Search results for: code error correction

Authors: Andrew Gray, Zhibing Zhang

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The ability for consumer products to deliver a sustained perfume response can be a key driver for a variety of applications. Many compounds in perfume oils are highly volatile, meaning they readily evaporate once the product is applied, and the longevity of the scent is poor. Perfume capsules have been introduced as a means of abating this evaporation once the product has been delivered. The impermeable capsules are aimed to be stable within the formulation, and remain intact during delivery to the desired substrate, only rupturing to release the core perfume oil through application of mechanical force applied by the consumer. This opens up the possibility of obtaining an olfactive response hours, weeks or even months after delivery, depending on the nature of the desired application. Tailoring the properties of the polymeric capsules to better address the needs of the application is not a trivial challenge and currently design of capsules is largely done by trial and error. The aim of this work is to have more predictive methods for capsule design depending on the consumer application. This means refining formulations such that they rupture at the right time for the specific consumer application, not too early, not too late. Finding the right balance between these extremes is essential if a benefit is sought with respect to neat addition of perfume to formulations. It is important to understand the forces that influence capsule rupture, first, by quantifying the magnitude of these different forces, and then by assessing bulk rupture in real-world applications to understand how capsules actually respond. Samples were provided by an industrial partner and the mechanical properties of individual capsules within the samples were characterized via a micromanipulation technique, developed by Professor Zhang at the University of Birmingham. The capsules were synthesized such as to change one particular physicochemical property at a time, such as core: wall material ratio, and the average size of capsules. Analysis of shell thickness via Transmission Electron Microscopy, size distribution via the use of a Mastersizer, as well as a variety of other techniques confirmed that only one particular physicochemical property was altered for each sample. The mechanical analysis was subsequently undertaken, showing the effect that changing certain capsule properties had on the response under compression. It was, however, important to link this fundamental mechanical response to capsule performance in real-world applications. As such, the capsule samples were introduced to a formulation and exposed to full scale stresses. GC-MS headspace analysis of the perfume oil released from broken capsules enabled quantification of what the relative strengths of capsules truly means for product performance. Correlations have been found between the mechanical strength of capsule samples and performance in terms of perfume release in consumer applications. Having a better understanding of the key parameters that drive performance benefits the design of future formulations by offering better guidelines on the parameters that can be adjusted without worrying about the performance effects, and singles out those parameters that are essential in finding the sweet spot for capsule performance.

Keywords: consumer products, mechanical and physicochemical properties, perfume capsules, rupture behaviour

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Authors: Oscar Bareiro, Johannes Sackmann, Thomas Gries

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Carbon fiber reinforced polymer composites (CFRP) are used in a wide variety of applications due to its advantageous properties and design versatility. The braiding process enables the manufacture of components with good toughness and fatigue strength. However, failure mechanisms of CFRPs are complex and still present challenges associated with their maintenance and repair. Within the broad scope of structural health monitoring (SHM), strain monitoring can be applied to composite materials to improve reliability, reduce maintenance costs and safely exhaust service life. Traditional SHM systems employ e.g. fiber optics, piezoelectrics as sensors, which are often expensive, time consuming and complicated to implement. A cost-efficient alternative can be the exploitation of the conductive properties of fiber-based sensors such as carbon, copper, or constantan - a copper-nickel alloy – that can be utilized as sensors within composite structures to achieve strain monitoring. This allows the structure to provide feedback via electrical signals to a user which are essential for evaluating the structural condition of the structure. This work presents a strategy for the in-process integration of resistance-based sensors (Elektrisola Feindraht AG, CuNi23Mn, Ø = 0.05 mm) into textile preforms during its manufacture via the braiding process (Herzog RF-64/120) to achieve strain monitoring of braided composites. For this, flat samples of instrumented composite laminates of carbon fibers (Toho Tenax HTS40 F13 24K, 1600 tex) and epoxy resin (Epikote RIMR 426) were manufactured via vacuum-assisted resin infusion. These flat samples were later cut out into test specimens and the integrated sensors were wired to the measurement equipment (National Instruments, VB-8012) for data acquisition during the execution of mechanical tests. Quasi-static tests were performed (tensile, 3-point bending tests) following standard protocols (DIN EN ISO 527-1 & 4, DIN EN ISO 14132); additionally, dynamic tensile tests were executed. These tests were executed to assess the sensor response under different loading conditions and to evaluate the influence of the sensor presence on the mechanical properties of the material. Several orientations of the sensor with regards to the applied loading and sensor placements inside the laminate were tested. Strain measurements from the integrated sensors were made by programming a data acquisition code (LabView) written for the measurement equipment. Strain measurements from the integrated sensors were then correlated to the strain/stress state for the tested samples. From the assessment of the sensor integration approach it can be concluded that it allows for a seamless sensor integration into the textile preform. No damage to the sensor or negative effect on its electrical properties was detected during inspection after integration. From the assessment of the mechanical tests of instrumented samples it can be concluded that the presence of the sensors does not alter significantly the mechanical properties of the material. It was found that there is a good correlation between resistance measurements from the integrated sensors and the applied strain. It can be concluded that the correlation is of sufficient accuracy to determinate the strain state of a composite laminate based solely on the resistance measurements from the integrated sensors.

Keywords: braiding process, in-process sensor integration, instrumented composite material, resistance-based sensor, strain monitoring

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Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

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Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

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Authors: Abderrazak Bannari

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Acid Mine Drainage (AMD) from mine wastes and contaminations of soils and water with metals are considered as a major environmental problem in mining areas. It is produced by interactions of water, air, and sulphidic mine wastes. This environment problem results from a series of chemical and biochemical oxidation reactions of sulfide minerals e.g. pyrite and pyrrhotite. These reactions lead to acidity as well as the dissolution of toxic and heavy metals (Fe, Mn, Cu, etc.) from tailings waste rock piles, and open pits. Soil and aquatic ecosystems could be contaminated and, consequently, human health and wildlife will be affected. Furthermore, secondary minerals, typically formed during weathering of mine waste storage areas when the concentration of soluble constituents exceeds the corresponding solubility product, are also important. The most common secondary mineral compositions are hydrous iron oxide (goethite, etc.) and hydrated iron sulfate (jarosite, etc.). The objectives of this study focus on the detection and mapping of MIOP in the soil using Hyperion EO-1 (Earth Observing - 1) hyperspectral data and constrained linear spectral mixture analysis (CLSMA) algorithm. The abandoned Kettara mine, located approximately 35 km northwest of Marrakech city (Morocco) was chosen as study area. During 44 years (from 1938 to 1981) this mine was exploited for iron oxide and iron sulphide minerals. Previous studies have shown that Kettara surrounding soils are contaminated by heavy metals (Fe, Cu, etc.) as well as by secondary minerals. To achieve our objectives, several soil samples representing different MIOP classes have been resampled and located using accurate GPS ( ≤ ± 30 cm). Then, endmembers spectra were acquired over each sample using an Analytical Spectral Device (ASD) covering the spectral domain from 350 to 2500 nm. Considering each soil sample separately, the average of forty spectra was resampled and convolved using Gaussian response profiles to match the bandwidths and the band centers of the Hyperion sensor. Moreover, the MIOP content in each sample was estimated by geochemical analyses in the laboratory, and a ground truth map was generated using simple Kriging in GIS environment for validation purposes. The acquired and used Hyperion data were corrected for a spatial shift between the VNIR and SWIR detectors, striping, dead column, noise, and gain and offset errors. Then, atmospherically corrected using the MODTRAN 4.2 radiative transfer code, and transformed to surface reflectance, corrected for sensor smile (1-3 nm shift in VNIR and SWIR), and post-processed to remove residual errors. Finally, geometric distortions and relief displacement effects were corrected using a digital elevation model. The MIOP fraction map was extracted using CLSMA considering the entire spectral range (427-2355 nm), and validated by reference to the ground truth map generated by Kriging. The obtained results show the promising potential of the proposed methodology for the detection and mapping of mine iron oxide pollution in the soil.

