Search results for: algorithm techniques

Authors: Hanaa M. Abu El Einin, Ahmed T. Sharaf El- Din

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Biomphalaria snails play an integral role in the transmission of Schistosoma mansoni, the causative agent for human schistosomiasis. Two species of Biomphalaria were reported from Egypt, Biomphalaria alexandrina and Biomphalaria glabrata, and later on a hybrid of B. alexandrina and B. glabrata was reported in streams at Nile Delta. All were known to be excellent hosts of S. mansoni. Host-parasite relationship can be viewed in terms of snail susceptibility and parasite infectivity. The objective of this study will highlight the progress that has been made in using molecular approaches to describe the correct identification of snail species that participating in transmission of schistosomiasis, rapid diagnose of infection in addition to susceptibility and resistance type. Snails were identified using of molecular methods involving Randomly Amplified Polymorphic DNA (RAPD), Polymerase Chain Reaction, Restriction Fragment Length Polymorphisms (PCR-RFLP) and Species - specific- PCR. Molecular approaches to diagnose parasite in snails from Egypt: Nested PCR assay and small subunit (SSU) rRNA gene. Also RAPD PCR for study susceptible and resistance phenotype. The results showed that RAPD- PCR, PCR-RFLP and species-specific-PCR techniques were confirmed that: no evidence for the presence of B. glabrata in Egypt, All Biomphalaria snails collected identified as B. alexandrina snail i-e B alexandrinia is a common and no evidence for hybridization with B. glabrata. The adopted specific nested PCR assay revealed much higher sensitivity which enables the detection of S. mansoni infected snails down to 3 days post infection. Nested PCR method for detection of infected snails using S. mansoni fructose -1,6- bisphosphate aldolase (SMALDO) primer, these primers are specific only for S. mansoni and not cross reactive with other schistosomes or molluscan aldolases Nested PCR for such gene is sensitive enough to detect one cercariae. Genetic variations between B. alexandrina strains that are susceptible and resistant to Schistosoma infec¬tion using a RAPD-PCR showed that 39.8% of the examined snails collected from the field were resistant, while 60.2% of these snails showed high infection rates. In conclusion the genetics of the intermediate host plays a more important role in the epidemiological control of schistosomiasis.

Keywords: biomphalaria, molecular differentiation, parasite detection, schistosomiasis

Procedia PDF Downloads 198

Authors: Ashwini Patil

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Caudal block is one of the most commonly used regional anesthetic techniques in children. Currently, fentanyl is used as an adjuvant to bupivacaine to prolong analgesia but fentanyl is a narcotic. Dexamethasone, a glucocorticoid with strong anti-inflammatory effects provides improvement in post-operative analgesia and post-operative side effects. However, its analgesic efficacy and safety in comparison with fentanyl has not been extensively studied. So the objective of this randomized controlled study is to compare dexamethasone with fentanyl as an adjuvant to bupivacaine for caudal block in children in relation to the duration of caudal analgesia, post-operative analgesic requirement and incidence of post-operative nausea and vomiting. This study included 100 children, aged 1–6 years, undergoing lower abdominal surgeries. Patients were randomized into two groups, 50 each to receive a combination of dexamethasone 0.2 mg/kg along with 1 ml/kg bupivacaine 0.25% (group A) or combination of fentanyl (1 ug/kg) along with 1ml/kg bupivacaine 0.25% (group B). In the post-operative period, pain was assessed using a Modified Objective Pain Scale (MOPS) until 12 hr after surgery and rescue analgesia is administered when MOPS score 4 or more is recorded. Residual motor block, number of analgesic doses required within 24 hr after surgery, sedation scores, intra-operative and post-operative hemodynamic variables, post-operative nausea and vomiting (PONV), and other adverse effects were recorded. Data is analysed using unpaired t test and Significance level of P< 0.05 is considered statistically significant. Group A showed a significantly longer time to first analgesic requirement than group B (p<0.05). The number of rescue analgesic doses required in the first 24 h was significantly less in group A (p<0.05). Group A showed significantly lower MOPS scores than group B(p<0.05). Intra-operative and post-operative hemodynamic variables, Modified Bromage Scale scores, and sedation scores were comparable in both the groups. Group A showed significantly fewer incidences of PONV compared with group B(p<0.05). This study reveals that adding dexamethasone to bupivacaine prolongs the duration of postoperative analgesia and decreases the incidence of PONV as compared to combination of fentanyl to bupivacaine after a caudal block in pediatric patients.

Keywords: bupivacaine, caudal analgesia, dexamethasone, pediatric

Procedia PDF Downloads 208

Authors: Javed Mohammed

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Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

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Authors: Yu-Wen Chen, Szu-Wei Huang, Chung-Hsiang Mu, Kelvin Cheng

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This paper developed a data-driven based model to deal with the causality between the Continuous Emission Monitoring System (CEMS, by Environmental Protection Administration, Taiwan) in industrial factories, and the air quality around environment. Compared to the heavy burden of traditional numerical models of regional weather and air pollution simulation, the lightweight burden of the proposed model can provide forecasting hourly with current observations of weather, air pollution and emissions from factories. The observation data are included wind speed, wind direction, relative humidity, temperature and others. The observations can be collected real time from Open APIs of civil IoT Taiwan, which are sourced from 439 weather stations, 10,193 qualitative air stations, 77 national quantitative stations and 140 CEMS quantitative industrial factories. This study completed a causal inference engine and gave an air pollution forecasting for the next 12 hours related to local industrial factories. The outcomes of the pollution forecasting are produced hourly with a grid resolution of 1km*1km on IIoTC (Industrial Internet of Things Cloud) and saved in netCDF4 format. The elaborated procedures to generate forecasts comprise data recalibrating, outlier elimination, Kriging Interpolation and particle tracking and random walk techniques for the mechanisms of diffusion and advection. The solution of these equations reveals the causality between factories emission and the associated air pollution. Further, with the aid of installed real-time flue emission (Total Suspension Emission, TSP) sensors and the mentioned forecasted air pollution map, this study also disclosed the converting mechanism between the TSP and PM2.5/PM10 for different region and industrial characteristics, according to the long-term data observation and calibration. These different time-series qualitative and quantitative data which successfully achieved a causal inference engine in cloud for factory management control in practicable. Once the forecasted air quality for a region is marked as harmful, the correlated factories are notified and asked to suppress its operation and reduces emission in advance.

