Search results for: factor models

Authors: James Jin, Weisi Xia, Runzhe Gao, Alain Vandal, Darren Svirkis, Andrew Hill

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Background: Post-haemorrhoidectomy pain is concerned by patients/clinicians. Minimizing the postoperation pain is highly interested clinically. Combinations of topical cream targeting three hypothesised post-haemorrhoidectomy pain mechanisms were developed and their effectiveness were evaluated. Specifically, a multi-centred double-blinded randomized clinical trial (RCT) was conducted in adults undergoing excisional haemorrhoidectomy. The primary analysis was conveyed on the data collected to evaluate the effectiveness of the combinations of topical cream targeting three hypothesized pain mechanisms after the operations. Methods: 192 patients were randomly allocated to 4 arms (each arm has 48 patients), and each arm was provided with pain cream 10% metronidazole (M), M and 2% diltiazem (MD), M with 4% lidocaine (ML), or MDL, respectively. Patients were instructed to apply topical treatments three times a day for 7 days, and record outcomes for 14 days after the operations. The primary outcome was VAS pain on day 4. Covariates and models were selected in the blind review stage. Multiple imputations were applied for the missingness. LMER, GLMER models together with natural splines were applied. Sandwich estimators and Wald statistics were used. P-values < 0.05 were considered as significant. Conclusions: The addition of topical lidocaine or diltiazem to metronidazole does not add any benefit. ML had significantly better pain and recovery scores than combination MDL. Multimodal topical analgesia with ML after haemorrhoidectomy could be considered for further evaluation. Further trials considering only 3 arms (M, ML, MD) might be worth exploring.

Keywords: RCT, primary analysis, multiple imputation, pain scores, haemorrhoidectomy, analgesia, lmer

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Authors: Julian Sandoval, Philipp Schober

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Lahar sand (lahars) is a material that originates from volcanic debris flows. During and after a volcano eruption, the lahars can move at speeds up to 22 meters per hour or more, so they can easily cover extensive areas and destroy any structure in their path. Mount Pinatubo eruption (1991) brought lahars to its vicinities, and its use has been a matter of research ever since. Lahars are often disposed of for land reclamation projects in the Manila Bay, Philippines. After reclamation, some deep loss deposits may still present and they are prone to liquefaction. To mitigate the risk of liquefaction of such deposits, Vibro compaction has been proposed and used as a ground improvement technique. Cone penetration testing (CPT) campaigns are usually initiated to monitor the effectiveness of the ground improvement works by vibro compaction. The CPT cone resistance is used to analyses the in-situ relative density of the reclaimed sand before and after compaction. Available correlations between the CPT cone resistance and the relative density are only valid for non-crushable sands. Due to the partially crushable nature of lahars, the CPT data requires to be adjusted to allow for a correct interpretation of the CPT data. The objective of this paper is to characterize the chemical and mechanical properties of the lahar sands used for an ongoing project in the Port of Manila, which comprises reclamation activities using lahars from the east of Mount Pinatubo, it investigates their effect in the proposed correction factor. Additionally, numerous CPTs were carried out in a test trial and during the execution of the project. Based on this data, the influence of the grid spacing, compaction steps and the holding time on the compaction results are analyzed. Moreover, the so-called “aging effect” of the lahars is studied by comparing the results of the CPT testing campaign at different times after the vibro compaction activities. A considerable increase in the tip resistance of the CPT was observed over time.

Keywords: vibro compaction, CPT, lahar sands, correction factor, chemical composition

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Authors: P. Serrão, E. Costa, A. Ribeiro, V. Infante

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The characteristic sonority of the Portuguese guitar is in great part what makes Fado so distinguishable from other traditional song styles. The Portuguese guitar is a pear-shaped plucked chordophone with six courses of double strings. This study compares the two types of plain strings available for Portuguese guitar and used by the musicians. One is stainless steel spring wire, the other is high carbon spring steel (music wire). Some musicians mention noticeable differences in sound quality between these two string materials, such as a little more brightness and sustain in the steel strings. Experimental tests were performed to characterize string tension at pitch; mechanical strength and tuning stability using the universal testing machine; dimensional control and chemical composition analysis using the scanning electron microscope. The string dynamical behaviour characterization experiments, including frequency response, inharmonicity, transient response, damping phenomena and were made in a monochord test set-up designed and built in-house. Damping factor was determined for the fundamental frequency. As musicians are able to detect very small damping differences, an accurate a characterization of the damping phenomena for all harmonics was necessary. With that purpose, another improved monochord was set and a new system identification methodology applied. Due to the complexity of this task several adjustments were necessary until obtaining good experimental data. In a few cases, dynamical tests were repeated to detect any evolution in damping parameters after break-in period when according to players experience a new string sounds gradually less dull until reaching the typically brilliant timbre. Finally, each set of strings was played on one guitar by a distinguished player and recorded. The recordings which include individual notes, scales, chords and a study piece, will be analysed to potentially characterize timbre variations.