Keywords: hyperion eo-1, hyperspectral, mine iron oxide pollution, environmental impact, unmixing

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Authors: Raja Umer Sajjad, Chang Hee Lee

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Stormwater runoff is the leading contributor to pollution of receiving waters. In response, an efficient stormwater monitoring program is required to quantify and eventually reduce stormwater pollution. The overall goals of stormwater monitoring programs primarily include the identification of high-risk dischargers and the development of total maximum daily loads (TMDLs). The challenge in developing better monitoring program is to reduce the variability in flux estimates due to sampling errors; however, the success of monitoring program mainly depends on the accuracy of the estimates. Apart from sampling errors, manpower and budgetary constraints also influence the quality of the estimates. This study attempted to develop optimum stormwater monitoring design considering both cost and the quality of the estimated pollutants flux. Three years stormwater monitoring data (2012 – 2014) from a mix land use located within Geumhak watershed South Korea was evaluated. The regional climate is humid and precipitation is usually well distributed through the year. The investigation of a large number of water quality parameters is time-consuming and resource intensive. In order to identify a suite of easy-to-measure parameters to act as a surrogate, Principal Component Analysis (PCA) was applied. Means, standard deviations, coefficient of variation (CV) and other simple statistics were performed using multivariate statistical analysis software SPSS 22.0. The implication of sampling time on monitoring results, number of samples required during the storm event and impact of seasonal first flush were also identified. Based on the observations derived from the PCA biplot and the correlation matrix, total suspended solids (TSS) was identified as a potential surrogate for turbidity, total phosphorus and for heavy metals like lead, chromium, and copper whereas, Chemical Oxygen Demand (COD) was identified as surrogate for organic matter. The CV among different monitored water quality parameters were found higher (ranged from 3.8 to 15.5). It suggests that use of grab sampling design to estimate the mass emission rates in the study area can lead to errors due to large variability. TSS discharge load calculation error was found only 2 % with two different sample size approaches; i.e. 17 samples per storm event and equally distributed 6 samples per storm event. Both seasonal first flush and event first flush phenomena for most water quality parameters were observed in the study area. Samples taken at the initial stage of storm event generally overestimate the mass emissions; however, it was found that collecting a grab sample after initial hour of storm event more closely approximates the mean concentration of the event. It was concluded that site and regional climate specific interventions can be made to optimize the stormwater monitoring program in order to make it more effective and economical.

Keywords: first flush, pollutant load, stormwater monitoring, surrogate parameters

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Authors: A.H.Y. Lai, C. Teyra

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In Taiwan, there are 16 official indigenous groups, accounting for 2.3% of the total population. Like other indigenous populations worldwide, indigenous peoples in Taiwan have poorer mental health because of their history of oppression and colonisation. Amid the negative narratives, the ethnic identity of cultural minorities is their unique psychological and cultural asset. Moreover, positive socialisation is found to be related to strong ethnic identity. Based on Phinney’s theory on ethnic identity development and social support theory, this study adopted a strength-based approach conceptualising ethnic identity as the central organising principle that linked perceived social support and mental health among indigenous adults in Taiwan. Aims. Overall aim is to examine the effect of ethnic identity and social support on mental health. Specific aims were to examine : (1) the association between ethnic identity and mental health; (2) the association between perceived social support and mental health ; (3) the indirect effect of ethnic identity linking perceived social support and mental health. Methods. Participants were indigenous adults in Taiwan (n=200; mean age=29.51; Female=31%, Male=61%, Others=8%). A cross-sectional quantitative design was implemented using data collected in the year 2020. Respondent-driven sampling was used. Standardised measurements were: Ethnic Identity Scale(6-item); Social Support Questionnaire-SF(6 items); Patient Health Questionnaire(9-item); and Generalised Anxiety Disorder(7-item). Covariates were age, gender and economic satisfaction. A four-stage structural equation modelling (SEM) with robust maximin likelihood estimation was employed using Mplus8.0. Step 1: A measurement model was built and tested using confirmatory factor analysis (CFA). Step 2: Factor covariates were re-specified as direct effects in the SEM. Covariates were added. The direct effects of (1) ethnic identity and social support on depression and anxiety and (2) social support on ethnic identity were tested. The indirect effect of ethnic identity was examined with the bootstrapping technique. Results. The CFA model showed satisfactory fit statistics: x^2(df)=869.69(608), p<.05; Comparative ft index (CFI)/ Tucker-Lewis fit index (TLI)=0.95/0.94; root mean square error of approximation (RMSEA)=0.05; Standardized Root Mean Squared Residual (SRMR)=0.05. Ethnic identity is represented by two latent factors: ethnic identity-commitment and ethnic identity-exploration. Depression, anxiety and social support are single-factor latent variables. For the SEM, model fit statistics were: x^2(df)=779.26(527), p<.05; CFI/TLI=0.94/0.93; RMSEA=0.05; SRMR=0.05. Ethnic identity-commitment (b=-0.30) and social support (b=-0.33) had direct negative effects on depression, but ethnic identity-exploration did not. Ethnic identity-commitment (b=-0.43) and social support (b=-0.31) had direct negative effects on anxiety, while identity-exploration (b=0.24) demonstrated a positive effect. Social support had direct positive effects on ethnic identity-exploration (b=0.26) and ethnic identity-commitment (b=0.31). Mediation analysis demonstrated the indirect effect of ethnic identity-commitment linking social support and depression (b=0.22). Implications: Results underscore the role of social support in preventing depression via ethnic identity commitment among indigenous adults in Taiwan. Adopting the strength-based approach, mental health practitioners can mobilise indigenous peoples’ commitment to their group to promote their well-being.

Keywords: ethnic identity, indigenous population, mental health, perceived social support

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Authors: Galit Shiff, Yael Gilad

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The need to retrofit old buildings lies in the fact that buildings are responsible for the main energy use and CO₂ emission. Existing old structures are more dominant in their effect than new energy-efficient buildings. Nevertheless not every case of urban renewal that aims to replace old buildings with new neighbourhoods necessarily has a financial or sustainable justification. Façade design plays a vital role in the building's energy performance and the unit's comfort conditions. A retrofit façade residential methodology and feasibility applicative study has been carried out for the past four years, with two projects already fully renovated. The intention of this study is to serve as a case study for limited budget façade retrofit in Mediterranean climate urban areas. The two case study buildings are set in Israel. However, they are set in different local climatic conditions. One is in 'Sderot' in the south of the country, and one is in' Migdal Hahemek' in the north of the country. The building typology is similar. The budget of the projects is around $14,000 per unit and includes interventions at the buildings' envelope while tenants are living in. Extensive research and analysis of the existing conditions have been done. The building's components, materials and envelope sections were mapped, examined and compared to relevant updated standards. Solar radiation simulations for the buildings in their surroundings during winter and summer days were done. The energy rate of each unit, as well as the building as a whole, was calculated according to the Israeli Energy Code. The buildings’ facades were documented with the use of a thermal camera during different hours of the day. This information was superimposed with data about the electricity use and the thermal comfort that was collected from the residential units. Later in the process, similar tools were further used in order to compare the effectiveness of different design options and to evaluate the chosen solutions. Both projects showed that the most problematic units were the ones below the roof and the ones on top of the elevated entrance floor (pilotis). Old buildings tend to have poor insulation on those two horizontal surfaces which require treatment. Different radiation levels and wall sections in the two projects influenced the design strategies: In the southern project, there was an extreme difference in solar radiations levels between the main façade and the back elevation. Eventually, it was decided to invest in insulating the main south-west façade and the side façades, leaving the back north-east façade almost untouched. Lower levels of radiation in the northern project led to a different tactic: a combination of basic insulation on all façades, together with intense treatment on areas with problematic thermal behavior. While poor execution of construction details and bad installation of windows in the northern project required replacing them all, in the southern project it was found that it is more essential to shade the windows than replace them. Although the buildings and the construction typology was chosen for this study are similar, the research shows that there are large differences due to the location in different climatic zones and variation in local conditions. Therefore, in order to reach a systematic and cost-effective method of work, a more extensive catalogue database is needed. Such a catalogue will enable public housing companies in the Mediterranean climate to promote massive projects of renovating existing old buildings, drawing on minimal analysis and planning processes.