Keywords: continuous emission monitoring system, total suspension particulates, causal inference, air pollution forecast, IoT

Procedia PDF Downloads 87

Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Pavana Basavakumar, Devadas Bhat

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Diabetes is a disease which often goes undiagnosed until its secondary effects are noticed. Early detection of the disease is necessary to avoid serious consequences which could lead to the death of the patient. Conventional invasive tests for screening of diabetes are mostly painful, time consuming and expensive. There’s also a risk of infection involved, therefore it is very essential to develop non-invasive methods to screen and estimate the level of blood glucose. Extensive research is going on with this perspective, involving various techniques that explore optical, electrical, chemical and thermal properties of the human body that directly or indirectly depend on the blood glucose concentration. Thus, non-invasive blood glucose monitoring has grown into a vast field of research. In this project, an attempt was made to device a prototype for screening of diabetes by measuring electrical impedance of the skin and building a model to predict a patient’s condition based on the measured impedance. The prototype developed, passes a negligible amount of constant current (0.5mA) across a subject’s index finger through tetra polar silver electrodes and measures output voltage across a wide range of frequencies (10 KHz – 4 MHz). The measured voltage is proportional to the impedance of the skin. The impedance was acquired in real-time for further analysis. Study was conducted on over 75 subjects with permission from the institutional ethics committee, along with impedance, subject’s blood glucose values were also noted, using conventional method. Nonlinear regression analysis was performed on the features extracted from the impedance data to obtain a model that predicts blood glucose values for a given set of features. When the predicted data was depicted on Clarke’s Error Grid, only 58% of the values predicted were clinically acceptable. Since the objective of the project was to screen diabetes and not actual estimation of blood glucose, the data was classified into three classes ‘NORMAL FASTING’,’NORMAL POSTPRANDIAL’ and ‘HIGH’ using linear Support Vector Machine (SVM). Classification accuracy obtained was 91.4%. The developed prototype was economical, fast and pain free. Thus, it can be used for mass screening of diabetes.

Keywords: Clarke’s error grid, electrical impedance of skin, linear SVM, nonlinear regression, non-invasive blood glucose monitoring, screening device for diabetes

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Authors: Abebe Taye

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The interest in enhancing the performance of all-solid-state batteries featuring lithium metal anodes as a potential alternative to traditional lithium-ion batteries has prompted exploration into new avenues. A promising strategy involves transforming lithium-ion batteries into hybrid configurations by integrating lithium-ion and lithium-metal solid-state components. This study is focused on achieving stable lithium deposition and advancing the electrochemical capabilities of solid-state lithium hybrid batteries with anodes by incorporating mesocarbon microbeads (MCMBs) blended with silver nanoparticles. To achieve this, mesocarbon microbeads (MCMBs) blended with silver nanoparticles are coated on stainless-steel current collectors. These samples undergo a battery of analyses employing diverse techniques. Surface morphology is studied through scanning electron microscopy (SEM). The electrochemical behavior of the coated samples is evaluated in both half-cell and full-cell setups utilizing an argyrodite-type sulfide electrolyte. The stability of MCMBs in the electrolyte is assessed using electrochemical impedance spectroscopy (EIS). Additional insights into the composition are gleaned through X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and energy-dispersive X-ray spectroscopy (EDS). At an ultra-low N/P ratio of 0.26, stability is upheld for over 100 charge/discharge cycles in half-cells. When applied in a full-cell configuration, the hybrid anode preserves 60.1% of its capacity after 80 cycles at 0.3 C under a low N/P ratio of 0.45. In sharp contrast, the capacity retention of the cell using untreated MCMBs declines to 20.2% after a mere 60 cycles. The introduction of mesocarbon microbeads (MCMBs) combined with silver nanoparticles into the hybrid anode of solid-state lithium batteries substantially elevates their stability and electrochemical performance. This approach ensures consistent lithium deposition and removal, mitigating dendrite growth and the accumulation of inactive lithium. The findings from this investigation hold significant value in elevating the reversibility and energy density of lithium-ion batteries, thereby making noteworthy contributions to the advancement of more efficient energy storage systems.

Keywords: MCMB, lithium metal, hybrid anode, silver nanoparticle, cycling stability

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Authors: Vijay Kumar, Jaspreet Singh, Manoj Wadhwa

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Piezo-resistive pressure sensors were one of the first developed micromechanical system (MEMS) devices and still display a significant growth prompted by the advancements in micromachining techniques and material technology. In MEMS based piezo-resistive pressure sensors, temperature can be considered as the main environmental condition which affects the system performance. The study of the thermal behavior of these sensors is essential to define the parameters that cause the output characteristics to drift. In this work, a study on the effects of temperature and doping concentration in a boron implanted piezoresistor for a silicon-based pressure sensor is discussed. We have optimized the temperature coefficient of resistance (TCR) and temperature coefficient of sensitivity (TCS) values to determine the effect of temperature drift on the sensor performance. To be more precise, in order to reduce the temperature drift, a high doping concentration is needed. And it is well known that the Wheatstone bridge in a pressure sensor is supplied with a constant voltage or a constant current input supply. With a constant voltage supply, the thermal drift can be compensated along with an external compensation circuit, whereas the thermal drift in the constant current supply can be directly compensated by the bridge itself. But it would be beneficial to also compensate the temperature coefficient of piezoresistors so as to further reduce the temperature drift. So, with a current supply, the TCS is dependent on both the TCπ and TCR. As TCπ is a negative quantity and TCR is a positive quantity, it is possible to choose an appropriate doping concentration at which both of them cancel each other. An exact cancellation of TCR and TCπ values is not readily attainable; therefore, an adjustable approach is generally used in practical applications. Thus, one goal of this work has been to better understand the origin of temperature drift in pressure sensor devices so that the temperature effects can be minimized or eliminated. This paper describes the optimum doping levels for the piezoresistors where the TCS of the pressure transducers will be zero due to the cancellation of TCR and TCπ values. Also, the fabrication and characterization of the pressure sensor are carried out. The optimized TCR value obtained for the fabricated die is 2300 ± 100ppm/ᵒC, for which the piezoresistors are implanted at a doping concentration of 5E13 ions/cm³ and the TCS value of -2100ppm/ᵒC is achieved. Therefore, the desired TCR and TCS value is achieved, which are approximately equal to each other, so the thermal effects are considerably reduced. Finally, we have calculated the effect of temperature and doping concentration on the output characteristics of the sensor. This study allows us to predict the sensor behavior against temperature and to minimize this effect by optimizing the doping concentration.

Keywords: piezo-resistive, pressure sensor, doping concentration, TCR, TCS

Procedia PDF Downloads 182

Authors: Saima Shaikh, Andre Brown, Wallace Enegbuma

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Earthquakes strike both developed and developing countries, causing tremendous damage and the loss of lives of millions of people, mainly due to the collapsing of buildings, particularly in poorer countries. Despite the socio-economic and technological restrictions, the poorer countries have adopted proven and established housing-strengthening techniques from affluent countries. Rural communities are aware of the earthquake-strengthening mechanisms for improving housing resilience, but owing to socio-economic and technological constraints, the seismic guidelines are rarely implemented, resulting in informal construction practice. Unregistered skilled laborers make substantial contributions to the informal construction sector, particularly in rural areas where knowledge is scarce. Laborers employ their local expertise in house construction; however, owing to a lack of seismic expertise in safe building procedures, the authorities' regulated seismic norms are not applied. From the perspective of seismic knowledge transformation in safe buildings practices, the study focuses on the feasibility of seismic guidelines implementation. The study firstly employs a literature review of massive-scale reconstruction after the 2005 earthquake in rural Pakistan. The 2005-earthquake damaged over 400,000 homes, killed 70,000 people and displaced 2.8 million people. The research subsequently corroborated the pragmatic approach using questionnaire field survey among the rural people in 2005-earthquake affected areas. Using the literature and the questionnaire survey, the research analyzing people's perspectives on technical acceptability, financial restrictions, and socioeconomic viability and examines the effectiveness of seismic knowledge transfer in safe buildings practices. The findings support the creation of a knowledge transfer framework in disaster mitigation and recovery planning, assisting rural communities and builders in minimising losses and improving response and recovery, as well as improving housing resilience and lowering vulnerabilities. Finally, certain conclusions are obtained in order to continue the resilience research. The research can be further applied in rural areas of developing countries having similar construction practices.

