-general metacognition was found, with significant positive correlations between metacognitive efficiency between lexical and emotion recognition tasks and substantial covariance indicated by the Bayesian model. However, no reliable correlations were found between metacognitive performance in the visual attention span and the remaining tasks. Remarkably, metacognitive ability significantly predicted children's learning in linguistic and non-linguistic domains a year later. These results suggest that metacognitive skill may be dissociated to some extent from general (i.e., language and attention) abilities and further stress the importance of creating educational programs that foster students’ metacognitive ability as a tool for long term learning. More research is crucial to understand whether these programs can enhance metacognitive ability as a transferable skill across distinct domains or whether unique domains should be targeted separately.

Keywords: confidence ratings, development, metacognitive efficiency, reading acquisition

Procedia PDF Downloads 150

Authors: Dana R. Thomson, Catherine Linard, Sabine Vanhuysse, Jessica E. Steele, Michal Shimoni, Jose Siri, Waleska Caiaffa, Megumi Rosenberg, Eleonore Wolff, Tais Grippa, Stefanos Georganos, Helen Elsey

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The Sustainable Development Goals (SDGs) and Urban Health Equity Assessment and Response Tool (Urban HEART) identify dozens of key indicators to help local decision-makers prioritize and track inequalities in health outcomes. However, presentations and discussions at the International Conference on Urban Health (ICUH) 2017 suggested that additional indicators are needed to make decisions and policies. A local decision-maker may realize that malaria or road accidents are a top priority. However, s/he needs additional health determinant indicators, for example about standing water or traffic, to address the priority and reduce inequalities. Health determinants reflect the physical and social environments that influence health outcomes often at community- and societal-levels and include such indicators as access to quality health facilities, access to safe parks, traffic density, location of slum areas, air pollution, social exclusion, and social networks. Indicator identification and disaggregation are necessarily constrained by available datasets – typically collected about households and individuals in surveys, censuses, and administrative records. Continued advancements in earth observation, data storage, computing and mobile technologies mean that new sources of health determinants indicators derived from 'big data' are becoming available at fine geographic scale. Big data includes high-resolution satellite imagery and aggregated, anonymized mobile phone data. While big data are themselves not representative of the population (e.g., satellite images depict the physical environment), they can provide information about population density, wealth, mobility, and social environments with tremendous detail and accuracy when combined with population-representative survey, census, administrative and health system data. The aim of this paper is to (1) flag to data scientists important indicators needed by health decision-makers at the city and sub-city scale - ideally free and publicly available, and (2) summarize for local decision-makers new datasets that can be generated from big data, with layperson descriptions of difficulties in generating them. We include SDGs and Urban HEART indicators, as well as indicators mentioned by decision-makers attending ICUH 2017.

Keywords: health determinant, health outcome, mobile phone, remote sensing, satellite imagery, SDG, urban HEART

Procedia PDF Downloads 210

Authors: Abdallah Touaibia, Nachida Kasbadji Merzouk, Mustapha Merzouk, Ryma Belarbi