Keywords: facade, low budget, residential, retrofit

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Authors: Salvatore Luongo, Carlo Luongo

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This paper discusses the implementation solutions to reduce the computational load for the Traffic Situational Awareness with Alerts (TSAA) application, based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology. In 2008, there were 23 total mid-air collisions involving general aviation fixed-wing aircraft, 6 of which were fatal leading to 21 fatalities. These collisions occurred during visual meteorological conditions, indicating the limitations of the see-and-avoid concept for mid-air collision avoidance as defined in the Federal Aviation Administration’s (FAA). The commercial aviation aircraft are already equipped with collision avoidance system called TCAS, which is based on classic transponder technology. This system dramatically reduced the number of mid-air collisions involving air transport aircraft. In general aviation, the same reduction in mid-air collisions has not occurred, so this reduction is the main objective of the TSAA application. The major difference between the original conflict detection application and the TSAA application is that the conflict detection is focused on preventing loss of separation in en-route environments. Instead TSAA is devoted to reducing the probability of mid-air collision in all phases of flight. The TSAA application increases the flight crew traffic situation awareness providing alerts of traffic that are detected in conflict with ownship in support of the see-and-avoid responsibility. The relevant effort has been spent in the design process and the code generation in order to maximize the efficiency and performances in terms of computational load and memory consumption reduction. The TSAA architecture is divided into two high-level systems: the “Threats database” and the “Conflict detector”. The first one receives the traffic data from ADS-B device and provides the memorization of the target’s data history. Conflict detector module estimates ownship and targets trajectories in order to perform the detection of possible future loss of separation between ownship and each target. Finally, the alerts are verified by additional conflict verification logic, in order to prevent possible undesirable behaviors of the alert flag. In order to reduce the computational load, a pre-check evaluation module is used. This pre-check is only a computational optimization, so the performances of the conflict detector system are not modified in terms of number of alerts detected. The pre-check module uses analytical trajectories propagation for both target and ownship. This allows major accuracy and avoids the step-by-step propagation, which requests major computational load. Furthermore, the pre-check permits to exclude the target that is certainly not a threat, using an analytical and efficient geometrical approach, in order to decrease the computational load for the following modules. This software improvement is not suggested by FAA documents, and so it is the main innovation of this work. The efficiency and efficacy of this enhancement are verified using fast-time and real-time simulations and by the execution on a real device in several FAA scenarios. The final implementation also permits the FAA software certification in compliance with DO-178B standard. The computational load reduction allows the installation of TSAA application also on devices with multiple applications and/or low capacity in terms of available memory and computational capabilities

Keywords: traffic situation awareness, general aviation, aircraft conflict detection, computational load reduction, implementation solutions, software certification

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Authors: Ntandazo Dlatu, Benjamin Longo-Mbenza, Andre Renzaho, Ruffin Appalata, Yolande Yvonne Valeria Matoumona Mavoungou, Mbenza Ben Longo, Kenneth Ekoru, Blaise Makoso, Gedeon Longo Longo

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Background: The gap between complexities related to the integration of Tuberculosis /HIV control and evidence-based knowledge motivated the initiation of the study. Therefore, the objective of this study was to explore correlations between national TB management guidelines, multiple deprivation indexes, quantiles, components and levels of Tuberculosis control programme using mathematical modeling in rural O.R. Tambo District Health Facilities, South Africa. Methods: The study design used mixed secondary data analysis and cross-sectional analysis between 2009 and 2013 across O.R Tambo District, Eastern Cape, South Africa using univariate/ bivariate analysis, linear multiple regression models, and multivariate discriminant analysis. Health inequalities indicators and component of an impediment to the tuberculosis control programme were evaluated. Results: In total, 62 400 records for TB notification were analyzed for the period 2009-2013. There was a significant but negative between Financial Year Expenditure (r= -0.894; P= 0.041) Seropositive HIV status(r= -0.979; P= 0.004), Population Density (r = -0.881; P= 0.048) and the number of TB defaulter in all TB cases. It was shown unsuccessful control of TB management program through correlations between numbers of new PTB smear positive, TB defaulter new smear-positive, TB failure all TB, Pulmonary Tuberculosis case finding index and deprivation-concentration-dispersion index. It was shown successful TB program control through significant and negative associations between declining numbers of death in co-infection of HIV and TB, TB deaths all TB and SMIAD gradient/ deprivation-concentration-dispersion index. The multivariate linear model was summarized by unadjusted r of 96%, adjusted R2 of 95 %, Standard Error of estimate of 0.110, R2 changed of 0.959 and significance for variance change for P=0.004 to explain the prediction of TB defaulter in all TB with equation y= 8.558-0.979 x number of HIV seropositive. After adjusting for confounding factors (PTB case finding the index, TB defaulter new smear-positive, TB death in all TB, TB defaulter all TB, and TB failure in all TB). The HIV and TB death, as well as new PTB smear positive, were identified as the most important, significant, and independent indicator to discriminate most deprived deprivation index far from other deprivation quintiles 2-5 using discriminant analysis. Conclusion: Elimination of poverty such as overcrowding, lack of sanitation and environment of highest burden of HIV might end the TB threat in O.R Tambo District, Eastern Cape, South Africa. Furthermore, ongoing adequate budget comprehensive, holistic and collaborative initiative towards Sustainable Developmental Goals (SDGs) is necessary for complete elimination of TB in poor O.R Tambo District.

Keywords: tuberculosis, HIV/AIDS, success, failure, control program, health inequalities, South Africa

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Authors: Mohammadreza Kashizadeh, Mohammadamin Hashemi

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Solymorph, a kinetic building facade designed for optimal energy capture and architectural expression, is explored in this paper. The system integrates photovoltaic panels with soft robotic actuators for precise solar tracking, resulting in enhanced electricity generation compared to static facades. Driven by the growing interest in dynamic building envelopes, the exploration of novel facade systems is necessitated. Increased energy generation and regulation of energy flow within buildings are potential benefits offered by integrating photovoltaic (PV) panels as kinetic elements. However, incorporating these technologies into mainstream architecture presents challenges due to the complexity of coordinating multiple systems. To address this, Solymorph leverages soft robotic actuators, known for their compliance, resilience, and ease of integration. Additionally, the project investigates the potential for employing Large Language Models (LLMs) to streamline the design process. The research methodology involved design development, material selection, component fabrication, and system assembly. Grasshopper (GH) was employed within the digital design environment for parametric modeling and scripting logic, and an LLM was experimented with to generate Python code for the creation of a random surface with user-defined parameters. Various techniques, including casting, 3D printing, and laser cutting, were utilized to fabricate the physical components. Finally, a modular assembly approach was adopted to facilitate installation and maintenance. A case study focusing on the application of Solymorph to an existing library building at Politecnico di Milano is presented. The facade system is divided into sub-frames to optimize solar exposure while maintaining a visually appealing aesthetic. Preliminary structural analyses were conducted using Karamba3D to assess deflection behavior and axial loads within the cable net structure. Additionally, Finite Element (FE) simulations were performed in Abaqus to evaluate the mechanical response of the soft robotic actuators under pneumatic pressure. To validate the design, a physical prototype was created using a mold adapted for a 3D printer's limitations. Casting Silicone Rubber Sil 15 was used for its flexibility and durability. The 3D-printed mold components were assembled, filled with the silicone mixture, and cured. After demolding, nodes and cables were 3D-printed and connected to form the structure, demonstrating the feasibility of the design. Solymorph demonstrates the potential of soft robotics and Artificial Intelligence (AI) for advancements in sustainable building design and construction. The project successfully integrates these technologies to create a dynamic facade system that optimizes energy generation and architectural expression. While limitations exist, Solymorph paves the way for future advancements in energy-efficient facade design. Continued research efforts will focus on cost reduction, improved system performance, and broader applicability.

Keywords: artificial intelligence, energy efficiency, kinetic photovoltaics, pneumatic control, soft robotics, sustainable building

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Authors: Riadh Zorgati, Thomas Triboulet