Keywords: earthquakes, knowledge transfer, resilience, informal construction practices

Procedia PDF Downloads 173

Authors: Virag Vass, Ilona Bereczki, Erzsebet Szabo, Nora Debreczeni, Aniko Borbas, Pal Herczegh, Arpad Tosaki

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Hydrogen sulfide (H₂S) is a toxic gas, but it is produced by certain tissues in a small quantity. According to earlier studies, ibuprofen and H₂S has a protective effect against damaging heart tissue caused by ischemia-reperfusion. Recently, we have been investigating the effect of a new water-soluble H₂S releasing ibuprofen molecule administered after artificially generated ischemia-reperfusion on isolated rat hearts. The H₂S releasing property of the new ibuprofen derivative was investigated in vitro in medium derived from heart endothelial cell isolation at two concentrations. The ex vivo examinations were carried out on rat hearts. Rats were anesthetized with an intraperitoneal injection of ketamine, xylazine, and heparin. After thoracotomy, hearts were excised and placed into ice-cold perfusion buffer. Perfusion of hearts was conducted in Langendorff mode via the cannulated aorta. In our experiments, we studied the dose-effect of the H₂S releasing molecule in Langendorff-perfused hearts with the application of gradually increasing concentration of the compound (0- 20 µM). The H₂S releasing ibuprofen derivative was applied before the ischemia for 10 minutes. H₂S concentration was measured with an H₂S detecting electrochemical sensor from the coronary effluent solution. The 10 µM concentration was chosen for further experiments when the treatment with this solution was occurred after the ischemia. The release of H₂S is occurred by the hydrolyzing enzymes that are present in the heart endothelial cells. The protective effect of the new H₂S releasing ibuprofen molecule can be confirmed by the infarct sizes of hearts using the Triphenyl-tetrazolium chloride (TTC) staining method. Furthermore, we aimed to define the effect of the H₂S releasing ibuprofen derivative on autophagic and apoptotic processes in damaged hearts after investigating the molecular markers of these events by western blotting and immunohistochemistry techniques. Our further studies will include the examination of LC3I/II, p62, Beclin1, caspase-3, and other apoptotic molecules. We hope that confirming the protective effect of new H₂S releasing ibuprofen molecule will open a new possibility for the development of more effective cardioprotective agents with exerting fewer side effects. Acknowledgment: This study was supported by the grants of NKFIH- K-124719 and the European Union and the State of Hungary co- financed by the European Social Fund in the framework of GINOP- 2.3.2-15-2016-00043.

Keywords: autophagy, hydrogen sulfide, ibuprofen, ischemia, reperfusion

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Authors: Jérôme de Reffye, Marc Antoni

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Following the development of the ERTMS system, we think it is interesting to develop another software-based track circuit system which would fit secondary railway lines with an easy-to-work implementation and a low sensitivity to rail-wheel impedance variations. We called this track circuit 'Track Railway by Automatic Circuits.' To be internationally implemented, this system must not have any mechanical component and must be compatible with existing track circuit systems. For example, the system is independent from the French 'Joints Isolants Collés' that isolate track sections from one another, and it is equally independent from component used in Germany called 'Counting Axles,' in French 'compteur d’essieux.' This track circuit is fully interoperable. Such universality is obtained by replacing the train detection mechanical system with a space-time filtering of train position. The various track sections are defined by the frequency of a continuous signal. The set of frequencies related to the track sections is a set of orthogonal functions in a Hilbert Space. Thus the failure probability of track sections separation is precisely calculated on the basis of signal-to-noise ratio. SNR is a function of the level of traction current conducted by rails. This is the reason why we developed a very powerful algorithm to reject noise and jamming to obtain an SNR compatible with the precision required for the track circuit and SIL 4 level. The SIL 4 level is thus reachable by an adjustment of the set of orthogonal functions. Our major contributions to railway engineering signalling science are i) Train space localization is precisely defined by a calibration system. The operation bypasses the GSM-R radio system of the ERTMS system. Moreover, the track circuit is naturally protected against radio-type jammers. After the calibration operation, the track circuit is autonomous. ii) A mathematical topology adapted to train space localization by following the train through a linear time filtering of the received signal. Track sections are numerically defined and can be modified with a software update. The system was numerically simulated, and results were beyond our expectations. We achieved a precision of one meter. Rail-ground and rail-wheel impedance sensitivity analysis gave excellent results. Results are now complete and ready to be published. This work was initialised as a research project of the French Railways developed by the Pi-Ramses Company under SNCF contract and required five years to obtain the results. This track circuit is already at Level 3 of the ERTMS system, and it will be much cheaper to implement and to work. The traffic regulation is based on variable length track sections. As the traffic growths, the maximum speed is reduced, and the track section lengths are decreasing. It is possible if the elementary track section is correctly defined for the minimum speed and if every track section is able to emit with variable frequencies.

Keywords: track section, track circuits, space-time crossing, adaptive track section, automatic railway signalling

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Authors: M. Atef Gabr, M. H. Abdel Latif, Ramadan El Gamsy

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Sandwich panels comprise a thick, light-weight plastic foam such as polyurethane (PU) sandwiched between two relatively thin faces. One or both faces may be flat, lightly profiled or fully profiled. Until recently sandwich panel construction in Egypt has been widely used in cold-storage buildings, cold trucks, prefabricated buildings and insulation in construction. Recently new techniques are used in mass production of Sandwich Materials such as Reaction Injection Molding (RIM) and Vacuum bagging technique. However, in recent times their use has increased significantly due to their widespread structural applications in building systems. Structural sandwich panels generally used in Egypt comprise polyurethane foam core and thinner (0.42 mm) and high strength about 550 MPa (yield strength) flat steel faces bonded together using separate adhesives and By RIM technique. In this paper, we will use a new technique in sandwich panel preparation by using different face sheet materials in combination with polyurethane foam to form sandwich panel structures. Previously, PU Foam core with same thin 2 faces material was used, but in this work, we use different face materials and thicknesses for the upper face sheet such as Galvanized steel sheets (G.S),Aluminum sheets (Al),Fiberglass sheets (F.G) and Aluminum-Rubber composite sheets (Al/R) with polyurethane foam core 10 mm thickness and 45 Kg/m3 Density and Galvanized steel as lower face sheet. Using Aluminum-Rubber composite sheets as face sheet is considered a hybrid composite sandwich panel which is built by Hand-Layup technique by using PU glue as adhesive. This modification increases the benefits of the face sheet that will withstand different working environments with relatively small increase in its weight and will be useful in several applications. In this work, a 3-point bending test is used assistant professor to measure the most important factor in sandwich materials that is strength to weight ratio(STW) for different combinations of sandwich structures and make a comparison to study the effect of changing the face sheet material on the mechanical behavior of PU sandwich material. Also, the density of the different prepared sandwich materials will be measured to obtain the specific bending strength.