Keywords: damping factor, music wire, portuguese guitar, string dynamics

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Authors: Magdalena Bury-Kaminska, Aneta Szudy-Szczyrek, Aleksandra Nowaczynska, Olga Jankowska-Lecka, Marek Hus, Klaudia Kot

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Introduction: The aim of the research was to determine the changes in cognitive functioning in patients with plasma cell myeloma by comparing patients’ state before the treatment and during chemotherapy as well as to determine the biological factors that can be used to predict patients’ cognitive state. Methods: The patients underwent the research procedure twice: before chemotherapy and after 4-6 treatment cycles. A psychological test and measurement of the following biological variables were carried out: TNF-α (tumor necrosis factor), IL-6 (interleukin 6), IL-10 (interleukin 10), BDNF (brain-derived neurotrophic factor). The following research methods were implemented: the Montreal Cognitive Assessment (MoCA), Battery of Tests for Assessing Cognitive Functions PU1, experimental and clinical trials based on the Choynowski’s Memory Scale, Stroop Color-Word Interference Test (SCWT), depression measurement questionnaire. Results: The analysis of the research showed better cognitive functions of patients during chemotherapy in comparison to the phase before it. Moreover, neurotrophin BDNF allows to predict the level of selected cognitive functions (semantic fluency and execution control) already at the diagnosis stage. After 4-6 cycles, it is also possible to draw conclusions concerning the extent of working memory based on the level of BDNF. Cytokine TNF-α allows us to predict the level of letter fluency during anti-cancer treatment. Conclusions: It is possible to presume that BDNF has a protective influence on patients’ cognitive functions and working memory and that cytokine TNF-α co-occurs with a diminished execution control and better material grouping in terms of phonological fluency. Acknowledgment: This work was funded by the National Science Center in Poland [grant no. 2017/27/N/HS6/02057.

Keywords: chemobrain, cognitive impairment, non−central nervous system cancers, hematologic diseases

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Authors: Najade Sheriff, Malaz Elsaddig, Kevin Jones

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Background: Death in the first month of life accounts for an increasing proportion of under-five mortality. Advancement to reduce this number is being made across the globe; however, progress is slowest in sub-Saharan Africa. Objectives: The study aims to identify differences between neonatal deaths of inpatient babies born in a hospital facility in rural Uganda to those of neonates admitted from the community and to explore whether they can be used to risk stratify neonatal admissions. Results: A retrospective chart review was conducted on records for neonates admitted to the Special Care Baby Unit (SCBU) Kitovu Hospital from 1st July 2016 to 21st July 2017. A total of 442 babies were admitted and the overall neonatal mortality was 24.8% (40% inpatient, 37% community, 23% hospital referrals). 40% of deaths occurred within 24 hours of admission and the majority were male (63%). 43% of babies were hypothermic upon admission, a significantly greater proportion of which were inpatient babies born in labour ward (P=0.0025). Intrapartum related death accounted for ½ of all inpatient babies whereas complications of prematurity were the predominant cause of death in the community group (37%). Severe infection does not seem like a significant factor of mortality for inpatients (2%) as it does for community admissions (29%). Furthermore, with 52.5% of community admissions weighing < 1500g, very low birth weight (VLBW) may be a significant risk factor for community neonatal death. Conclusion: The neonatal mortality rate in this study is high, and the leading causes of death are all largely preventable. A high rate of inpatient birth asphyxiation indicates the need for good quality facility-based perinatal care as well as a greater focus on the management of hypothermia, such as Kangaroo care. Moreover, a reduction in preterm deliveries is necessary to reduce associated comorbidities, and monitoring for signs of infection is especially important for community admissions.

Keywords: community, mortality, newborn, Uganda

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Heba M. Jahin, Ali F. Bakr, Zeyad T. Elsayad

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As smart spaces can be defined as any working environment which integrates embedded computers, information appliances and multi-modal sensors to remain focused on the interaction between the users, their activity, and their behavior in the space; hence, smart space must be aware of their contexts and automatically adapt to their changing context-awareness, by interacting with their physical environment through natural and multimodal interfaces. Also, by serving the information used proactively. This paper suggests a dynamic framework through the architectural design process of the space based on the principles of computational methods and context-awareness principles to help in creating a field of changes and modifications. It generates possibilities, concerns about the physical, structural and user contexts. This framework is concerned with five main processes: gathering and analyzing data to generate smart design scenarios, parameters, and attributes; which will be transformed by coding into four types of models. Furthmore, connecting those models together in the interaction model which will represent the context-awareness system. Then, transforming that model into a virtual and ambient environment which represents the physical and real environments, to act as a linkage phase between the users and their activities taking place in that smart space . Finally, the feedback phase from users of that environment to be sure that the design of that smart space fulfill their needs. Therefore, the generated design process will help in designing smarts spaces that can be adapted and controlled to answer the users’ defined goals, needs, and activity.

Keywords: computational methods, context-awareness, design process, smart spaces

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Authors: Justin Fu, Thomas Mazzuchi, Shahram Sarkani

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A key factor in technological immaturity in defense weapons acquisition is lack of understanding critical integrations at the subsystem and component level. To address this shortfall, recent research in integration readiness level (IRL) combines with technology readiness level (TRL) to form a system readiness level (SRL). SRL can be enriched with more robust quantitative methods to provide the program manager a useful tool prior to committing to major weapons acquisition programs. This research harnesses previous mathematical models based on graph theory, Petri nets, and tropical algebra and proposes a modification of the desirable SRL mathematical properties such that a tightly integrated (multitude of interfaces) subsystem can display a lower SRL than an inherently less coupled subsystem. The synthesis of these methods informs an improved decision tool for the program manager to commit to expensive technology development. This research ties the separately developed manufacturing readiness level (MRL) into the network representation of the system and addresses shortfalls in previous frameworks, including the lack of integration weighting and the over-importance of a single extremely immature component. Tropical algebra (based on the minimum of a set of TRLs or IRLs) allows one low IRL or TRL value to diminish the SRL of the entire system, which may not be reflective of actuality if that component is not critical or tightly coupled. Integration connections can be weighted according to importance and readiness levels are modified to be a cardinal scale (based on an analytic hierarchy process). Integration arcs’ importance are dependent on the connected nodes and the additional integrations arcs connected to those nodes. Lack of integration is not represented by zero, but by a perfect integration maturity value. Naturally, the importance (or weight) of such an arc would be zero. To further explore the impact of grouping subsystems, a multi-objective genetic algorithm is then used to find various clusters or communities that can be optimized for the most representative subsystem SRL. This novel calculation is then benchmarked through simulation and using past defense acquisition program data, focusing on the newly introduced Middle Tier of Acquisition (rapidly field prototypes). The model remains a relatively simple, accessible tool, but at higher fidelity and validated with past data for the program manager to decide major defense acquisition program milestones.