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In quite diverse application areas such as astronomy, medical imaging, geophysics or nondestructive evaluation, many problems related to calibration, fitting or estimation of a large number of input parameters of a model from a small amount of output noisy data, can be cast as inverse problems. Due to noisy data corruption, insufficient data and model errors, most inverse problems are ill-posed in a Hadamard sense, i.e. existence, uniqueness and stability of the solution are not guaranteed. A wide class of inverse problems in physics relates to the Fredholm equation of the first kind. The ill-posedness of such inverse problem results, after discretization, in a very ill-conditioned linear system of equations, the condition number of the associated matrix can typically range from 109 to 1018. This condition number plays the role of an amplifier of uncertainties on data during inversion and then, renders the inverse problem difficult to handle numerically. Similar problems appear in other areas such as numerical optimization when using interior points algorithms for solving linear programs leads to face ill-conditioned systems of linear equations. Devising efficient solution approaches for such system of equations is therefore of great practical interest. Efficient iterative algorithms are proposed for solving a system of linear equations. The approach is based on a preconditioning of the initial matrix of the system with an approximation of a generalized inverse leading to a stochastic preconditioned matrix. This approach, valid for non-negative matrices, is first extended to hermitian, semi-definite positive matrices and then generalized to any complex rectangular matrices. The main results obtained are as follows: 1) We are able to build a generalized inverse of any complex rectangular matrix which satisfies the convergence condition requested in iterative algorithms for solving a system of linear equations. This completes the (short) list of generalized inverse having this property, after Kaczmarz and Cimmino matrices. Theoretical results on both the characterization of the type of generalized inverse obtained and the convergence are derived. 2) Thanks to its properties, this matrix can be efficiently used in different solving schemes as Richardson-Tanabe or preconditioned conjugate gradients. 3) By using Lp norms, we propose generalized Kaczmarz’s type matrices. We also show how Cimmino's matrix can be considered as a particular case consisting in choosing the Euclidian norm in an asymmetrical structure. 4) Regarding numerical results obtained on some pathological well-known test-cases (Hilbert, Nakasaka, …), some of the proposed algorithms are empirically shown to be more efficient on ill-conditioned problems and more robust to error propagation than the known classical techniques we have tested (Gauss, Moore-Penrose inverse, minimum residue, conjugate gradients, Kaczmarz, Cimmino). We end on a very early prospective application of our approach based on stochastic matrices aiming at computing some parameters (such as the extreme values, the mean, the variance, …) of the solution of a linear system prior to its resolution. Such an approach, if it were to be efficient, would be a source of information on the solution of a system of linear equations.

Keywords: conditioning, generalized inverse, linear system, norms, stochastic matrix

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Authors: Imen Asma Mbarek, Alexis Rusinek, Etienne Petit, Guy Sutter, Gautier List

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Over the past two decades, the automotive, aerospace and army industries have been paying an increasing attention to Finite Elements (FE) numerical simulations of the fracture process of their structures. Thanks to the numerical simulations, it is nowadays possible to analyze several problems involving costly and dangerous extreme loadings safely and at a reduced cost such as blast or ballistic impact problems. The present paper is concerned with ballistic impact and perforation problems involving ductile fracture of thin armor steel targets. The target fracture process depends usually on various parameters: the projectile nose shape, the target thickness and its mechanical properties as well as the impact conditions (friction, oblique/normal impact...). In this work, the investigations are concerned with the normal impact of a conical head-shaped projectile on thin armor steel targets. The main aim is to establish a comparative study of four fracture criteria that are commonly used in the fracture process simulations of structures subjected to extreme loadings such as ballistic impact and perforation. Usually, the damage initiation results from a complex physical process that occurs at the micromechanical scale. On a macro scale and according to the following fracture models, the variables on which the fracture depends are mainly the stress triaxiality ƞ, the strain rate, temperature T, and eventually the Lode angle parameter Ɵ. The four failure criteria are: the critical strain to failure model, the Johnson-Cook model, the Wierzbicki model and the Modified Hosford-Coulomb model MHC. Using the SEM, the observations of the fracture facies of tension specimen and of armor steel targets impacted at low and high incident velocities show that the fracture of the specimens is a ductile fracture. The failure mode of the targets is petalling with crack propagation and the fracture facies are covered with micro-cavities. The parameters of each ductile fracture model have been identified for three armor steels and the applicability of each criterion was evaluated using experimental investigations coupled to numerical simulations. Two loading paths were investigated in this study, under a wide range of strain rates. Namely, quasi-static and intermediate uniaxial tension and quasi-static and dynamic double shear testing allow covering various values of stress triaxiality ƞ and of the Lode angle parameter Ɵ. All experiments were conducted on three different armor steel specimen under quasi-static strain rates ranging from 10-4 to 10-1 1/s and at three different temperatures ranging from 297K to 500K, allowing drawing the influence of temperature on the fracture process. Intermediate tension testing was coupled to dynamic double shear experiments conducted on the Hopkinson tube device, allowing to spot the effect of high strain rate on the damage evolution and the crack propagation. The aforementioned fracture criteria are implemented into the FE code ABAQUS via VUMAT subroutine and they were coupled to suitable constitutive relations allow having reliable results of ballistic impact problems simulation. The calibration of the four damage criteria as well as a concise evaluation of the applicability of each criterion are detailed in this work.

Keywords: armor steels, ballistic impact, damage criteria, ductile fracture, SEM

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Authors: Naoko Ono, Junko Kato

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According to the Ministry of Justice, 9,044,000 foreigners visited Japan in 2010. The number of foreign residents in Japan was over 2,134,000 at the end of 2010. Further, medical tourism has emerged as a new area of business. Against this background, language barriers put the health of foreigners in Japan at risk, because they have difficulty in accessing health care and communicating with medical professionals. Medical interpreting training is urgently needed in response to language problems resulting from the rapid increase in the number of foreign workers in Japan over recent decades. Especially, there is a growing need in medical settings in Japan to speak international languages for communication, with Tokyo selected as the host city of the 2020 Summer Olympics. Due to the limited number of practical activities on medical interpreting, it is difficult for learners to acquire the interpreting skills. In order to eliminate the shortcoming, a web-based English-Japanese medical interpreting training system was developed. We conducted a literature review to identify learning contents, core competencies for medical interpreters by using Pubmed, PsycINFO, Cochrane Library, and Google Scholar. Selected papers were investigated to find core competencies in medical interpreting. Eleven papers were selected through literature review indicating core competencies for medical interpreters. Core competencies in medical interpreting abstracted from the literature review, showed consistency in previous research whilst the content of the programs varied in domestic and international training programs for medical interpreters. Results of the systematic review indicated five core competencies: (a) maintaining accuracy and completeness; (b) medical terminology and understanding the human body; (c) behaving ethically and making ethical decisions; (d) nonverbal communication skills; and (e) cross-cultural communication skills. We developed an e-leaning program for training medical interpreters. A Web-based Medical Interpreter Training Program which cover these competencies was developed. The program included the following : online word list (Quizlet), allowing student to study online and on their smartphones; self-study tool (Quizlet) for help with dictation and spelling; word quiz (Quizlet); test-generating system (Quizlet); Interactive body game (BBC);Online resource for understanding code of ethics in medical interpreting; Webinar about non-verbal communication; and Webinar about incompetent vs. competent cultural care. The design of a virtual environment allows the execution of complementary experimental exercises for learners of medical interpreting and introduction to theoretical background of medical interpreting. Since this system adopts a self-learning style, it might improve the time and lack of teaching material restrictions of the classroom method. In addition, as a teaching aid, virtual medical interpreting is a powerful resource for the understanding how actual medical interpreting can be carried out. The developed e-learning system allows remote access, enabling students to perform experiments at their own place, without being physically in the actual laboratory. The web-based virtual environment empowers students by granting them access to laboratories during their free time. A practical example will be presented in order to show capabilities of the system. The developed web-based training program for medical interpreters could bridge the gap between medical professionals and patients with limited English proficiency.

Keywords: e-learning, language education, moodle, medical interpreting

Procedia PDF Downloads 332

Authors: Glykeria A. Visvini, George Ν. Mathioudakis, Amaia Soto Beobide, George A. Voyiatzis

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Polymer nanocomposites are materials in which a polymer matrix is reinforced with nanoscale inclusions, such as nanoparticles, nanoplates, or nanofibers. These nanoscale inclusions can significantly enhance the mechanical, thermal, electrical, and other properties of the polymer matrix, making them attractive for a wide range of industrial applications. These properties can be tailored by adjusting the type and the concentration of the nanoinclusions, which provides a high degree of flexibility in their design and development. An important property that polymeric membranes can exhibit is water vapor permeability (WVP). This can be accomplished by various methods, including the incorporation of micro/nano-fillers into the polymer matrix. In this way, a micro/nano-pore network can be formed, allowing water vapor to permeate through the membrane. At the same time, the membrane can be stretched uni- or bi-axially, creating aligned or cross-linked micropores in the composite, respectively, which can also increase the WVP. Nowadays, in industry, stretched films reinforced with CaCO3 develop micro-porosity sufficient to give them breathability characteristics. Carbon-based nanomaterials, such as graphene oxide (GO), are tentatively expected to be able to effectively improve the WVP of corresponding composite polymer films. The presence in the GO structure of various functional oxidizing groups enhances its ability to attract and channel water molecules, exploiting the unique large surface area of graphene that allows the rapid transport of water molecules. Polypropylene (PP) is widely used in various industrial applications due to its desirable properties, including good chemical resistance, excellent thermal stability, low cost, and easy processability. The specific properties of PP are highly influenced by its crystalline behavior, which is determined by its processing conditions. The development of the β-crystalline phase in PP, in combination with stretching, is anticipating improving the microporosity of the polymer matrix, thereby enhancing its WVP. The aim of present study is to create breathable PP composite membranes using carbon-based nanomaterials, such as graphene oxide (GO), reduced graphene oxide (rGO), and graphene nanoplatelets (GNPs). Unlike traditional methods that rely on the drawing process to enhance the WVP of PP, this study intents to develop a low-cost approach using melt mixing with β-nucleating agents and carbon fillers to create highly breathable PP composite membranes. The study aims to investigate how the concentration of these additives affects the water vapor transport properties of the resulting PP films/membranes. The presence of β-nucleating agents and carbon fillers is expected to enhance β-phase growth in PP, while an alternation between β- and α-phase is expected to lead to improved microporosity and WVP. Our ambition is to develop highly breathable PP composite films with superior performance and at a lower cost compared to the benchmark. Acknowledgment: This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call «Special Actions "AQUACULTURE"-"INDUSTRIAL MATERIALS"-"OPEN INNOVATION IN CULTURE"» (project code: Τ6YBP-00337)