Keywords: hybrid sandwich panel, mechanical behavior, PU foam, sandwich panel, 3-point bending, flexural strength

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Authors: Sayed Mansour, Mohammad Aldoasri, Nagib Elmarzugi, Nadia A. Al-Mouallimi

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The archaeological large stone used in construction of ancient Pharaonic tombs, temples, obelisks and other sculptures, always subject to physicomechanical deterioration and destructive forces, leading to their partial or total broken. The task of reassembling this type of artifact represent a big challenge for the conservators. Recently, the researchers are turning to new technologies to improve the properties of traditional adhesive materials and techniques used in re-assembly of broken large stone. The epoxy resins are used extensively in stone conservation and re-assembly of broken stone because of their outstanding mechanical properties. The introduction of nanoparticles to polymeric adhesives at low percentages may lead to substantial improvements of their mechanical performances in structural joints and large objects. The aim of this study is to evaluate the effectiveness of clay nanoparticles in enhancing the performances of epoxy adhesives used in re-assembly of archaeological massive stone by adding proper amounts of those nanoparticles. The nanoparticles reinforced epoxy nanocomposite was prepared by direct melt mixing with a nanoparticles content of 3% (w/v), and then mould forming in the form of rectangular samples, and used as adhesive for experimental stone samples. Scanning electron microscopy (SEM) was employed to investigate the morphology of the prepared nanocomposites, and the distribution of nanoparticles inside the composites. The stability and efficiency of the prepared epoxy-nanocomposites and stone block assemblies with new formulated adhesives were tested by aging artificially the samples under different environmental conditions. The effect of incorporating clay nanoparticles on the mechanical properties of epoxy adhesives was evaluated comparatively before and after aging by measuring the tensile, compressive, and Elongation strength tests. The morphological studies revealed that the mixture process between epoxy and nanoparticles has succeeded with a relatively homogeneous morphology and good dispersion in low nano-particles loadings in epoxy matrix was obtained. The results show that the epoxy-clay nanocomposites exhibited superior tensile, compressive, and Elongation strength. Moreover, a marked improvement of the mechanical properties of stone joints increased in all states by adding nano-clay to epoxy in comparison with pure epoxy resin.

Keywords: epoxy resins, nanocomposites, clay nanoparticles, re-assembly, archaeological massive stones, mechanical properties

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Authors: Zhen Cao, Yu Zhu, Junxue Fu

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Immunoassay, i.e., antigen-antibody reaction, is crucial for disease diagnostics. To achieve the adequate signal of the antigen protein detection, a large amount of sample and long incubation time is needed. However, the amount of protein is usually small at the early stage, which makes it difficult to detect. Unlike cells and DNAs, no valid chemical method exists for protein amplification. Thus, an alternative way to improve the signal is through particle manipulation techniques to concentrate proteins, among which dielectrophoresis (DEP) is an effective one. DEP is a technique that concentrates particles to the designated region through a force created by the gradient in a non-uniform electric field. Since DEP force is proportional to the cube of particle size and square of electric field gradient, it is relatively easy to capture larger particles such as cells. For smaller ones like proteins, a super high gradient is then required. In this work, three-dimensional Ag/SiO2 nanorods arrays, fabricated by an easy physical vapor deposition technique called as oblique angle deposition, have been integrated with a DEP device and created the field gradient as high as of 2.6×10²⁴ V²/m³. The nanorods based DEP device is able to enrich bovine serum albumin (BSA) protein by 1800-fold and the rate has reached 180-fold/s when only applying 5 V electric potential. Based on the above nanorods integrated DEP platform, an immunoassay of mouse immunoglobulin G (IgG) proteins has been performed. Briefly, specific antibodies are immobilized onto nanorods, then IgG proteins are concentrated and captured, and finally, the signal from fluorescence-labelled antibodies are detected. The limit of detection (LoD) is measured as 275.3 fg/mL (~1.8 fM), which is a 20,000-fold enhancement compared with identical assays performed on blank glass plates. Further, prostate-specific antigen (PSA), which is a cancer biomarker for diagnosis of prostate cancer after radical prostatectomy, is also quantified with a LoD as low as 2.6 pg/mL. The time to signal saturation has been significantly reduced to one minute. In summary, together with an easy nanorod fabrication and integration method, this nanorods based DEP platform has demonstrated highly sensitive immunoassay performance and thus poses great potentials in applications for early point-of-care diagnostics.

Keywords: dielectrophoresis, immunoassay, oblique angle deposition, protein concentration

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Authors: Hala Barakat

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Architecture plays a crucial role in the colonization and organization of spaces, as well as the preservation of cultures and history. As a result of 70 years of occupation, Palestinian land, culture, and history are endangered today. The government of Israel has used architecture to strangulate Palestinians out and seize their land. The occupation has managed to fragment the West Bank and cause sensible scars on the landscape by creating obstacles, barriers, watchtowers, checkpoints, walls, apartheid roads, border devices, and illegal settlements to unjustly claim land from its indigenous population. The apartheid architecture has divided the Palestinian social and urban fabric into pieces, similarly to the Bantustans. The architectural techniques and methods used by the occupation are evidence of prejudice, and while the illegal settlements remain to be condemned by the United Nations, little is being done to officially end this apartheid. Illegal settlements range in scale from individual units to established cities and house more than 60,000 Israeli settlers that immigrated from all over Europe and the United States. Often architecture by Israel is being directed towards expressing ideologies and serving as evidence of its political agenda. More than 78% of what was granted to Palestine after the development of the Green Line in 1948 is under Israeli occupation today. This project aims to map the illegal architecture as a criticism of governmental agendas in the West Bank and Historic Palestinian land. The paper will also discuss the resistance to the newly developed plan for the last Arab village in Jerusalem, Lifta. The illegal architecture has isolated Palestinians from each other and installed obstacles to control their movement. The architecture of occupation has no ethical or humane logic but rather entirely political, administrative, and it should not be left for the silenced architecture to tell the story. Architecture is not being used as a connecting device but rather a way to implement political injustice and spatial oppression. By narrating stories of the architecture of occupation, we can highlight the spatial injustice of the complex apartheid infrastructure. The Israeli government has managed to intoxicate architecture to serve as a divider between cultural groups, allowing the unlawful and unethical architecture to define its culture and values. As architects and designers, the roles we play in the development of illegal settlements must align with the spatial ethics we practice. Most importantly, our profession is not performing architecturally when we design a house with a particular roof color to ensure it would not be mistaken with a Palestinian house and be attacked accidentally.

Keywords: apartheid, illegal architecture, occupation, politics

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Authors: Mohamed Abdelkareem

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Remote sensing data contributed on predicting the prospective areas of water resources. Integration of microwave and multispectral data along with climatic, hydrologic, and geological data has been used here. In this article, Sentinel-2, Landsat-8 Operational Land Imager (OLI), Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), and Advanced Land Observing Satellite (ALOS) Phased Array Type L‐band Synthetic Aperture Radar (PALSAR) data were utilized to identify the geological, hydrologic and structural features of Wadi Asyuti which represents a defunct tributary of the Nile basin, in the eastern Sahara. The image transformation of Sentinel-2 and Landsat-8 data allowed characterizing the different varieties of rock units. Integration of microwave remotely-sensed data and GIS techniques provided information on physical characteristics of catchments and rainfall zones that are of a crucial role for mapping groundwater prospective zones. A fused Landsat-8 OLI and ALOS/PALSAR data improved the structural elements that difficult to reveal using optical data. Lineament extraction and interpretation indicated that the area is clearly shaped by the NE-SW graben that is cut by NW-SE trend. Such structures allowed the accumulation of thick sediments in the downstream area. Processing of recent OLI data acquired on March 15, 2014, verified the flood potential maps and offered the opportunity to extract the extent of the flooding zone of the recent flash flood event (March 9, 2014), as well as revealed infiltration characteristics. Several layers including geology, slope, topography, drainage density, lineament density, soil characteristics, rainfall, and morphometric characteristics were combined after assigning a weight for each using a GIS-based knowledge-driven approach. The results revealed that the predicted groundwater potential zones (GPZs) can be arranged into six distinctive groups, depending on their probability for groundwater, namely very low, low, moderate, high very, high, and excellent. Field and well data validated the delineated zones.