Keywords: readiness, maturity, system, integration

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Konja Knüppel, Gerrit Meyer, Peter Nyhuis

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The increasing interconnectedness and complexity of production processes raise the susceptibility of production systems to failure. Therefore, the ability to respond quickly to failures is increasingly becoming a competitive factor. The research project "Sustainable failure management in manufacturing SMEs" is developing a methodology to identify failures in the production and select preventive and reactive measures in order to correct failures and to establish sustainable failure management systems.

Keywords: failure categorization, failure management, logistic performance, production optimization

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Authors: Viktor R. Stoynov, Zlatka V. Valkova-Jarvis

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The intelligent management and optimisation of radio resource technologies will lead to a considerable improvement in the overall performance in Next Generation Networks (NGNs). Carrier Aggregation (CA) technology, also known as Spectrum Aggregation, enables more efficient use of the available spectrum by combining multiple Component Carriers (CCs) in a virtual wideband channel. LTE-A (Long Term Evolution–Advanced) CA technology can combine multiple adjacent or separate CCs in the same band or in different bands. In this way, increased data rates and dynamic load balancing can be achieved, resulting in a more reliable and efficient operation of mobile networks and the enabling of high bandwidth mobile services. In this paper, several distinct CA deployment strategies for the utilisation of spectrum bands are compared in indoor-outdoor scenarios, simulated via the recently-developed Realistic Indoor Environment Generator (RIEG). We analyse the performance of the User Equipment (UE) by integrating the average throughput, the level of fairness of radio resource allocation, and other parameters, into one summative assessment termed a Comparative Factor (CF). In addition, comparison of non-CA and CA indoor mobile networks is carried out under different load conditions: varying numbers and positions of UEs. The experimental results demonstrate that the CA technology can improve network performance, especially in the case of indoor scenarios. Additionally, we show that an increase of carrier frequency does not necessarily lead to improved CF values, due to high wall-penetration losses. The performance of users under bad-channel conditions, often located in the periphery of the cells, can be improved by intelligent CA location. Furthermore, a combination of such a deployment and effective radio resource allocation management with respect to user-fairness plays a crucial role in improving the performance of LTE-A networks.

Keywords: comparative factor, carrier aggregation, indoor mobile network, resource allocation

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Authors: Rani Danavath, V. B. Narsimha

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Cloud computing is emerging as a new paradigm of large scale distributed computing. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., three service models, and four deployment networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics models. Load balancing is one of the main challenges in cloud computing, which is required to distribute the dynamic workload across multiple nodes, to ensure that no single node is overloaded. It helps in optimal utilization of resources, enhancing the performance of the system. The goal of the load balancing is to minimize the resource consumption and carbon emission rate, that is the direct need of cloud computing. This determined the need of new metrics energy consumption and carbon emission for energy-efficiency load balancing techniques in cloud computing. Existing load balancing techniques mainly focuses on reducing overhead, services, response time and improving performance etc. In this paper we introduced a Technique for energy-efficiency, but none of the techniques have considered the energy consumption and carbon emission. Therefore, our proposed work will go towards energy – efficiency. So this energy-efficiency load balancing technique can be used to improve the performance of cloud computing by balancing the workload across all the nodes in the cloud with the minimum resource utilization, in turn, reducing energy consumption, and carbon emission to an extent, which will help to achieve green computing.

Keywords: cloud computing, distributed computing, energy efficiency, green computing, load balancing, energy consumption, carbon emission

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Authors: Emma Serwaa Obobisaa, Haibo Chen

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International trade and outward foreign direct investment are important factors which are generally recognized in the economic growth and development. Though several scholars have struggled to reveal the influence of trade and outward foreign direct investment (FDI) on economic growth, most studies utilized common econometric models such as vector autoregression and aggregated the variables, which for the most part prompts, however, contradictory and mixed results. Thus, there is an exigent need for the precise study of the trade and FDI effect of economic growth while applying strong econometric models and disaggregating the variables into its separate individual variables to explicate their respective effects on economic growth. This will guarantee the provision of policies and strategies that are geared towards individual variables to ensure sustainable development and growth. This study, therefore, seeks to examine the causal effect of China-Africa trade and Outward Foreign Direct Investment on the economic growth of Africa using a robust and recent econometric approach such as system dynamics model. Our study impanels and tests an ensemble of a group of vital variables predominant in recent studies on trade-FDI-economic growth causality: Foreign direct ınvestment, international trade and economic growth. Our results showed that the system dynamics method provides accurate statistical inference regarding the direction of the causality among the variables than the conventional method such as OLS and Granger Causality predominantly used in the literature as it is more robust and provides accurate, critical values.

Keywords: economic growth, outward foreign direct investment, system dynamics model, international trade

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Authors: Adrian Kong, William Chang, Rolando Valdes, Alec Rodriguez, Roberto Miki

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The Pneumask consists of a custom snorkel-specific adapter that attaches a snorkel-port of the mask to a 3D-printed filter. This full-face snorkel mask was designed for use as personal protective equipment (PPE) during the COVID-19 pandemic when there was a widespread shortage of PPE for medical personnel. Various clinical validation tests have been conducted, including the sealing capability of the mask, filter performance, CO2 buildup, and clinical usability. However, data regarding the filter efficiencies of Pneumask and multiple filter types have not been determined. Using an experimental system, we evaluated the filtration efficiency across various masks and filters during inhalation. Eighteen combinations of respirator models (5 P100 FFRs, 4 Dolfino Masks) and filters (2091, 7093, 7093CN, BB50T) were evaluated for their exposure to airborne particles sized 0.3 - 10.0 microns using an electronic airborne particle counter. All respirator model combinations provided similar performance levels for 1.0-micron, 3.0-micron, 5.0-micron, 10.0-microns, with the greatest differences in the 0.3-micron and 0.5-micron range. All models provided expected performances against all particle sizes, with Class P100 respirators providing the highest performance levels across all particle size ranges. In conclusion, the modified snorkel mask has the potential to protect providers who care for patients with COVID-19 from increased airborne particle exposure.