Keywords: carbon based nanomaterials, nanocomposites, nucleating agent, polypropylene, water vapor permeability

Procedia PDF Downloads 55

Authors: Quan Wen, Tianxiao Chang, Shaolu Shi, Yushi Wang, Guangyu Wang

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As a kind of working environment of the fuze, the spin rate decaying law of projectile in exterior trajectory is of great value in the design of the rotation count fixed distance fuze. In addition, it is significant in the field of devices for simulation tests of fuze exterior ballistic environment, flight stability, and dispersion accuracy of gun projectile and opening and scattering design of submunition and illuminating cartridges. Besides, the self-destroying mechanism of the fuze in small-caliber projectile often works by utilizing the attenuation of centrifugal force. In the theory of projectile aerodynamics and fuze design, there are many formulas describing the change law of projectile angular velocity in external ballistic such as Roggla formula, exponential function formula, and power function formula. However, these formulas are mostly semi-empirical due to the poor test conditions and insufficient test data at that time. These formulas are difficult to meet the design requirements of modern fuze because they are not accurate enough and have a narrow range of applications now. In order to provide more accurate ballistic environment parameters for the design of a hemispherical head projectile fuze, the projectile’s spin rate decaying law in exterior trajectory under the effect of air resistance was studied. In the analysis, the projectile shape was simplified as hemisphere head, cylindrical part, rotating band part, and anti-truncated conical tail. The main assumptions are as follows: a) The shape and mass are symmetrical about the longitudinal axis, b) There is a smooth transition between the ball hea, c) The air flow on the outer surface is set as a flat plate flow with the same area as the expanded outer surface of the projectile, and the boundary layer is turbulent, d) The polar damping moment attributed to the wrench hole and rifling mark on the projectile is not considered, e) The groove of the rifle on the rotating band is uniform, smooth and regular. The impacts of the four parts on aerodynamic moment of the projectile rotation were obtained by aerodynamic theory. The surface friction stress of the projectile, the polar damping moment formed by the head of the projectile, the surface friction moment formed by the cylindrical part, the rotating band, and the anti-truncated conical tail were obtained by mathematical derivation. After that, the mathematical model of angular spin rate attenuation was established. In the whole trajectory with the maximum range angle (38°), the absolute error of the polar damping torque coefficient obtained by simulation and the coefficient calculated by the mathematical model established in this paper is not more than 7%. Therefore, the credibility of the mathematical model was verified. The mathematical model can be described as a first-order nonlinear differential equation, which has no analytical solution. The solution can be only gained as a numerical solution by connecting the model with projectile mass motion equations in exterior ballistics.

Keywords: ammunition engineering, fuze technology, spin rate, numerical simulation

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Authors: Seyed Hossein Hosseini, Seyed Majid Hashemzadeh

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Solar energy is one of the remarkable renewable energy sources that have particular characteristics such as unlimited, no environmental pollution, and free access. Generally, solar energy can be used in thermal and photovoltaic (PV) types. The cost of installation of the PV system is very high. Additionally, due to dependence on environmental situations such as solar radiation and ambient temperature, electrical power generation of this system is unpredictable and without power electronics devices, there is no guarantee to maximum power delivery at the output of this system. Maximum power point tracking (MPPT) should be used to achieve the maximum power of a PV string. MPPT is one of the essential parts of the PV system which without this section, it would be impossible to reach the maximum amount of the PV string power and high losses are caused in the PV system. One of the noticeable challenges in the problem of MPPT is the partial shading conditions (PSC). In PSC, the output photocurrent of the PV module under the shadow is less than the PV string current. The difference between the mentioned currents passes from the module's internal parallel resistance and creates a large negative voltage across shaded modules. This significant negative voltage damages the PV module under the shadow. This condition is called hot-spot phenomenon. An anti-paralleled diode is inserted across the PV module to prevent the happening of this phenomenon. This diode is known as the bypass diode. Due to the performance of the bypass diode under PSC, the P-V curve of the PV string has several peaks. One of the P-V curve peaks that makes the maximum available power is the global peak. Model-based Global MPPT (GMPPT) methods can estimate the optimal point with higher speed than other GMPPT approaches. Centralized, modular, and interleaved DC-DC converter topologies are the significant structures that can be used for GMPPT at a PV string. there are some problems in the centralized structure such as current mismatch losses at PV sting, loss of power of the shaded modules because of bypassing by bypass diodes under PSC, needing to series connection of many PV modules to reach the desired voltage level. In the modular structure, each PV module is connected to a DC-DC converter. In this structure, by increasing the amount of demanded power from the PV string, the number of DC-DC converters that are used at the PV system will increase. As a result, the cost of the modular structure is very high. We can implement the model-based GMPPT through the multi-input interleaved boost DC-DC converter to increase the power extraction from the PV string and reduce hot-spot and current mismatch error in a PV string under different environmental condition and variable load circumstances. The interleaved boost DC-DC converter has many privileges than other mentioned structures, such as high reliability and efficiency, better regulation of DC voltage at DC link, overcome the notable errors such as module's current mismatch and hot spot phenomenon, and power switches voltage stress reduction.

Keywords: solar energy, photovoltaic systems, interleaved boost converter, maximum power point tracking, model-based method, partial shading conditions

Procedia PDF Downloads 102

Authors: Nisha Govender, Siju Senan, Zeti-Azura Hussein, Wickneswari Ratnam

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Jatropha curcas, a well-described bioenergy crop has been extensively accepted as future fuel need especially in tropical regions. Ideal planting material required for large-scale plantation is still lacking. Breeding programmes for improved J. curcas varieties are rendered difficult due to limitations in genetic diversity. Using a combined transcriptome and physiological data, we investigated the molecular and physiological differences in high and low yielding Jatropha curcas to address plausible heritable variations underpinning these differences, in regard to photosynthesis, a key metabolism affecting yield potentials. A total of 6 individual Jatropha plant from 4 accessions described as high and low yielding planting materials were selected from the Experimental Plot A, Universiti Kebangsaan Malaysia (UKM), Bangi. The inflorescence and shoots were collected for transcriptome study. For the physiological study, each individual plant (n=10) from the high and low yielding populations were screened for agronomic traits, chlorophyll content and stomatal patterning. The J. curcas transcriptomes are available under BioProject PRJNA338924 and BioSample SAMN05827448-65, respectively Each transcriptome was subjected to functional annotation analysis of sequence datasets using the BLAST2Go suite; BLASTing, mapping, annotation, statistical analysis and visualization Large-scale phenotyping of the number of fruits per plant (NFPP) and fruits per inflorescence (FPI) classified the high yielding Jatropha accessions with average NFPP =60 and FPI > 10, whereas the low yielding accessions yielded an average NFPP=10 and FPI < 5. Next generation sequencing revealed genes with differential expressions in the high yielding Jatropha relative to the low yielding plants. Distinct differences were observed in transcript level associated to photosynthesis metabolism. DEGs collection in the low yielding population showed comparable CAM photosynthetic metabolism and photorespiration, evident as followings: phosphoenolpyruvate phosphate translocator chloroplastic like isoform with 2.5 fold change (FC) and malate dehydrogenase (2.03 FC). Green leaves have the most pronounced photosynthetic activity in a plant body due to significant accumulation of chloroplast. In most plants, the leaf is always the dominant photosynthesizing heart of the plant body. Large number of the DEGS in the high-yielding population were found attributable to chloroplast and chloroplast associated events; STAY-GREEN chloroplastic, Chlorophyllase-1-like (5.08 FC), beta-amylase (3.66 FC), chlorophyllase-chloroplastic-like (3.1 FC), thiamine thiazole chloroplastic like (2.8 FC), 1-4, alpha glucan branching enzyme chloroplastic amyliplastic (2.6FC), photosynthetic NDH subunit (2.1 FC) and protochlorophyllide chloroplastic (2 FC). The results were parallel to a significant increase in chlorophyll a content in the high yielding population. In addition to the chloroplast associated transcript abundance, the TOO MANY MOUTHS (TMM) at 2.9 FC, which code for distant stomatal distribution and patterning in the high-yielding population may explain high concentration of CO2. The results were in agreement with the role of TMM. Clustered stomata causes back diffusion in the presence of gaps localized closely to one another. We conclude that high yielding Jatropha population corresponds to a collective function of C3 metabolism with a low degree of CAM photosynthetic fixation. From the physiological descriptions, high chlorophyll a content and even distribution of stomata in the leaf contribute to better photosynthetic efficiency in the high yielding Jatropha compared to the low yielding population.