Keywords: GIS, remote sensing, groundwater, Egypt

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Authors: B. Mendonça, D. Sandwell

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The Metropolitan Region of Sao Paulo (MRSP) has suffered from serious water scarcity. Consequently, the most convenient solution has been building wells to extract groundwater from local aquifers. However, it requires constant vigilance to prevent over extraction and future events that can pose serious threat to the population, such as subsidence. Radar imaging techniques (InSAR) have allowed continuous investigation of such phenomena. The analysis of data in the present study consists of 23 SAR images dated from October 2007 to March 2011, obtained by the ALOS-1 spacecraft. Data processing was made with the software GMTSAR, by using the InSAR technique to create pairs of interferograms with ground displacement during different time spans. First results show a correlation between the location of 102 wells registered in 2009 and signals of ground displacement equal or lower than -90 millimeters (mm) in the region. The longest time span interferogram obtained dates from October 2007 to March 2010. As a result, from that interferogram, it was possible to detect the average velocity of displacement in millimeters per year (mm/y), and which areas strong signals have persisted in the MRSP. Four specific areas with signals of subsidence of 28 mm/y to 40 mm/y were chosen to investigate the phenomenon: Guarulhos (Sao Paulo International Airport), the Greater Sao Paulo, Itaquera and Sao Caetano do Sul. The coverage area of the signals was between 0.6 km and 1.65 km of length. All areas are located above a sedimentary type of aquifer. Itaquera and Sao Caetano do Sul showed signals varying from 28 mm/y to 32 mm/y. On the other hand, the places most likely to be suffering from stronger subsidence are the ones in the Greater Sao Paulo and Guarulhos, right beside the International Airport of Sao Paulo. The rate of displacement observed in both regions goes from 35 mm/y to 40 mm/y. Previous investigations of the water use at the International Airport highlight the risks of excessive water extraction that was being done through 9 deep wells. Therefore, it is affirmed that subsidence events are likely to occur and to cause serious damage in the area. This study could show a situation that has not been explored with proper importance in the city, given its social and economic consequences. Since the data were only available until 2011, the question that remains is if the situation still persists. It could be reaffirmed, however, a scenario of risk at the International Airport of Sao Paulo that needs further investigation.

Keywords: ground subsidence, Interferometric Satellite Aperture Radar (InSAR), metropolitan region of Sao Paulo, water extraction

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Authors: Christoph Lohr, Hanna Wund, Peter Elsner, Kay André Weidenmann

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Injection molding is one of the most commonly used techniques in the industrial polymer processing. In the conventional process of injection molding, the liquid polymer is injected into the cavity of the mold, where the polymer directly starts hardening at the cooled walls. To compensate the shrinkage, which is caused predominantly by the immediate cooling, holding pressure is applied. Through that whole process, residual stresses are produced by the temperature difference of the polymer melt and the injection mold and the relocation of the polymer chains, which were oriented by the high process pressures and injection speeds. These residual stresses often weaken or change the structural behavior of the parts or lead to deformation of components. One solution to reduce the residual stresses is the use of variothermal processing. Hereby the mold is heated – i.e. near/over the glass transition temperature of the polymer – the polymer is injected and before opening the mold and ejecting the part the mold is cooled. For the next cycle, the mold gets heated again and the procedure repeats. The rapid heating and cooling of the mold are realized indirectly by convection of heated and cooled liquid (here: water) which is pumped through fluid channels underneath the mold surface. In this paper, the influences of variothermal processing on the residual stresses are analyzed with samples in a larger scale (500 mm x 250 mm x 4 mm). In addition, the influence on functional elements, such as abrupt changes in wall thickness, bosses, and ribs, on the residual stress is examined. Therefore the polycarbonate samples are produced by variothermal and isothermal processing. The melt is injected into a heated mold, which has in our case a temperature varying between 70 °C and 160 °C. After the filling of the cavity, the closed mold is cooled down varying from 70 °C to 100 °C. The pressure and temperature inside the mold are monitored and evaluated with cavity sensors. The residual stresses of the produced samples are illustrated by birefringence where the effect on the refractive index on the polymer under stress is used. The colorful spectrum can be uncovered by placing the sample between a polarized light source and a second polarization filter. To show the achievement and processing effects on the reduction of residual stress the birefringence images of the isothermal and variothermal produced samples are compared and evaluated. In this comparison to the variothermal produced samples have a lower amount of maxima of each color spectrum than the isothermal produced samples, which concludes that the residual stress of the variothermal produced samples is lower.

Keywords: birefringence, injection molding, polycarbonate, residual stress, variothermal processing

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Authors: Pradip Kumar Das

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As the entire world continues its rapid move towards industrialization, it has seriously threatened mankind’s ability to maintain an ecological balance. Geographical and natural forces have a significant influence on the location of industries. Industrialization is the foundation stone of the development of any country, while the unplanned industrialization and discharge of waste by industries is the cause of environmental pollution. There is growing degree of awareness and concern globally among nations about environmental degradation or pollution. Environmental resources endowed by the gift of nature and not manmade are invaluable natural resources of a country like India. Any developmental activity is directly related to natural and environmental resources. Economic development without environmental considerations brings about environmental crises and damages the quality of life of present, as well as future generation. As corporate sectors in the global market, especially in India, are becoming anxious about environmental degradation, naturally more and more emphasis will be ascribed to how environment-friendly the outcomes are. Maintaining accounts of such environmental and natural resources in the country has become more urgent. Moreover, international awareness and acceptance of the importance of environmental issues has motivated the development of a branch of accounting called “Environmental Accounting”. Environmental accounting attempts to detect and focus the resources consumed and the costs rendered by an industrial unit to the environment. For the sustainable development of mankind, a healthy environment is indispensable. Gradually, therefore, in many countries including India, environment matters are being given top most priority. Accounting and disclosure of environmental matters have been increasingly manifesting as an important dimension of corporate accounting and reporting practices. But, as conventional accounting deals with mainly non-living things, the formulation of valuation, and measurement and accounting techniques for incorporating environment-related matters in the corporate financial statement sometimes creates problems for the accountant. In the light of this situation, the conceptual analysis of the study is concerned with the rationale of environmental accounting on the economy and society as a whole, and focuses the failures of the traditional accounting system. A modest attempt has been made to throw light on the environmental awareness in developing nations like India and discuss the problems associated with the implementation of environmental accounting. The conceptual study also reflects that despite different anomalies, environmental accounting is becoming an increasing important aspect of the accounting agenda within the corporate sector in India. Lastly, a conclusion, along with recommendations, has been given to overcome the situation.