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accuracy assessment and a better understand of the wind resource distribution are the most important tasks for decision making before installing wind energy operating systems in a given region, there where our interest come to the Algerian coastline and its Mediterranean sea area. Despite its large coastline overlooking the border of Mediterranean Sea, there is still no strategy encouraging the development of offshore wind farms in Algerian waters. The present work aims to estimate the offshore wind fields for the Algerian Mediterranean Sea based on wind data measurements ranging from 1995 to 2018 provided of 24 years of measurement by seven observation stations focusing on three coastline cities in Algeria under a different measurement time step recorded from 30 min, 60 min, and 180 min variate from one to each other, two stations in Spain, two other ones in Italy and three in the coast of Algeria from the east Annaba, at the center Algiers, and to Oran taken place at the west of it. The idea behind consists to have multiple measurement points that helping to characterize this area in terms of wind potential by the use of interpolation method of their average wind speed values between these available data to achieve the approximate values of others locations where aren’t any available measurement because of the difficulties against the implementation of masts within the deep depth water. This study is organized as follow: first, a brief description of the studied area and its climatic characteristics were done. After that, the statistical properties of the recorded data were checked by evaluating wind histograms, direction roses, and average speeds using MatLab programs. Finally, ArcGIS and MapInfo soft-wares were used to establish offshore wind maps for better understanding the wind resource distribution, as well as to identify windy sites for wind farm installation and power management. The study pointed out that Cap Carbonara is the windiest site with an average wind speed of 7.26 m/s at 10 m, inducing a power density of 902 W/m², then the site of Cap Caccia with 4.88 m/s inducing a power density of 282 W/m². The average wind speed of 4.83 m/s is occurred for the site of Oran, inducing a power density of 230 W/m². The results indicated also that the dominant wind direction where the frequencies are highest for the site of Cap Carbonara is the West with 34%, an average wind speed of 9.49 m/s, and a power density of 1722 W/m². Then comes the site of Cap Caccia, where the prevailing wind direction is the North-west, about 20% and 5.82 m/s occurring a power density of 452 W/m². The site of Oran comes in third place with the North dominant direction with 32% inducing an average wind speed of 4.59 m/s and power density of 189 W/m². It also shown that the proposed method is either crucial in understanding wind resource distribution for revealing windy sites over a large area and more effective for wind turbines micro-siting.

Keywords: wind ressources, mediterranean sea, offshore, arcGIS, mapInfo, wind maps, wind farms

Procedia PDF Downloads 147

Authors: Mustapha Rilwan Adewale, Salau Ayobami Muhammed

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This study focuses on the heat transfer analysis of magneto-hydrodynamics (MHD) squeezing flow between parallel disks, considering a viscous incompressible fluid. The upper disk exhibits both upward and downward motion, while the lower disk remains stationary but permeable. By employing similarity transformations, a system of nonlinear ordinary differential equations is derived to describe the flow behavior. To solve this system, a numerical approach, namely the Chebyshev collocation method, is utilized. The study investigates the influence of flow parameters and compares the obtained results with existing literature. The significance of this research lies in understanding the heat transfer characteristics of MHD squeezing flow, which has practical implications in various engineering and industrial applications. By employing the similarity transformations, the complex governing equations are simplified into a system of nonlinear ordinary differential equations, facilitating the analysis of the flow behavior. To obtain numerical solutions for the system, the Chebyshev collocation method is implemented. This approach provides accurate approximations for the nonlinear equations, enabling efficient computations of the heat transfer properties. The obtained results are compared with existing literature, establishing the validity and consistency of the numerical approach. The study's major findings shed light on the influence of flow parameters on the heat transfer characteristics of the squeezing flow. The analysis reveals the impact of parameters such as magnetic field strength, disk motion amplitude, fluid viscosity on the heat transfer rate between the disks, the squeeze number(S), suction/injection parameter(A), Hartman number(M), Prandtl number(Pr), modified Eckert number(Ec), and the dimensionless length(δ). These findings contribute to a comprehensive understanding of the system's behavior and provide insights for optimizing heat transfer processes in similar configurations. In conclusion, this study presents a thorough heat transfer analysis of magneto-hydrodynamics squeezing flow between parallel disks. The numerical solutions obtained through the Chebyshev collocation method demonstrate the feasibility and accuracy of the approach. The investigation of flow parameters highlights their influence on heat transfer, contributing to the existing knowledge in this field. The agreement of the results with previous literature further strengthens the reliability of the findings. These outcomes have practical implications for engineering applications and pave the way for further research in related areas.