Keywords: chlorophyll, gene expression, genetic variation, stomata

Procedia PDF Downloads 210

Authors: Yuna Lee

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In the current policy process, there has been a growing interest in more open approaches that incorporate creativity and innovation based on the forecasting groups composed by the public and experts together into scientific data-driven foresight methods to implement more effective policymaking. Especially, citizen participation as collective intelligence in policymaking with design and deep scale of innovation at the global level has been developed and human-centred design thinking is considered as one of the most promising methods for strategic foresight. Yet, there is a lack of a common theoretical foundation for a comprehensive approach for the current situation of and post-COVID-19 era, and substantial changes in policymaking practice are insignificant and ongoing with trial and error. This project hypothesized that rigorously developed policy systems and tools that support strategic foresight by considering the public understanding could maximize ways to create new possibilities for a preferable future, however, it must involve a better understating of Behavioural Insights, including individual and cultural values, profit motives and needs, and psychological motivations, for implementing holistic and multilateral foresight and creating more positive possibilities. To what extent is the policymaking system theoretically possible that incorporates the holistic and comprehensive foresight and policy process implementation, assuming that theory and practice, in reality, are different and not connected? What components and environmental conditions should be included in the strategic foresight system to enhance the capacity of decision from policymakers to predict alternative futures, or detect uncertainties of the future more accurately? And, compared to the required environmental condition, what are the environmental vulnerabilities of the current policymaking system? In this light, this research contemplates the question of how effectively policymaking practices have been implemented through the synthesis of scientific, technology-oriented innovation with the strategic design for tackling complex societal challenges and devising more significant insights to make society greener and more liveable. Here, this study conceptualizes the notions of a new collaborative way of strategic foresight that aims to maximize mutual benefits between policy actors and citizens through the cooperation stemming from evolutionary game theory. This study applies mixed methodology, including interviews of policy experts, with the case in which digital transformation and strategic design provided future-oriented solutions or directions to cities’ sustainable development goals and society-wide urgent challenges such as COVID-19. As a result, artistic and sensual interpreting capabilities through strategic design promote a concrete form of ideas toward a stable connection from the present to the future and enhance the understanding and active cooperation among decision-makers, stakeholders, and citizens. Ultimately, an improved theoretical foundation proposed in this study is expected to help strategically respond to the highly interconnected future changes of the post-COVID-19 world.

Keywords: policymaking, strategic design, sustainable innovation, evolution of cooperation

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Authors: Sthanu Mahadev, Wen Chan, Melanie Lim

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Anisotropy dominated continuous-fiber composite materials have garnered attention in numerous mechanical and aerospace structural applications. Tailored mechanical properties in advanced composites can exhibit superiority in terms of stiffness-to-weight ratio, strength-to-weight ratio, low-density characteristics, coupled with significant improvements in fatigue resistance as opposed to metal structure counterparts. Extensive research has demonstrated their core potential as more than just mere lightweight substitutes to conventional materials. Prior work done by Mahadev and Chan focused on formulating a modified composite shell theory based prognosis methodology for investigating the structural response of thin-walled circular cylindrical shell type composite configurations under in-plane mechanical loads respectively. The prime motivation to develop this theory stemmed from its capability to generate simple yet accurate closed-form analytical results that can efficiently characterize circular composite shell construction. It showcased the development of a novel mathematical framework to analytically identify the location of the centroid for thin-walled, open cross-section, curved composite shells that were characterized by circumferential arc angle, thickness-to-mean radius ratio, and total laminate thickness. Ply stress variations for curved cylindrical shells were analytically examined under the application of centric tensile and bending loading. This work presents a cost-effective, small-platform experimental methodology by taking advantage of the full-field measurement capability of digital image correlation (DIC) for an accurate assessment of key mechanical parameters such as in-plane mechanical stresses and strains, centroid location etc. Mechanical property measurement of advanced composite materials can become challenging due to their anisotropy and complex failure mechanisms. Full-field displacement measurements are well suited for characterizing the mechanical properties of composite materials because of the complexity of their deformation. This work encompasses the fabrication of a set of curved cylindrical shell coupons, the design and development of a novel test-fixture design and an innovative experimental methodology that demonstrates the capability to very accurately predict the location of centroid in such curved composite cylindrical strips via employing a DIC based strain measurement technique. Error percentage difference between experimental centroid measurements and previously estimated analytical centroid results are observed to be in good agreement. The developed analytical modified-shell theory provides the capability to understand the fundamental behavior of thin-walled cylindrical shells and offers the potential to generate novel avenues to understand the physics of such structures at a laminate level.

Keywords: anisotropy, composites, curved cylindrical shells, digital image correlation

Procedia PDF Downloads 275

Authors: Claire Limoge Schraen, Philippe Gueguen, Cedric Giry, Cedric Desprez, Frédéric Ragueneau

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Mediterranean area is characterized by numerous monumental or vernacular masonry structures illustrating old ways of build and live. Those precious buildings are often poorly documented, present complex shapes and loadings, and are protected by the States, leading to legal constraints. This area also presents a moderate to high seismic activity. Even moderate earthquakes can be magnified by local site effects and cause collapse or significant damage. Moreover the structural resistance of masonry buildings, especially when less famous or located in rural zones has been generally lowered by many factors: poor maintenance, unsuitable restoration, ambient pollution, previous earthquakes. Recent earthquakes prove that any damage to these architectural witnesses to our past is irreversible, leading to the necessity of acting preventively. This means providing preventive assessments for hundreds of structures with no or few documents. In this context we want to propose a general method, based on hierarchized numerical models, to provide preliminary structural diagnoses at a regional scale, indicating whether more precise investigations and models are necessary for each building. To this aim, we adapt different tools, being developed such as photogrammetry or to be created such as a preprocessor starting from pictures to build meshes for a FEM software, in order to allow dynamic studies of the buildings of the panel. We made an inventory of 198 baroque chapels and churches situated in the French Alps. Then their structural characteristics have been determined thanks field surveys and the MicMac photogrammetric software. Using structural criteria, we determined eight types of churches and seven types for chapels. We studied their dynamical behavior thanks to CAST3M, using EC8 spectrum and accelerogramms of the studied zone. This allowed us quantifying the effect of the needed simplifications in the most sensitive zones and choosing the most effective ones. We also proposed threshold criteria based on the observed damages visible in the in situ surveys, old pictures and Italian code. They are relevant in linear models. To validate the structural types, we made a vibratory measures campaign using vibratory ambient noise and velocimeters. It also allowed us validating this method on old masonry and identifying the modal characteristics of 20 churches. Then we proceeded to a dynamic identification between numerical and experimental modes. So we updated the linear models thanks to material and geometrical parameters, often unknown because of the complexity of the structures and materials. The numerically optimized values have been verified thanks to the measures we made on the masonry components in situ and in laboratory. We are now working on non-linear models redistributing the strains. So we validate the damage threshold criteria which we use to compute the vulnerability curves of each defined structural type. Our actual results show a good correlation between experimental and numerical data, validating the final modeling simplifications and the global method. We now plan to use non-linear analysis in the critical zones in order to test reinforcement solutions.