Keywords: COVID-19, PPE, mask, filtration, efficiency

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Authors: Nazaitul Idya Hamzah, Muhammad Syafiq Mazli, Maszatul Akmar Mustafa

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The peak demand forecast is crucial to identify the future generation plant up needed in the long-term capacity planning analysis for Peninsular Malaysia as well as for the transmission and distribution network planning activities. Currently, peak demand forecast (in Mega Watt) is derived from the generation forecast by using load factor assumption. However, a forecast using this method has underperformed due to the structural changes in the economy, emerging trends and weather uncertainty. The dynamic changes of these drivers will result in many possible outcomes of peak demand for Peninsular Malaysia. This paper will look into the independent model of peak demand forecasting. The model begins with the selection of driver variables to capture long-term growth. This selection and construction of variables, which include econometric, emerging trend and energy variables, will have an impact on the peak forecast. The actual framework begins with the development of system energy and load shape forecast by using the system’s hourly data. The shape forecast represents the system shape assuming all embedded technology and use patterns to continue in the future. This is necessary to identify the movements in the peak hour or changes in the system load factor. The next step would be developing the peak forecast, which involves an iterative process to explore model structures and variables. The final step is combining the system energy, shape, and peak forecasts into the hourly system forecast then modifying it with the forecast adjustments. Forecast adjustments are among other sales forecasts for electric vehicles, solar and other adjustments. The framework will result in an hourly forecast that captures growth, peak usage and new technologies. The advantage of this approach as compared to the current methodology is that the peaks capture new technology impacts that change the load shape.

Keywords: hourly load profile, load forecasting, long term peak demand forecasting, peak demand

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Authors: Neil Bar, Andrew Heweston

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Probability of failure (PF) often appears alongside factor of safety (FS) in design acceptance criteria for rock slope, underground excavation and open pit mine designs. However, the design acceptance criteria generally provide no guidance relating to how PF should be calculated for homogeneous and heterogeneous rock masses, or what qualifies a ‘reasonable’ PF assessment for a given slope design. Observational and kinematic methods were widely used in the 1990s until advances in computing permitted the routine use of numerical modelling. In the 2000s and early 2010s, PF in numerical models was generally calculated using the point estimate method. More recently, some limit equilibrium analysis software offer statistical parameter inputs along with Monte-Carlo or Latin-Hypercube sampling methods to automatically calculate PF. Factors including rock type and density, weathering and alteration, intact rock strength, rock mass quality and shear strength, the location and orientation of geologic structure, shear strength of geologic structure and groundwater pore pressure influence the stability of rock slopes. Significant engineering and geological judgment, interpretation and data interpolation is usually applied in determining these factors and amalgamating them into a geotechnical model which can then be analysed. Most factors are estimated ‘approximately’ or with allowances for some variability rather than ‘exactly’. When it comes to numerical modelling, some of these factors are then treated deterministically (i.e. as exact values), while others have probabilistic inputs based on the user’s discretion and understanding of the problem being analysed. This paper discusses the importance of understanding the key aspects of slope design for homogeneous and heterogeneous rock masses and how they can be translated into reasonable PF assessments where the data permits. A case study from a large open pit gold mine in a complex geological setting in Western Australia is presented to illustrate how PF can be calculated using different methods and obtain markedly different results. Ultimately sound engineering judgement and logic is often required to decipher the true meaning and significance (if any) of some PF results.

Keywords: probability of failure, point estimate method, Monte-Carlo simulations, sensitivity analysis, slope stability

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Authors: Scott M. Black, Craig Maclean, Pauline Hall Barrientos, Konstantinos Ritos, Asimina Kazakidi

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Aortic aneurysms and dissections are leading causes of death in cardiovascular disease. Both inevitably lead to hemodynamic instability without surgical intervention in the form of vascular stent-graft deployment. An accurate description of the aortic geometry and blood flow in patient-specific cases is vital for treatment planning and long-term success of such grafts, as they must generate physiological branch perfusion and in-stent hemodynamics. The aim of this study was to create patient-specific computational fluid dynamics (CFD) models through a multi-modality, multi-dimensional approach with boundary condition optimization to predict branch flow rates and in-stent hemodynamics in custom stent-graft configurations. Three-dimensional (3D) thoracoabdominal aortae were reconstructed from four-dimensional flow-magnetic resonance imaging (4D Flow-MRI) and computed tomography (CT) medical images. The former employed a novel approach to generate and enhance vessel lumen contrast via through-plane velocity at discrete, user defined cardiac time steps post-hoc. To produce patient-specific boundary conditions (BCs), the aortic geometry was reduced to a one-dimensional (1D) model. Thereafter, a zero-dimensional (0D) 3-Element Windkessel model (3EWM) was coupled to each terminal branch to represent the distal vasculature. In this coupled 0D-1D model, the 3EWM parameters were optimized to yield branch flow waveforms which are representative of the 4D Flow-MRI-derived in-vivo data. Thereafter, a 0D-3D CFD model was created, utilizing the optimized 3EWM BCs and a 4D Flow-MRI-obtained inlet velocity profile. A sensitivity analysis on the effects of stent-graft configuration and BC parameters was then undertaken using multiple stent-graft configurations and a range of distal vasculature conditions. 4D Flow-MRI granted unparalleled visualization of blood flow throughout the cardiac cycle in both the pre- and postsurgical states. Segmentation and reconstruction of healthy and stented regions from retrospective 4D Flow-MRI images also generated 3D models with geometries which were successfully validated against their CT-derived counterparts. 0D-1D coupling efficiently captured branch flow and pressure waveforms, while 0D-3D models also enabled 3D flow visualization and quantification of clinically relevant hemodynamic parameters for in-stent thrombosis and graft limb occlusion. It was apparent that changes in 3EWM BC parameters had a pronounced effect on perfusion distribution and near-wall hemodynamics. Results show that the 3EWM parameters could be iteratively changed to simulate a range of graft limb diameters and distal vasculature conditions for a given stent-graft to determine the optimal configuration prior to surgery. To conclude, this study outlined a methodology to aid in the prediction post-surgical branch perfusion and in-stent hemodynamics in patient specific cases for the implementation of custom stent-grafts.