Keywords: environmental accounting, environmental degradation, environmental management, environmental resources

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Authors: Nji Queenta Ngum, Mwanza Mulunda

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Introduction Most African research reports on the frequent aflatoxin contamination of various foodstuffs, with researchers rarely specifying which of the Aspergillus species are present in these commodities. Numerous research works provide evidence of the ability of fungi to grow, thrive, and interact with other crop species and focus on the fact that these processes are largely affected by climatic variables. South Africa is a water-stressed country with high spatio-temporal rainfall variability; moreover, temperatures have been projected to rise at a rate twice the global rate. This weather pattern change may lead to crop stress encouraging mold contamination with subsequent mycotoxin production. In this study, the biodiversity and distribution of Aspergillus species with their corresponding toxins in maize from six distinct maize producing regions with different weather patterns in South Africa were investigated. Materials And Methods By applying cultural and molecular methods, a total of 1028 maize samples from six distinct agro-climatic regions were examined for contamination by the Aspergillus species while the high performance liquid chromatography (HPLC) method was applied to analyse the level of contamination by aflatoxins. Results About 30% of the overall maize samples were contaminated by at least one Aspergillus species. Less than 30% (28.95%) of the 228 isolates subjected to the aflatoxigenic test was found to possess at least one of the aflatoxin biosynthetic genes. Furthermore, almost 20% were found to be contaminated with aflatoxins, with mean total aflatoxin concentration levels of 64.17 ppb. Amongst the contaminated samples, 59.02% had mean total aflatoxin concentration levels above the SA regulatory limit of 20ppb for animals and 10 for human consumption. Conclusion In this study, climate variables (rainfall reduction) were found to significantly (p<0.001) influence the occurrence of the Aspergillus species (especially Aspergillus fumigatus) and the production of aflatoxin in South Africa commercial maize by maize variety, year of cultivation as well as the agro-climatic region in which the maize is cultivated. This included, amongst others, a reduction in the average annual rainfall of the preceding year to about 21.27 mm, and, as opposed to other regions whose average maximum rainfall ranged between 37.24 – 44.1 mm, resulted in a significant increase in the aflatoxin contamination of maize.

Keywords: aspergillus species, aflatoxins, diversity, drought, food safety, HPLC and PCR techniques

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Authors: A. O. Abulibdeh

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The aim of this study is to investigate and compare the changes in the Land Surface Temperature (LST) as a function of urbanization, particularly land use/land cover changes, in three cities in the UAE, mainly Abu Dhabi, Dubai, and Al Ain. The scale of this assessment will be at the macro- and micro-levels. At the macro-level, a comparative assessment will take place to compare between the four cities in the UAE. At the micro-level, the study will compare between the effects of different land use/land cover on the LST. This will provide a clear and quantitative city-specific information related to the relationship between urbanization and local spatial intra-urban LST variation in three cities in the UAE. The main objectives of this study are 1) to investigate the development of LST on the macro- and micro-level between and in three cities in the UAE over two decades time period, 2) to examine the impact of different types of land use/land cover on the spatial distribution of LST. Because these three cities are facing harsh arid climate, it is hypothesized that (1) urbanization is affecting and connected to the spatial changes in LST; (2) different land use/land cover have different impact on the LST; and (3) changes in spatial configuration of land use and vegetation concentration over time would control urban microclimate on a city scale and control macroclimate on the country scale. This study will be carried out over a 20-year period (1996-2016) and throughout the whole year. The study will compare between two distinct periods with different thermal characteristics which are the cool/cold period from November to March and warm/hot period between April and October. The best practice research method for this topic is to use remote sensing data to target different aspects of natural and anthropogenic systems impacts. The project will follow classical remote sensing and mapping techniques to investigate the impact of urbanization, mainly changes in land use/land cover, on LST. The investigation in this study will be performed in two stages. Stage one remote sensing data will be used to investigate the impact of urbanization on LST on a macroclimate level where the LST and Urban Heat Island (UHI) will be compared in the three cities using data from the past two decades. Stage two will investigate the impact on microclimate scale by investigating the LST and UHI using a particular land use/land cover type. In both stages, an LST and urban land cover maps will be generated over the study area. The outcome of this study should represent an important contribution to recent urban climate studies, particularly in the UAE. Based on the aim and objectives of this study, the expected outcomes are as follow: i) to determine the increase or decrease of LST as a result of urbanization in these four cities, ii) to determine the effect of different land uses/land covers on increasing or decreasing the LST.

Keywords: land use/land cover, global warming, land surface temperature, remote sensing

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Authors: Kai Zhang, Xi Jiang

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Effect of fuel variabilities on premixed combustion of bio-syngas mixtures is of great importance in bio-syngas utilisation. The uncertainties of concentrations of fuel constituents such as H2, CO and CH4 may lead to unpredictable combustion performances, combustion instabilities and hot spots which may deteriorate and damage the combustion hardware. Numerical modelling and simulations can assist in understanding the behaviour of bio-syngas combustion with pre-defined species concentrations, while the evaluation of variabilities of concentrations is expensive. To be more specific, questions such as ‘what is the burning velocity of bio-syngas at specific equivalence ratio?’ have been answered either experimentally or numerically, while questions such as ‘what is the likelihood of burning velocity when precise concentrations of bio-syngas compositions are unknown, but the concentration ranges are pre-described?’ have not yet been answered. Uncertainty quantification (UQ) methods can be used to tackle such questions and assess the effects of fuel compositions. An efficient probabilistic UQ method based on Polynomial Chaos Expansion (PCE) techniques is employed in this study. The method relies on representing random variables (combustion performances) with orthogonal polynomials such as Legendre or Gaussian polynomials. The constructed PCE via Galerkin Projection provides easy access to global sensitivities such as main, joint and total Sobol indices. In this study, impacts of fuel compositions on combustion (adiabatic flame temperature and laminar flame speed) of bio-syngas fuel mixtures are presented invoking this PCE technique at several equivalence ratios. High-pressure effects on bio-syngas combustion instability are obtained using detailed chemical mechanism - the San Diego Mechanism. Guidance on reducing combustion instability from upstream biomass gasification process is provided by quantifying the significant contributions of composition variations to variance of physicochemical properties of bio-syngas combustion. It was found that flame speed is very sensitive to hydrogen variability in bio-syngas, and reducing hydrogen uncertainty from upstream biomass gasification processes can greatly reduce bio-syngas combustion instability. Variation of methane concentration, although thought to be important, has limited impacts on laminar flame instabilities especially for lean combustion. Further studies on the UQ of percentage concentration of hydrogen in bio-syngas can be conducted to guide the safer use of bio-syngas.