Keywords: heritage structures, masonry numerical modeling, seismic vulnerability assessment, vibratory measure

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Authors: Sabrina Neururer, Marco Schweitzer, Werner Hackl, Bernhard Tilg, Patrick Raudaschl, Andreas Huber, Bernhard Pfeifer

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History and the current outbreak of Covid-19 have shown the deadly potential of infectious diseases. However, infectious diseases also have a serious impact on areas other than health and healthcare, such as the economy or social life. These areas are strongly codependent. Therefore, disease control measures, such as social distancing, quarantines, curfews, or lockdowns, have to be adopted in a very considerate manner. Infectious disease modeling can support policy and decision-makers with adequate information regarding the dynamics of the pandemic and therefore assist in planning and enforcing appropriate measures that will prevent the healthcare system from collapsing. In this work, an agent-based simulation package named “survival” for simulating infectious diseases is presented. A special focus is put on SARS-Cov-2. The presented simulation package was used in Austria to model the SARS-Cov-2 outbreak from the beginning of 2020. Agent-based modeling is a relatively recent modeling approach. Since our world is getting more and more complex, the complexity of the underlying systems is also increasing. The development of tools and frameworks and increasing computational power advance the application of agent-based models. For parametrizing the presented model, different data sources, such as known infections, wastewater virus load, blood donor antibodies, circulating virus variants and the used capacity for hospitalization, as well as the availability of medical materials like ventilators, were integrated with a database system and used. The simulation result of the model was used for predicting the dynamics and the possible outcomes and was used by the health authorities to decide on the measures to be taken in order to control the pandemic situation. The survival package was implemented in the programming language Java and the analytics were performed with R Studio. During the first run in March 2020, the simulation showed that without measures other than individual personal behavior and appropriate medication, the death toll would have been about 27 million people worldwide within the first year. The model predicted the hospitalization rates (standard and intensive care) for Tyrol and South Tyrol with an accuracy of about 1.5% average error. They were calculated to provide 10-days forecasts. The state government and the hospitals were provided with the 10-days models to support their decision-making. This ensured that standard care was maintained for as long as possible without restrictions. Furthermore, various measures were estimated and thereafter enforced. Among other things, communities were quarantined based on the calculations while, in accordance with the calculations, the curfews for the entire population were reduced. With this framework, which is used in the national crisis team of the Austrian province of Tyrol, a very accurate model could be created on the federal state level as well as on the district and municipal level, which was able to provide decision-makers with a solid information basis. This framework can be transferred to various infectious diseases and thus can be used as a basis for future monitoring.

Keywords: modelling, simulation, agent-based, SARS-Cov-2, COVID-19

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Authors: Andreas Rudisch, Ralf Lampert, Andreas Kolbitsch

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It is a well-known fact among the engineering community that earthquakes with comparatively low magnitudes can cause serious damage to nonstructural components (NSCs) of buildings, even when the supporting structure performs relatively well. Past research works focused mainly on NSCs of nuclear power plants and industrial plants. Particular attention should also be given to architectural façade elements of old masonry buildings (e.g. ornamental figures, balustrades, vases), which are very vulnerable under seismic excitation. Large numbers of these historical nonstructural components (HiNSCs) can be found in highly frequented historical city centers and in the event of failure, they pose a significant danger to persons. In order to estimate the vulnerability of acceleration sensitive HiNSCs, the peak horizontal floor acceleration (PHFA) is used. The PHFA depends on the dynamic characteristics of the building, the ground excitation, and induced nonlinearities. Consequently, the PHFA can not be generalized as a simple function of height. In the present research work, an extensive case study was conducted to investigate the influence of induced nonlinearity on the PHFA for old masonry buildings. Probabilistic nonlinear FE time-history analyses considering three different hazard levels were performed. A set of eighteen synthetically generated ground motions was used as input to the structure models. An elastoplastic macro-model (multiPlas) for nonlinear homogenized continuum FE-calculation was calibrated to multiple scales and applied, taking specific failure mechanisms of masonry into account. The macro-model was calibrated according to the results of specific laboratory and cyclic in situ shear tests. The nonlinear macro-model is based on the concept of multi-surface rate-independent plasticity. Material damage or crack formation are detected by reducing the initial strength after failure due to shear or tensile stress. As a result, shear forces can only be transmitted to a limited extent by friction when the cracking begins. The tensile strength is reduced to zero. The first goal of the calibration was the consistency of the load-displacement curves between experiment and simulation. The calibrated macro-model matches well with regard to the initial stiffness and the maximum horizontal load. Another goal was the correct reproduction of the observed crack image and the plastic strain activities. Again the macro-model proved to work well in this case and shows very good correlation. The results of the case study show that there is significant scatter in the absolute distribution of the PHFA between the applied ground excitations. An absolute distribution along the normalized building height was determined in the framework of probability theory. It can be observed that the extent of nonlinear behavior varies for the three hazard levels. Due to the detailed scope of the present research work, a robust comparison with code-recommendations and simplified PHFA distributions are possible. The chosen methodology offers a chance to determine the distribution of PHFA along the building height of old masonry structures. This permits a proper hazard assessment of HiNSCs under seismic loads.

Keywords: nonlinear macro-model, nonstructural components, time-history analysis, unreinforced masonry

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Authors: Roula Kitsiou, Sofia Tsioli, Eleni Gana

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The current sociopolitical realities (displacement, encampment, and resettlement) refugees experience in Greece are a quite complex issue. Their educational and social ‘integration’ is characterized by transition, insecurity, and constantly changing needs. Based on the current research data, technology and more specifically mobile phones are one of the most important resources for refugees, regardless of their levels of conventional literacy. The proposed paper discusses the challenges encountered during the design and implementation of the educational Action 16 ‘Language Education for Adult Refugees’. Action 16 is one of the 24 Actions of the Project PRESS (Provision of Refugee Education and Support Scheme), funded by the Hellenic Open University (2016-2017). Project PRESS had two main objectives: a) to address the educational and integration needs of refugees in transit, who currently reside in Greece, and b) implement research-based educational interventions in online and offline sites. In the present paper, the focus is on reflection and discussion about the challenges and the potential of integrating technology in language learning for a target-group with many specific needs, which have been recorded in field notes among other research tools (ethnographic data) used in the context of PRESS. Action 16, explores if and how technology enhanced language activities in real-time and place mediated through teachers, as well as an autonomous computer-mediated learning space (moodle platform and application) builds on and expands the linguistic, cultural and digital resources and repertoires of the students by creating collaborative face-to-face and digital learning spaces. A broader view on language as a dynamic puzzle of semiotic resources and processes based on the concept of translanguaging is adopted. Specifically, designing the blended learning environment we draw on the construct of translanguaging a) as a symbolic means to valorize students’ repertoires and practices, b) as a method to reach to specific applications of a target-language that the context brings forward (Greek useful to them), and c) as a means to expand refugees’ repertoires. This has led to the creation of a learning space where students' linguistic and cultural resources can find paths to expression. In this context, communication and learning are realized by mutually investing multiple aspects of the team members' identities as educational material designers, teachers, and students on the teaching and learning processes. Therefore, creativity, humour, code-switching, translation, transference etc. are all possible means that can be employed in order to promote multilingual communication and language learning towards raising intercultural awareness in a critical digital pedagogy context. The qualitative analysis includes critical reflection on the developed educational material, team-based reflexive discussions, teachers’ reports data, and photographs from the interventions. The endeavor to involve women and men with a refugee background into a blended learning experience was quite innovative especially for the Greek context. It reflects a pragmatist ethos of the choices made in order to respond to the here-and-now needs of the refugees, and finally it was a very challenging task that has led all actors involved into Action 16 to (re)negotiations of subjectivities and products in a creative and hopeful way.