Keywords: 4D flow-MRI, computational fluid dynamics, vascular stent-grafts, windkessel

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Authors: Mohd Bahroddin Badri, Ridzuan Masri

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Despite the existence of large volume of waqf assets, it is argued that the potential of these assets not fully unleashed. There are many waqf assets especially in the form of land waqf that are idle and undeveloped mainly because of the insufficient fund and lack of investment expertise. This paper attempts to explore few cases on the innovation of waqf development in Malaysia and some countries that demonstrate synergistic collaboration between stakeholders, e.g., the government, nazir, Islamic religious councils, corporate entities and Islamic financial institutions for waqf development. This paper shows that cash waqf, corporate waqf, Build-Operate-Transfer (BOT) and Sukuk are found to be contemporary mechanisms within Islamic finance ecosystem that drive and rejuvenate the development of waqf to the next level. It further highlights few samples of waqf Sukuk that were successfully issued in selected countries. This paper also demonstrates that the benefit of waqf is beyond religious matters, which may also include education, healthcare, social care, infrastructure and corporate social responsibility (CSR) activities. This research is qualitative in nature, whereby the researcher employs descriptive method on the collected data. The researcher applies case study and library research method to collect and analyse data from journal articles, research papers, conference paper and annual reports. In a nutshell, the potential of contemporary models as demonstrated in this paper is very promising, in which the practical application of those instruments should be expanded for the rejuvenation of waqf asset.

Keywords: cash waqf, corporate waqf, Sukuk waqf, build-operate-transfer

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Authors: Estela Díaz

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This article aims to make three contributions. First, build on the literature on ethical decision-making literature by exploring factors that influence the intention of adopting veganism. Second, study the superiority of extended models of the Theory of Planned Behavior (TPB) for understanding the process involved in forming the intention of adopting veganism. Third, analyze the moderating effect of gender on TPB given that attitudes and behavior towards animals are gender-sensitive. No study, to our knowledge, has examined these questions. Veganism is not a diet but a political and moral stand that exclude, for moral reasons, the use of animals. Although there is a growing interest in studying veganism, it continues being overlooked in empirical research, especially within the domain of social psychology. TPB has been widely used to study a broad range of human behaviors, including moral issues. Nonetheless, TPB has rarely been applied to examine ethical decisions about animals and, even less, to veganism. Hence, the validity of TPB in predicting the intention of adopting veganism remains unanswered. A total of 476 non-vegan Spanish university students (55.6% female; the mean age was 23.26 years, SD= 6.1) responded to online and pencil-and-paper self-reported questionnaire based on previous studies. TPB extended models incorporated two background factors: ‘general attitudes towards humanlike-attributes ascribed to animals’ (AHA) (capacity for reason/emotions/suffer, moral consideration, and affect-towards-animals); and ‘general attitudes towards 11 uses of animals’ (AUA). SPSS 22 and SmartPLS 3.0 were used for statistical analyses. This study constructed a second-order reflective-formative model and took the multi-group analysis (MGA) approach to study gender effects. Six models of TPB (the standard and five competing) were tested. No a priori hypotheses were formulated. The results gave partial support to TPB. Attitudes (ATTV) (β = .207, p < .001), subjective norms (SNV) (β = .323, p < .001), and perceived control behavior (PCB) (β = .149, p < .001) had a significant direct effect on intentions (INTV). This model accounted for 27,9% of the variance in intention (R2Adj = .275) and had a small predictive relevance (Q2 = .261). However, findings from this study reveal that contrary to what TPB generally proposes, the effect of the background factors on intentions was not fully mediated by the proximal constructs of intentions. For instance, in the final model (Model#6), both factors had significant multiple indirect effect on INTV (β = .074, 95% C = .030, .126 [AHA:INTV]; β = .101, 95% C = .055, .155 [AUA:INTV]) and significant direct effect on INTV (β = .175, p < .001 [AHA:INTV]; β = .100, p = .003 [AUA:INTV]). Furthermore, the addition of direct paths from background factors to intentions improved the explained variance in intention (R2 = .324; R2Adj = .317) and the predictive relevance (Q2 = .300) over the base-model. This supports existing literature on the superiority of enhanced TPB models to predict ethical issues; which suggests that moral behavior may add additional complexity to decision-making. Regarding gender effect, MGA showed that gender only moderated the influence of AHA on ATTV (e.g., βWomen−βMen = .296, p < .001 [Model #6]). However, other observed gender differences (e.g. the explained variance of the model for intentions were always higher for men that for women, for instance, R2Women = .298; R2Men = .394 [Model #6]) deserve further considerations, especially for developing more effective communication strategies.