Keywords: squeezing flow, magneto-hydro-dynamics (MHD), chebyshev collocation method(CCA), parallel manifolds, finite difference method (FDM)

Procedia PDF Downloads 76

Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

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The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

Procedia PDF Downloads 92

Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

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Authors: Mona Shojaei

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The Sub-Atmospheric Vapor Pipeline (SAVP) introduces a distinct approach to seawater desalination with promising applications in both land and industrial sectors. SAVP systems exploit the temperature difference between a hot source and a cold environment to facilitate efficient vapor transfer, offering substantial benefits in diverse industrial and field applications. This approach incorporates dynamic boundary conditions, where the temperatures of hot and cold sources vary over time, particularly in natural and industrial environments. Such variations critically influence convection and diffusion processes, introducing challenges that require the refinement of the convection-diffusion equation and the derivation of temperature profiles along the pipeline through advanced engineering mathematics. This study formulates vapor temperature as a function of time and length using two mathematical approaches: Eigen functions and Green’s equation. Combining detailed theoretical modeling, mathematical simulations, and extensive field and industrial tests, this research underscores the SAVP system’s scalability for real-world applications. Results reveal a high degree of accuracy, highlighting SAVP’s significant potential for energy conservation and environmental sustainability. Furthermore, the integration of SAVP technology within smart and green building systems creates new opportunities for sustainable urban water management. By capturing and repurposing vapor for non-potable uses such as irrigation, greywater recycling, and ecosystem support in green spaces, SAVP aligns with the principles of smart and green buildings. Smart buildings emphasize efficient resource management, enhanced system control, and automation for optimal energy and water use, while green buildings prioritize environmental impact reduction and resource conservation. SAVP technology bridges both paradigms, enhancing water self-sufficiency and reducing reliance on external water supplies. The sustainable and energy-efficient properties of SAVP make it a vital component in resilient infrastructure development, addressing urban water scarcity while promoting eco-friendly living. This dual alignment with smart and green building goals positions SAVP as a transformative solution in the pursuit of sustainable urban resource management.

Keywords: sub-atmospheric vapor pipeline, seawater desalination, energy efficiency, vapor transfer dynamics, mathematical modeling, sustainable water solutions, smart buildings

Procedia PDF Downloads 14

Authors: Kathrina Aseanne Acapulco-Gomez, Shayne Julieane Morales, Tzar Francis Verame

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Mean platelet volume (MPV) is the most accurate measure of the size of platelets and is routinely measured by most automated hematological analyzers. Several studies have shown associations between MPV and cardiovascular risks and outcomes. Although its measurement may provide useful data, MPV remains to be a diagnostic tool that is yet to be included in routine clinical decision making. The aim of this systematic review and meta-analysis is to determine summary estimates of the diagnostic accuracy of mean platelet volume for the diagnosis of myocardial infarction among adult patients with angina and/or its equivalents in terms of sensitivity, specificity, diagnostic odds ratio, and likelihood ratios, and to determine the difference of the mean MPV values between those with MI and those in the non-MI controls. The primary search was done through search in electronic databases PubMed, Cochrane Review CENTRAL, HERDIN (Health Research and Development Information Network), Google Scholar, Philippine Journal of Pathology, and Philippine College of Physicians Philippine Journal of Internal Medicine. The reference list of original reports was also searched. Cross-sectional, cohort, and case-control articles studying the diagnostic performance of mean platelet volume in the diagnosis of acute myocardial infarction in adult patients were included in the study. Studies were included if: (1) CBC was taken upon presentation to the ER or upon admission (within 24 hours of symptom onset); (2) myocardial infarction was diagnosed with serum markers, ECG, or according to accepted guidelines by the Cardiology societies (American Heart Association (AHA), American College of Cardiology (ACC), European Society of Cardiology (ESC); and, (3) if outcomes were measured as significant difference AND/OR sensitivity and specificity. The authors independently screened for inclusion of all the identified potential studies as a result of the search. Eligible studies were appraised using well-defined criteria. Any disagreement between the reviewers was resolved through discussion and consensus. The overall mean MPV value of those with MI (9.702 fl; 95% CI 9.07 – 10.33) was higher than in those of the non-MI control group (8.85 fl; 95% CI 8.23 – 9.46). Interpretation of the calculated t-value of 2.0827 showed that there was a significant difference in the mean MPV values of those with MI and those of the non-MI controls. The summary sensitivity (Se) and specificity (Sp) for MPV were 0.66 (95% CI; 0.59 - 0.73) and 0.60 (95% CI; 0.43 – 0.75), respectively. The pooled diagnostic odds ratio (DOR) was 2.92 (95% CI; 1.90 – 4.50). The positive likelihood ratio of MPV in the diagnosis of myocardial infarction was 1.65 (95% CI; 1.20 – 22.27), and the negative likelihood ratio was 0.56 (95% CI; 0.50 – 0.64). The intended role for MPV in the diagnostic pathway of myocardial infarction would perhaps be best as a triage tool. With a DOR of 2.92, MPV values can discriminate between those who have MI and those without. For a patient with angina presenting with elevated MPV values, it is 1.65 times more likely that he has MI. Thus, it is implied that the decision to treat a patient with angina or its equivalents as a case of MI could be supported by an elevated MPV value.