Keywords: bio-syngas combustion, clean energy utilisation, fuel variability, PCE, targeted uncertainty reduction, uncertainty quantification

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Authors: Hoda Rahmani, Gary Weckman

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The body of research examining the integration of drones into various industries is expanding rapidly. Despite progress made in addressing the cybersecurity concerns for commercial drones, knowledge deficits remain in determining potential occupational hazards and risks of drone use to employees’ well-being and health in the workplace. This creates difficulty in identifying key approaches to risk mitigation strategies and thus reflects the need for raising awareness among employers, safety professionals, and policymakers about workplace drone-related accidents. The purpose of this study is to investigate the prevalence of and possible risk factors for drone-related mishaps by comparing the application of drones in construction with manufacturing industries. The chief reason for considering these specific sectors is to ascertain whether there exists any significant difference between indoor and outdoor flights since most construction sites use drones outside and vice versa. Therefore, the current research seeks to examine the causes and patterns of workplace drone-related mishaps and suggest possible ergonomic interventions through data collection. Potential ergonomic practices to mitigate hazards associated with flying drones could include providing operators with professional pieces of training, conducting a risk analysis, and promoting the use of personal protective equipment. For the purpose of data analysis, two data mining techniques, the random forest and association rule mining algorithms, will be performed to find meaningful associations and trends in data as well as influential features that have an impact on the occurrence of drone-related accidents in construction and manufacturing sectors. In addition, Spearman’s correlation and chi-square tests will be used to measure the possible correlation between different variables. Indeed, by recognizing risks and hazards, occupational safety stakeholders will be able to pursue data-driven and evidence-based policy change with the aim of reducing drone mishaps, increasing productivity, creating a safer work environment, and extending human performance in safe and fulfilling ways. This research study was supported by the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant #T42OH008432.

Keywords: commercial drones, ergonomic interventions, occupational safety, pattern recognition

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Authors: Martín Pratto Burgos

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The Engineering School of the University of the Republic in Uruguay offers an Introductory Mathematical Course from the second semester of 2019. This course has been designed to assist students in preparing themselves for math courses that are essential for Engineering Degrees, namely Math1, Math2, and Math3 in this research. The research proposes to build a model that can accurately predict the student's activity and academic progress based on their performance in the three essential Mathematical courses. Additionally, there is a need for a model that can forecast the incidence of the Introductory Mathematical Course in the three essential courses approval during the first academic year. The techniques used are Principal Component Analysis and predictive modelling using the Generalised Linear Model. The dataset includes information from 5135 engineering students and 12 different characteristics based on activity and course performance. Two models are created for a type of data that follows a binomial distribution using the R programming language. Model 1 is based on a variable's p-value being less than 0.05, and Model 2 uses the stepAIC function to remove variables and get the lowest AIC score. After using Principal Component Analysis, the main components represented in the y-axis are the approval of the Introductory Mathematical Course, and the x-axis is the approval of Math1 and Math2 courses as well as student activity three years after taking the Introductory Mathematical Course. Model 2, which considered student’s activity, performed the best with an AUC of 0.81 and an accuracy of 84%. According to Model 2, the student's engagement in school activities will continue for three years after the approval of the Introductory Mathematical Course. This is because they have successfully completed the Math1 and Math2 courses. Passing the Math3 course does not have any effect on the student’s activity. Concerning academic progress, the best fit is Model 1. It has an AUC of 0.56 and an accuracy rate of 91%. The model says that if the student passes the three first-year courses, they will progress according to the timeline set by the curriculum. Both models show that the Introductory Mathematical Course does not directly affect the student’s activity and academic progress. The best model to explain the impact of the Introductory Mathematical Course on the three first-year courses was Model 1. It has an AUC of 0.76 and 98% accuracy. The model shows that if students pass the Introductory Mathematical Course, it will help them to pass Math1 and Math2 courses without affecting their performance on the Math3 course. Matching the three predictive models, if students pass Math1 and Math2 courses, they will stay active for three years after taking the Introductory Mathematical Course, and also, they will continue following the recommended engineering curriculum. Additionally, the Introductory Mathematical Course helps students to pass Math1 and Math2 when they start Engineering School. Models obtained in the research don't consider the time students took to pass the three Math courses, but they can successfully assess courses in the university curriculum.

Keywords: machine-learning, engineering, university, education, computational models

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Authors: Syeda Fatima Manzelat

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This chapter investigates the biodeterioration of parks in the UK caused by lichens, focusing on Campbell Park and Great Linford Manor Park in Milton Keynes. The study first isolates and identifies potent biodeteriogens responsible for potential biodeterioration in these parks, enumerating and recording different classes and genera of lichens known for their biodeteriorative properties. It then examines the implications of lichens on biodeterioration at historic sites within these parks, considering impacts on historic structures, the environment, and associated health risks. Conservation strategies and preventive measures are discussed before concluding.Lichens, characterized by their symbiotic association between a fungus and an alga, thrive on various surfaces including building materials, soil, rock, wood, and trees. The fungal component provides structure and protection, while the algal partner performs photosynthesis. Lichens collected from the park sites, such as Xanthoria, Cladonia, and Arthonia, were observed affecting the historic walls, objects, and trees. Their biodeteriorative impacts were visible to the naked eye, contributing to aesthetic and structural damage. The study highlights the role of lichens as bioindicators of pollution, sensitive to changes in air quality. The presence and diversity of lichens provide insights into the air quality and pollution levels in the parks. However, lichens also pose health risks, with certain species causing respiratory issues, allergies, skin irritation, and other toxic effects in humans and animals. Conservation strategies discussed include regular monitoring, biological and chemical control methods, physical removal, and preventive cleaning. The study emphasizes the importance of a multifaceted, multidisciplinary approach to managing lichen-induced biodeterioration. Future management practices could involve advanced techniques such as eco-friendly biocides and self-cleaning materials to effectively control lichen growth and preserve historic structures. In conclusion, this chapter underscores the dual role of lichens as agents of biodeterioration and indicators of environmental quality. Comprehensive conservation management approaches, encompassing monitoring, targeted interventions, and advanced conservation methods, are essential for preserving the historic and natural integrity of parks like Campbell Park and Great Linford Manor Park.

Keywords: biodeterioration, historic parks, algae, UK

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Authors: L. Hoade, O. O. Onafowokan, K. Anderson, G. E. Bartlett, E. D. Fern, M. R. Norton, R. G. Middleton

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Aims: Femoroacetabular impingement (FAI) was first recognised as a potential driver for hip pain at the turn of the last millennium. While there is an increasing trend towards surgical management of FAI by arthroscopic means, open surgical hip dislocation and debridement (SHD) remains the Gold Standard of care in terms of reported outcome measures. (1) Long-term functional and survivorship outcomes of SHD as a treatment for FAI are yet to be sufficiently reported in the literature. This study sets out to help address this imbalance. Methods: We undertook a retrospective review of our institutional database for all patients who underwent SHD for FAI between January 2003 and December 2008. A total of 223 patients (241 hips) were identified and underwent a ten year review with a standardised radiograph and patient-reported outcome measures questionnaire. The primary outcome measure of interest was survivorship, defined as progression to total hip arthroplasty (THA). Negative predictive factors were analysed. Secondary outcome measures of interest were survivorship to further (non-arthroplasty) surgery, functional outcomes as reflected by patient reported outcome measure scores (PROMS) scores, and whether a learning curve could be identified. Results: The final cohort consisted of 131 females and 110 males, with a mean age of 34 years. There was an overall native hip joint survival rate of 85.4% at ten years. Those who underwent a THA were significantly older at initial surgery, had radiographic evidence of preoperative osteoarthritis and pre- and post-operative acetabular undercoverage. In those whom had not progressed to THA, the average Non-arthritic Hip Score and Oxford Hip Score at ten year follow-up were 72.3% and 36/48, respectively, and 84% still deemed their surgery worthwhile. A learning curve was found to exist that was predicated on case selection rather than surgical technique. Conclusion: This is only the second study to evaluate the long-term outcomes (beyond ten years) of SHD for FAI and the first outside the originating centre. Our results suggest that, with correct patient selection, this remains an operation with worthwhile outcomes at ten years. How the results of open surgery compared to those of arthroscopy remains to be answered. While these results precede the advent of collison software modelling tools, this data helps set a benchmark for future comparison of other techniques effectiveness at the ten year mark.