Keywords: veganism, Theory of Planned Behavior, background factors, gender moderation

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Authors: Romel Cordova Shedan

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The traditional seismic design is the methodology of Forced Based Design (FBD). The Displacement Based Design (DBD) is a seismic design that considers structural damage to achieve a failure mechanism of the structure before the collapse. It is easier to quantify damage of a structure with displacements rather than forces. Therefore, a structure to achieve an inelastic displacement design with good ductility, it is necessary to be damaged. The first part of this investigation is about differences between the methodologies of DBD and FBD with some DBD advantages. In the second part, there is a study case about a dual building 5-story, which is regular in plan and elevation. The building is located in a seismic zone, which acceleration in firm soil is 45% of the acceleration of gravity. Then it is applied both methodologies into the study case to compare its displacements, shear forces and overturning moments. In the third part, the Dynamic Time History Analysis (DTHA) is done, to compare displacements with DBD and FBD methodologies. Three accelerograms were used and the magnitude of the acceleration scaled to be spectrum compatible with design spectrum. Then, using ASCE 41-13 guidelines, the hinge plastics were assigned to structure. Finally, both methodologies results about study case are compared. It is important to take into account that the seismic performance level of the building for DBD is greater than FBD method. This is due to drifts of DBD are in the order of 2.0% and 2.5% comparing with FBD drifts of 0.7%. Therefore, displacements of DBD is greater than the FBD method. Shear forces of DBD result greater than FBD methodology. These strengths of DBD method ensures that structure achieves design inelastic displacements, because those strengths were obtained due to a displacement spectrum reduction factor which depends on damping and ductility of the dual system. Also, the displacements for the study case for DBD results to be greater than FBD and DTHA. In that way, it proves that the seismic performance level of the building for DBD is greater than FBD method. Due to drifts of DBD which are in the order of 2.0% and 2.5% compared with little FBD drifts of 0.7%.

Keywords: displacement-based design, displacement spectrum reduction factor, dynamic time history analysis, forced based design

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Authors: Yan Huang

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Event-related potentials (ERPs) technique helps researchers to make continuous measures on the whole process of language comprehension, with an excellent temporal resolution at the level of milliseconds. The research on sentence processing has developed from the behavioral level to the neuropsychological level, which brings about a variety of sentence processing theories and models. However, the applicability of these models to L2 learners is still under debate. Therefore, the present study aims to investigate the neural mechanisms underlying English subjunctive mood processing by Chinese ESL learners. To this end, English subject clauses with subjunctive moods are used as the stimuli, all of which follow the same syntactic structure, “It is + adjective + that … + (should) do + …” Besides, in order to examine the role that language proficiency plays on L2 processing, this research deals with two groups of Chinese ESL learners (18 males and 22 females, mean age=21.68), namely, high proficiency group (Group H) and low proficiency group (Group L). Finally, the behavioral and neurophysiological data analysis reveals the following findings: 1) Syntax and semantics interact with each other on the SECOND phase (300-500ms) of sentence processing, which is partially in line with the Three-phase Sentence Model; 2) Language proficiency does affect L2 processing. Specifically, for Group H, it is the syntactic processing that plays the dominant role in sentence processing while for Group L, semantic processing also affects the syntactic parsing during the THIRD phase of sentence processing (500-700ms). Besides, Group H, compared to Group L, demonstrates a richer native-like ERPs pattern, which further demonstrates the role of language proficiency in L2 processing. Based on the research findings, this paper also provides some enlightenment for the L2 pedagogy as well as the L2 proficiency assessment.

Keywords: Chinese ESL learners, English subjunctive mood, ERPs, L2 processing

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Authors: Jatziri Y. Moreno-Martínez, Arturo Galván, Israel Enrique Herrera Díaz, José Ramón Gasca Tirado

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In recent years, for the elevated viaducts in Mexico City, a construction system based on precast/pre-stressed concrete elements has been used, in which the bridge girders are divided in two parts by imposing a hinged support in sections where the bending moments that are originated by the gravity loads in a continuous beam are minimal. Precast concrete girders with dapped ends are a representative sample of a behavior that has complex configurations of stresses that make them more vulnerable to cracking due to flexure–shear interaction. The design procedures for ends of the dapped girders are well established and are based primarily on experimental tests performed for different configurations of reinforcement. The critical failure modes that can govern the design have been identified, and for each of them, the methods for computing the reinforcing steel that is needed to achieve adequate safety against failure have been proposed. Nevertheless, the design recommendations do not include procedures for controlling diagonal cracking at the entrant corner under service loading. These cracks could cause water penetration and degradation because of the corrosion of the steel reinforcement. The lack of visual access to the area makes it difficult to detect this damage and take timely corrective actions. Three-dimensional non-linear numerical models based on Finite Element Method to study the cracking at the entrant corner of dapped-end beams were performed using the software package ANSYS v. 11.0. The cracking was numerically simulated by using the smeared crack approach. The concrete structure was modeled using three-dimensional solid elements SOLID65 capable of cracking in tension and crushing in compression. Drucker-Prager yield surface was used to include the plastic deformations. The longitudinal post-tension was modeled using LINK8 elements with multilinear isotropic hardening behavior using von Misses plasticity. The reinforcement was introduced with smeared approach. The numerical models were calibrated using experimental tests carried out in “Instituto de Ingeniería, Universidad Nacional Autónoma de México”. In these numerical models the characteristics of the specimens were considered: typical solution based on vertical stirrups (hangers) and on vertical and horizontal hoops with a post-tensioned steel which contributed to a 74% of the flexural resistance. The post-tension is given by four steel wires with a 5/8’’ (16 mm) diameter. Each wire was tensioned to 147 kN and induced an average compressive stress of 4.90 MPa on the concrete section of the dapped end. The loading protocol consisted on applying symmetrical loading to reach the service load (180 kN). Due to the good correlation between experimental and numerical models some additional numerical models were proposed by considering different percentages of post-tension in order to find out how much it influences in the appearance of the cracking in the reentrant corner of the dapped-end beams. It was concluded that the increasing of percentage of post-tension decreases the displacements and the cracking in the reentrant corner takes longer to appear. The authors acknowledge at “Universidad de Guanajuato, Campus Celaya-Salvatierra” and the financial support of PRODEP-SEP (UGTO-PTC-460) of the Mexican government. The first author acknowledges at “Instituto de Ingeniería, Universidad Nacional Autónoma de México”.