Keywords: mean platelet volume, MPV, myocardial infarction, angina, chest pain

Procedia PDF Downloads 87

Authors: Najibullah M, Soumendra Nath Kuiry

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Dams and reservoirs are perceived for their estimable alms to irrigation, water supply, flood control, electricity generation, etc. which civilize the prosperity and wealth of society across the world. Meantime the dam breach could cause devastating flood that can threat to the human lives and properties. Failures of large dams remain fortunately very seldom events. Nevertheless, a number of occurrences have been recorded in the world, corresponding in an average to one to two failures worldwide every year. Some of those accidents have caused catastrophic consequences. So it is decisive to predict the dam break flow for emergency planning and preparedness, as it poses high risk to life and property. To mitigate the adverse impact of dam break, modeling is necessary to gain a good understanding of the temporal and spatial evolution of the dam-break floods. This study will mainly deal with one-dimensional (1D) dam break modeling. Less commonly used in the hydraulic research community, another possible option for modeling the rapidly varied dam-break flows is the extended Boussinesq equations (BEs), which can describe the dynamics of short waves with a reasonable accuracy. Unlike the Shallow Water Equations (SWEs), the BEs taken into account the wave dispersion and non-hydrostatic pressure distribution. To capture the dam-break oscillations accurately it is very much needed of at least fourth-order accurate numerical scheme to discretize the third-order dispersion terms present in the extended BEs. The scope of this work is therefore to develop an 1D fourth-order accurate in both space and time Boussinesq model for dam-break flow analysis by using finite-volume / finite difference scheme. The spatial discretization of the flux and dispersion terms achieved through a combination of finite-volume and finite difference approximations. The flux term, was solved using a finite-volume discretization whereas the bed source and dispersion term, were discretized using centered finite-difference scheme. Time integration achieved in two stages, namely the third-order Adams Basforth predictor stage and the fourth-order Adams Moulton corrector stage. Implementation of the 1D Boussinesq model done using PYTHON 2.7.5. Evaluation of the performance of the developed model predicted as compared with the volume of fluid (VOF) based commercial model ANSYS-CFX. The developed model is used to analyze the risk of cascading dam failures similar to the Panshet dam failure in 1961 that took place in Pune, India. Nevertheless, this model can be used to predict wave overtopping accurately compared to shallow water models for designing coastal protection structures.

Keywords: Boussinesq equation, Coastal protection, Dam-break flow, One-dimensional model

Procedia PDF Downloads 232

Authors: Ali Ashtiani, Hamid Shirazi

Abstract:

This paper reviews the status of existing practice and research related to measuring pavement cracking and using crack density as a pavement surface evaluation protocol. Crack density for pavement evaluation is currently not widely used within the airport community and its use by the highway community is limited. However, surface cracking is a distress that is closely monitored by airport staff and significantly influences the development of maintenance, rehabilitation and reconstruction plans for airport pavements. Therefore crack density has the potential to become an important indicator of pavement condition if the type, severity and extent of surface cracking can be accurately measured. A pavement distress survey is an essential component of any pavement assessment. Manual crack surveying has been widely used for decades to measure pavement performance. However, the accuracy and precision of manual surveys can vary depending upon the surveyor and performing surveys may disrupt normal operations. Given the variability of manual surveys, this method has shown inconsistencies in distress classification and measurement. This can potentially impact the planning for pavement maintenance, rehabilitation and reconstruction and the associated funding strategies. A substantial effort has been devoted for the past 20 years to reduce the human intervention and the error associated with it by moving toward automated distress collection methods. The automated methods refer to the systems that identify, classify and quantify pavement distresses through processes that require no or very minimal human intervention. This principally involves the use of a digital recognition software to analyze and characterize pavement distresses. The lack of established protocols for measurement and classification of pavement cracks captured using digital images is a challenge to developing a reliable automated system for distress assessment. Variations in types and severity of distresses, different pavement surface textures and colors and presence of pavement joints and edges all complicate automated image processing and crack measurement and classification. This paper summarizes the commercially available systems and technologies for automated pavement distress evaluation. A comprehensive automated pavement distress survey involves collection, interpretation, and processing of the surface images to identify the type, quantity and severity of the surface distresses. The outputs can be used to quantitatively calculate the crack density. The systems for automated distress survey using digital images reviewed in this paper can assist the airport industry in the development of a pavement evaluation protocol based on crack density. Analysis of automated distress survey data can lead to a crack density index. This index can be used as a means of assessing pavement condition and to predict pavement performance. This can be used by airport owners to determine the type of pavement maintenance and rehabilitation in a more consistent way.

Keywords: airport pavement management, crack density, pavement evaluation, pavement management

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Authors: Melese Wondatir

Abstract:

Flooding ranks among the most destructive natural disasters, impacting millions of individuals globally and resulting in substantial economic, social, and environmental repercussions. This study's objective was to create a comprehensive model that assesses the Nile River basin's susceptibility to flood damage and improves existing flood risk management strategies. Authorities responsible for enacting policies and implementing measures may benefit from this research to acquire essential information about the flood, including its scope and susceptible areas. The identification of severe flood damage locations and efficient mitigation techniques were made possible by the use of geospatial data. Slope, elevation, distance from the river, drainage density, topographic witness index, rainfall intensity, distance from road, NDVI, soil type, and land use type were all used throughout the study to determine the vulnerability of flood damage. Ranking elements according to their significance in predicting flood damage risk was done using the Analytic Hierarchy Process (AHP) and geospatial approaches. The analysis finds that the most important parameters determining the region's vulnerability are distance from the river, topographic witness index, rainfall, and elevation, respectively. The consistency ratio (CR) value obtained in this case is 0.000866 (<0.1), which signifies the acceptance of the derived weights. Furthermore, 10.84m2, 83331.14m2, 476987.15m2, 24247.29m2, and 15.83m2 of the region show varying degrees of vulnerability to flooding—very low, low, medium, high, and very high, respectively. Due to their close proximity to the river, the northern-western regions of the Nile River basin—especially those that are close to Sudanese cities like Khartoum—are more vulnerable to flood damage, according to the research findings. Furthermore, the AUC ROC curve demonstrates that the categorized vulnerability map achieves an accuracy rate of 91.0% based on 117 sample points. By putting into practice strategies to address the topographic witness index, rainfall patterns, elevation fluctuations, and distance from the river, vulnerable settlements in the area can be protected, and the impact of future flood occurrences can be greatly reduced. Furthermore, the research findings highlight the urgent requirement for infrastructure development and effective flood management strategies in the northern and western regions of the Nile River basin, particularly in proximity to major towns such as Khartoum. Overall, the study recommends prioritizing high-risk locations and developing a complete flood risk management plan based on the vulnerability map.

Keywords: analytic hierarchy process, Blue Nile Basin, geospatial techniques, flood vulnerability, multi-criteria decision making

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

Abstract:

Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

Abstract:

To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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