Keywords: femoroacetabular impingement, hip pain, surgical hip dislocation, hip debridement

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Authors: Georges Ghazi, Magali Gelhaye, Ruxandra Botez

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Over the years, the Flight Management System (FMS) has experienced a continuous improvement of its many features, to the point of becoming the pilot’s primary interface for flight planning operation on the airplane. With the assistance of the FMS, the concept of distance and time has been completely revolutionized, providing the crew members with the determination of the optimized route (or flight plan) from the departure airport to the arrival airport. To accomplish this function, the FMS needs an accurate Aircraft Performance Model (APM) of the aircraft. In general, APMs that equipped most modern FMSs are established before the entry into service of an individual aircraft, and results from the combination of a set of ordinary differential equations and a set of performance databases. Unfortunately, an aircraft in service is constantly exposed to dynamic loads that degrade its flight characteristics. These degradations endow two main origins: airframe deterioration (control surfaces rigging, seals missing or damaged, etc.) and engine performance degradation (fuel consumption increase for a given thrust). Thus, after several years of service, the performance databases and the APM associated to a specific aircraft are no longer representative enough of the actual aircraft performance. It is important to monitor the trend of the performance deterioration and correct the uncertainties of the aircraft model in order to improve the accuracy the flight management system predictions. The basis of this research lies in the new ability to continuously update an Aircraft Performance Model (APM) during flight using an adaptive lookup table technique. This methodology was developed and applied to the well-known Cessna Citation X business aircraft. For the purpose of this study, a level D Research Aircraft Flight Simulator (RAFS) was used as a test aircraft. According to Federal Aviation Administration the level D is the highest certification level for the flight dynamics modeling. Basically, using data available in the Flight Crew Operating Manual (FCOM), a first APM describing the variation of the engine fan speed and aircraft fuel flow w.r.t flight conditions was derived. This model was next improved using the proposed methodology. To do that, several cruise flights were performed using the RAFS. An algorithm was developed to frequently sample the aircraft sensors measurements during the flight and compare the model prediction with the actual measurements. Based on these comparisons, a correction was performed on the actual APM in order to minimize the error between the predicted data and the measured data. In this way, as the aircraft flies, the APM will be continuously enhanced, making the FMS more and more precise and the prediction of trajectories more realistic and more reliable. The results obtained are very encouraging. Indeed, using the tables initialized with the FCOM data, only a few iterations were needed to reduce the fuel flow prediction error from an average relative error of 12% to 0.3%. Similarly, the FCOM prediction regarding the engine fan speed was reduced from a maximum error deviation of 5.0% to 0.2% after only ten flights.

Keywords: aircraft performance, cruise, trajectory optimization, adaptive lookup tables, Cessna Citation X

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Authors: D. Bjelica, T. Slavinski, V. Vukimrovic, D. Pavlovic, D. Bodroza, V. Dabetic

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Traditional methods and techniques used in higher education may be significantly persuasive on the university students' perception about quality of the teaching process. Students’ satisfaction with the university experience may be affected by chosen educational approaches. Contemporary project management trends recognize agile approaches' beneficial, so modern practice highlights their usage, especially in the IT industry. A key research question concerns the possibility of applying agile methods in youth education. As agile methodology pinpoint iteratively-incremental delivery of results, its employment could be remarkably fruitful in education. This paper demonstrates the agile concept's application in the university students’ education through the continuous delivery of student solutions. Therefore, based on the fundamental values and principles of the agile manifest, paper will analyze students' performance and learned lessons in their encounter with the agile environment. The research is based on qualitative and quantitative analysis that includes sprints, as preparation and realization of student tasks in shorter iterations. Consequently, the performance of student teams will be monitored through iterations, as well as the process of adaptive planning and realization. Grounded theory methodology has been used in this research, as so as descriptive statistics and Man Whitney and Kruskal Wallis test for group comparison. Developed constructs of the model will be showcase through qualitative research, then validated through a pilot survey, and eventually tested as a concept in the final survey. The paper highlights the variability of educational curricula based on university students' feedbacks, which will be collected at the end of every sprint and indicates to university students' satisfaction inconsistency according to approaches applied in education. Values delivered by the lecturers will also be continuously monitored; thus, it will be prioritizing in order to students' requests. Minimal viable product, as the early delivery of results, will be particularly emphasized in the implementation process. The paper offers both theoretical and practical implications. This research contains exceptional lessons that may be applicable by educational institutions in curriculum creation processes, or by lecturers in curriculum design and teaching. On the other hand, they can be beneficial regarding university students' satisfaction increscent in respect of teaching styles, gained knowledge, or even educational content.

Keywords: academic performances, agile, high education, university students' satisfaction

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Authors: A. Shaygani, R. Saifi, M. S. Saidi, M. Sani

Abstract:

Particle size distribution, the most important characteristics of aerosols, is obtained through electrical characterization techniques. The dynamics of charged nano-particles under the influence of electric field in electrical mobility spectrometer (EMS) reveals the size distribution of these particles. The accuracy of this measurement is influenced by flow conditions, geometry, electric field and particle charging process, therefore by the transfer function (transfer matrix) of the instrument. In this work, a wire-cylinder corona charger was designed and the combined field-diffusion charging process of injected poly-disperse aerosol particles was numerically simulated as a prerequisite for the study of a multi-channel EMS. The result, a cloud of particles with non-uniform charge distribution, was introduced to the EMS. The flow pattern and electric field in the EMS were simulated using computational fluid dynamics (CFD) to obtain particle trajectories in the device and therefore to calculate the reported signal by each electrometer. According to the output signals (resulted from bombardment of particles and transferring their charges as currents), we proposed a modification to the size of detecting rings (which are connected to electrometers) in order to evaluate particle size distributions more accurately. Based on the capability of the system to transfer information contents about size distribution of the injected particles, we proposed a benchmark for the assessment of optimality of the design. This method applies the concept of Von Neumann entropy and borrows the definition of entropy from information theory (Shannon entropy) to measure optimality. Entropy, according to the Shannon entropy, is the ''average amount of information contained in an event, sample or character extracted from a data stream''. Evaluating the responses (signals) which were obtained via various configurations of detecting rings, the best configuration which gave the best predictions about the size distributions of injected particles, was the modified configuration. It was also the one that had the maximum amount of entropy. A reasonable consistency was also observed between the accuracy of the predictions and the entropy content of each configuration. In this method, entropy is extracted from the transfer matrix of the instrument for each configuration. Ultimately, various clouds of particles were introduced to the simulations and predicted size distributions were compared to the exact size distributions.

Keywords: aerosol nano-particle, CFD, electrical mobility spectrometer, von neumann entropy

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