Keywords: concrete dapped-end beams, cracking control, finite element analysis, postension

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Authors: Lubna Azmi, Ila Shukla, Shyam Sundar Gupta, Padam Kant, Ch. V. Rao

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Gastric ulcer is a major global health threat, and it is the leading cause of stomach cancer death worldwide. Helicobacter pylori bacteriumis the most important etiologic factor for gastric ulcer. This infection is highly pervasive in South Asian developing countries, especially in India, Nepal, Srilanka etc. due to diversification in geographic area. Pathophysiology of gastric mucosal damage associated with non-invasive bacterium has not justified in detail, but it leads to change in histopathology, immunochemistry of the gastric and duodenal reason of host. The mechanism responsible for bacteria tissue tropism and mucosal damage in stomach variance during the disease is not clearly described and understood scientifically in treatment and control of pathogenic organisms. Polyphenols are secondary metabolites of plants and are generally involved in defense against aggression by pathogens. 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one and 1-hydroxy-5,7-dimethoxy-2-naphthalene-carboxaldehyde are polyphenolic compound obtained from popular Indian medicinal plants ghavpatta (ArgeriaspeciosaLinn.f) andBael (Aeglemarmelos) have long been used in traditional Ayurvedic Indian medicine for various diseases. They have promising effects on ulcer, as detailed investigation has made in our laboratory. Therefore, the aim of present study is to explore membrane –dependent morphogenesis of H. pylori and associated apoptosis-mediated cell death. Based on this we analyzed immune gene expression in stomach of experimental animals with H. pylori, using quantitative reverse transcription polymerase chain reaction(q RT-PCR). This revealed rapid induction of prostaglandin, interferon I (INF-I), interferon II (INF-II) and INF-I associated genes in the infected animal. Ultrastructural changes associated with H. pylori will be taken for advanced studies. This investigation shows that the biomarkers eradicate H. pylori bacterium caused gastric ulcer which is a major risk factor for gastric cancer.

Keywords: gastric ulcer, Helicobacter pylori, immunochemistry, polyphenols

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Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Hamid Ahmadi, Amirreza Ghaffari

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Fatigue life of tubular joints in offshore structures is not only dependent on the value of hot-spot stress, but is also significantly influenced by the through-the-thickness stress distribution characterized by the degree of bending (DoB). The DoB exhibits considerable scatter calling for greater emphasis in accurate determination of its governing probability distribution which is a key input for the fatigue reliability analysis of a tubular joint. Although the tubular X-joints are commonly found in offshore jacket structures, as far as the authors are aware, no comprehensive research has been carried out on the probability distribution of the DoB in tubular X-joints. What has been used so far as the probability distribution of the DoB in reliability analyses is mainly based on assumptions and limited observations, especially in terms of distribution parameters. In the present paper, results of parametric equations available for the calculation of the DoB have been used to develop probability distribution models for the DoB in the chord member of tubular X-joints subjected to four types of bending loads. Based on a parametric study, a set of samples was prepared and density histograms were generated for these samples using Freedman-Diaconis method. Twelve different probability density functions (PDFs) were fitted to these histograms. The maximum likelihood method was utilized to determine the parameters of fitted distributions. In each case, Kolmogorov-Smirnov test was used to evaluate the goodness of fit. Finally, after substituting the values of estimated parameters for each distribution, a set of fully defined PDFs have been proposed for the DoB in tubular X-joints subjected to bending loads.

Keywords: tubular X-joint, degree of bending (DoB), probability density function (PDF), Kolmogorov-Smirnov goodness-of-fit test

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Authors: B. Godefroy, D. Beladjine, K. Beddiar

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In France, construction BIM actors commonly evoke the BIM gains for exploitation by integrating of the life cycle of a building. The standardization of level 7 of development would achieve this stage of the digital model. The householders include local public authorities, social landlords, public institutions (health and education), enterprises, facilities management companies. They have a dual role: owner and manager of their housing complex. In a context of financial constraint, the BIM of exploitation aims to control costs, make long-term investment choices, renew the portfolio and enable environmental standards to be met. It assumes a knowledge of the existing buildings, marked by its size and complexity. The information sought must be synthetic and structured, it concerns, in general, a real estate complex. We conducted a study with professionals about their concerns and ways to use it to see how householders could benefit from this development. To obtain results, we had in mind the recurring interrogation of the project management, on the needs of the operators, we tested the following stages: 1) Inculcate a minimal culture of BIM with multidisciplinary teams of the operator then by business, 2) Learn by BIM tools, the adaptation of their trade in operations, 3) Understand the place and creation of a graphic and technical database management system, determine the components of its library so their needs, 4) Identify the cross-functional interventions of its managers by business (operations, technical, information system, purchasing and legal aspects), 5) Set an internal protocol and define the BIM impact in their digital strategy. In addition, continuity of management by the integration of construction models in the operation phase raises the question of interoperability in the control of the production of IFC files in the operator’s proprietary format and the export and import processes, a solution rivaled by the traditional method of vectorization of paper plans. Companies that digitize housing complex and those in FM produce a file IFC, directly, according to their needs without recourse to the model of construction, they produce models business for the exploitation. They standardize components, equipment that are useful for coding. We observed the consequences resulting from the use of the BIM in the property industry and, made the following observations: a) The value of data prevail over the graphics, 3D is little used b) The owner must, through his organization, promote the feedback of technical management information during the design phase c) The operator's reflection on outsourcing concerns the acquisition of its information system and these services, observing the risks and costs related to their internal or external developments. This study allows us to highlight: i) The need for an internal organization of operators prior to a response to the construction management ii) The evolution towards automated methods for creating models dedicated to the exploitation, a specialization would be required iii) A review of the communication of the project management, management continuity not articulating around his building model, it must take into account the environment of the operator and reflect on its scope of action.

Keywords: information system, interoperability, models for exploitation, property industry

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Authors: Kldiashvili Ekaterina, Burduli Archil, Ghortlishvili Gocha

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Introduction – Medical information system (MIS) is at the heart of information technology (IT) implementation policies in healthcare systems around the world. Different architecture and application models of MIS are developed. Despite of obvious advantages and benefits, application of MIS in everyday practice is slow. Objective - On the background of analysis of the existing models of MIS in Georgia has been created a multi-user web-based approach. This presentation will present the architecture of the system and its application for image based second opinion consultations. Methods – The MIS has been created with .Net technology and SQL database architecture. It realizes local (intranet) and remote (internet) access to the system and management of databases. The MIS is fully operational approach, which is successfully used for medical data registration and management as well as for creation, editing and maintenance of the electronic medical records (EMR). Five hundred Georgian language electronic medical records from the cervical screening activity illustrated by images were selected for second opinion consultations. Results – The primary goal of the MIS is patient management. However, the system can be successfully applied for image based second opinion consultations. Discussion – The ideal of healthcare in the information age must be to create a situation where healthcare professionals spend more time creating knowledge from medical information and less time managing medical information. The application of easily available and adaptable technology and improvement of the infrastructure conditions is the basis for eHealth applications. Conclusion - The MIS is perspective and actual technology solution. It can be successfully and effectively used for image based second opinion consultations.

Keywords: digital images, medical information system, second opinion consultations, electronic medical record

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Authors: Jenny Fairthorne, Yuka Mori, Helen Leonard

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Background: Primary care-givers of children with autism spectrum disorder (ASD) or intellectual disability (ID) have poorer health and quality of life (QoL) than primary care-givers (hereafter referred to as just care-givers) of typically developing children. We aimed to review original research which described factors impacting the health of care-givers of children with ASD or ID and to discuss how these factors might influence care-giver health. Methods: We searched Web of Knowledge, Medline, Scopus and Google Scholar using selections of words from each of three groups. The first comprised terms associated with ASD and ID and included autism, pervasive development disorder, intellectual disability, mental retardation, disability, disabled, Down and Asperger. The second included terms related to health such as depression, physical, mental, psychiatric, psychological and well-being. The third was terms related to care-givers such as mother, parent and care-giver. We included an original paper in our review if it was published between 1st January 1990 and 31st December, 2016, described original research in a peer-reviewed journal and was written in English. Additional criteria were that the research used a study population of 15 persons or more; described a risk or protective factor for the health of care-givers of a child with ASD, ID or a sub-type (such as ASD with ID or Down syndrome). Using previous research, we developed a simple and objective five-level tool to assess the strength of evidence provided by the reviewed papers. Results: We retained 33 papers. Factors impacting primary care-giver health included child behaviour, level of support, socio-economic status (SES) and diagnostic issues. Challenging child behaviour, the most commonly identified risk factor for poorer care-giver health and QoL was reported in ten of the studies. A higher level of support was associated with improved care-giver health and QoL. For example, substantial evidence indicated that family support reduced care-giver burden in families with a child with ASD and that family and neighbourhood support was associated with improved care-giver mental health. Higher socio-economic status (SES) was a protective factor for care-giver health and particularly maternal health. Diagnostic uncertainty and an unclear prognosis are factors which can cause the greatest concern to care-givers of children with ASD and those for whom a cause of their child’s ID has not been identified. We explain how each of these factors might impact caregiver health and how they might act differentially in care-givers of children with different types of ASD or ID (such as Down syndrome and ASD without ID). Conclusion: Care-givers of children with ASD may be more likely to experience many risk factors and less likely to experience the protective factors we identified for poorer mental health. Interventions to reduce risk factors and increase protective factors could pave the way for improved care-giver health. For example, workshops to train care-givers to better manage challenging child behaviours and earlier diagnosis of ASD (and particularly ASD without ID) would seem likely to improve care-giver well-being. Similarly, helping to expand support networks might reduce care-giver burden and stress leading to improved health.

Keywords: autism, caregivers, health, intellectual disability, mothers, review

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Jun-Ki Choi

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The usage of valuable resources and the potential for waste generation at the end of the life cycle of photovoltaic (PV) technologies necessitate a proactive planning for a PV recycling infrastructure. To ensure the sustainability of PV in large scales of deployment, it is vital to develop and institute low-cost recycling technologies and infrastructure for the emerging PV industry in parallel with the rapid commercialization of these new technologies. There are various issues involved in the economics of PV recycling and this research examine those at macro and micro levels, developing a holistic interpretation of the economic viability of the PV recycling systems. This study developed mathematical models to analyze the profitability of recycling technologies and to guide tactical decisions for allocating optimal location of PV take-back centers (PVTBC), necessary for the collection of end of life products. The economic decision is usually based on the level of the marginal capital cost of each PVTBC, cost of reverse logistics, distance traveled, and the amount of PV waste collected from various locations. Results illustrated that the reverse logistics costs comprise a major portion of the cost of PVTBC; PV recycling centers can be constructed in the optimally selected locations to minimize the total reverse logistics cost for transporting the PV wastes from various collection facilities to the recycling center. In the micro- process level, automated recycling processes should be developed to handle the large amount of growing PV wastes economically. The market price of the reclaimed materials are important factors for deciding the profitability of the recycling process and this illustrates the importance of the recovering the glass and expensive metals from PV modules.

Keywords: photovoltaic, recycling, mathematical models, sustainability

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