Keywords: blended learning, integration, language education, refugees

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Authors: P. Ramesh, Aby Joseph, Arya G. Lal, U. S. Aji

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Integration of distributed green renewable energy sources in addition with battery energy storage is an inevitable requirement in a smart grid environment. To achieve this, an Intelligent Green Energy Management System (i-GEMS) needs to be incorporated to ensure coordinated operation between supply and load demand based on the hierarchy of Renewable Energy Sources (RES), battery energy storage and distribution grid. A bi-directional energy harvester is an integral component facilitating Intelligent Green Energy Management System (i-GEMS) and it is required to meet the technical challenges mentioned as follows: (1) capability for bi-directional mode of operation (buck/boost) (2) reduction of circuit parasitic to suppress voltage spikes (3) converter startup problem (4) high frequency magnetics (5) higher power density (6) mode transition issues during battery charging and discharging. This paper is focused to address the above mentioned issues and targeted to design, develop and implement a bi-directional energy harvester with galvanic isolation. In this work, the hardware architecture for bi-directional energy harvester rated 3.5 kW is developed with Isolated Full Bridge Boost Converter (IFBBC) as well as Dual Active Bridge (DAB) Converter configuration using modular power electronics hardware which is identical for both solar PV array and battery energy storage. In IFBBC converter, the current fed full bridge circuit is enabled and voltage fed full bridge circuit is disabled through Pulse Width Modulation (PWM) pulses for boost mode of operation and vice-versa for buck mode of operation. In DAB converter, all the switches are in active state so as to adjust the phase shift angle between primary full bridge and secondary full bridge which in turn decides the power flow directions depending on modes (boost/buck) of operation. Here, the control algorithm is developed to ensure the regulation of the common DC link voltage and maximum power extraction from the renewable energy sources depending on the selected mode (buck/boost) of operation. The circuit analysis and simulation study are conducted using PSIM 9.0 in three scenarios which are - 1.IFBBC with passive clamp, 2. IFBBC with active clamp, 3. DAB converter. In this work, a common hardware prototype for bi-directional energy harvester with 3.5 kW rating is built for IFBBC and DAB converter configurations. The power circuit is equipped with right choice of MOSFETs, gate drivers with galvanic isolation, high frequency transformer, filter capacitors, and filter boost inductor. The experiment was conducted for IFBBC converter with passive clamp under boost mode and the prototype confirmed the simulation results showing the measured efficiency as 88% at 2.5 kW output power. The digital controller hardware platform is developed using floating point microcontroller TMS320F2806x from Texas Instruments. The firmware governing the operation of the bi-directional energy harvester is written in C language and developed using code composer studio. The comprehensive analyses of the power circuit design, control strategy for battery charging/discharging under buck/boost modes and comparative performance evaluation using simulation and experimental results will be presented.

Keywords: bi-directional energy harvester, dual active bridge, isolated full bridge boost converter, intelligent green energy management system, maximum power point tracking, renewable energy sources

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Authors: Patrick Cormier, Cesar Meylan, Matt Jensen, Dana Agar-Newman, Chloe Werle, Ming-Chang Tsai, Marc Klimstra

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Sprint-derived horizontal force-velocity-power (FVP) profiles can be developed with adequate validity and reliability with satellite (GPS/GNSS) systems. However, FVP metrics are sensitive to small nuances in data processing procedures such that minor differences in defining the onset and end of the sprint could result in different FVP metric outcomes. Furthermore, in team-sports, there is a requirement for rapid analysis and feedback of results from multiple athletes, therefore developing standardized and automated methods to improve the speed, efficiency and reliability of this process are warranted. Thus, the purpose of this study was to compare different methods of sprint end detection on the development of FVP profiles from 10Hz GPS/GNSS data through goodness-of-fit and intertrial reliability statistics. Seventeen national team female soccer players participated in the FVP protocol which consisted of 2x40m maximal sprints performed towards the end of a soccer specific warm-up in a training session (1020 hPa, wind = 0, temperature = 30°C) on an open grass field. Each player wore a 10Hz Catapult system unit (Vector S7, Catapult Innovations) inserted in a vest in a pouch between the scapulae. All data were analyzed following common procedures. Variables computed and assessed were the model parameters, estimated maximal sprint speed (MSS) and the acceleration constant τ, in addition to horizontal relative force (F₀), velocity at zero (V₀), and relative mechanical power (Pmax). The onset of the sprints was standardized with an acceleration threshold of 0.1 m/s². The sprint end detection methods were: 1. Time when peak velocity (MSS) was achieved (zero acceleration), 2. Time after peak velocity drops by -0.4 m/s, 3. Time after peak velocity drops by -0.6 m/s, and 4. When the integrated distance from the GPS/GNSS signal achieves 40-m. Goodness-of-fit of each sprint end detection method was determined using the residual sum of squares (RSS) to demonstrate the error of the FVP modeling with the sprint data from the GPS/GNSS system. Inter-trial reliability (from 2 trials) was assessed utilizing intraclass correlation coefficients (ICC). For goodness-of-fit results, the end detection technique that used the time when peak velocity was achieved (zero acceleration) had the lowest RSS values, followed by -0.4 and -0.6 velocity decay, and 40-m end had the highest RSS values. For intertrial reliability, the end of sprint detection techniques that were defined as the time at (method 1) or shortly after (method 2 and 3) when MSS was achieved had very large to near perfect ICC and the time at the 40 m integrated distance (method 4) had large to very large ICCs. Peak velocity was reached at 29.52 ± 4.02-m. Therefore, sport scientists should implement end of sprint detection either when peak velocity is determined or shortly after to improve goodness of fit to achieve reliable between trial FVP profile metrics. Although, more robust processing and modeling procedures should be developed in future research to improve sprint model fitting. This protocol was seamlessly integrated into the usual training which shows promise for sprint monitoring in the field with this technology.

Keywords: automated, biomechanics, team-sports, sprint

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Authors: George Charkoftakis, Panagiotis Liosatos, Nicolas-Alexander Tatlas, Dimitrios Goustouridis, Stelios M. Potirakis

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E-maintenance is a relatively new concept, generally referring to maintenance management by monitoring assets over the Internet. One of the key links in the chain of an e-maintenance system is data acquisition and transmission. Specifically for the case of a fleet of heavy-duty vehicles, where the main challenge is the diversity of the vehicles and vehicle-embedded self-diagnostic/reporting technologies, the design of the data acquisition and transmission unit is a demanding task. This clear if one takes into account that a heavy-vehicles fleet assortment may range from vehicles with only a limited number of analog sensors monitored by dashboard light indicators and gauges to vehicles with plethora of sensors monitored by a vehicle computer producing digital reporting. The present work proposes an adaptable internet of things (IoT) sensor node that is capable of addressing this challenge. The proposed sensor node architecture is based on the increasingly popular single-board computer – expansion boards approach. In the proposed solution, the expansion boards undertake the tasks of position identification by means of a global navigation satellite system (GNSS), cellular connectivity by means of 3G/long-term evolution (LTE) modem, connectivity to on-board diagnostics (OBD), and connectivity to analog and digital sensors by means of a novel design of expansion board. Specifically, the later provides eight analog plus three digital sensor channels, as well as one on-board temperature / relative humidity sensor. The specific device offers a number of adaptability features based on appropriate zero-ohm resistor placement and appropriate value selection for limited number of passive components. For example, although in the standard configuration four voltage analog channels with constant voltage sources for the power supply of the corresponding sensors are available, up to two of these voltage channels can be converted to provide power to the connected sensors by means of corresponding constant current source circuits, whereas all parameters of analog sensor power supply and matching circuits are fully configurable offering the advantage of covering a wide variety of industrial sensors. Note that a key feature of the proposed sensor node, ensuring the reliable operation of the connected sensors, is the appropriate supply of external power to the connected sensors and their proper matching to the IoT sensor node. In standard mode, the IoT sensor node communicates to the data center through 3G/LTE, transmitting all digital/digitized sensor data, IoT device identity, and position. Moreover, the proposed IoT sensor node offers WiFi connectivity to mobile devices (smartphones, tablets) equipped with an appropriate application for the manual registration of vehicle- and driver-specific information, and these data are also forwarded to the data center. All control and communication tasks of the IoT sensor node are performed by dedicated firmware. It is programmed with a high-level language (Python) on top of a modern operating system (Linux). Acknowledgment: This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code: T1EDK- 01359, IntelligentLogger).

Keywords: IoT sensor nodes, e-maintenance, single-board computers, sensor expansion boards, on-board diagnostics

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Authors: Martin Alexander Eder, Sergei Semenov

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Many glass-epoxy composite structures, such as large utility wind turbine rotor blades (WTBs), comprise of adhesive joints with typically thick bond lines used to connect the different components during assembly. Performance optimization of rotor blades to increase power output by simultaneously maintaining high stiffness-to-low-mass ratios entails intricate geometries in conjunction with complex anisotropic material behavior. Consequently, adhesive joints in WTBs are subject to multiaxial stress states with significant stress gradients depending on the local joint geometry. Moreover, the dynamic aero-elastic interaction of the WTB with the airflow generates non-proportional, variable amplitude stress histories in the material. Empiricism shows that a prominent failure type in WTBs is high cycle fatigue failure of adhesive bond line interfaces, which in fact over time developed into a design driver as WTB sizes increase rapidly. Structural optimization employed at an early design stage, therefore, sets high demands on computationally efficient interface fatigue models capable of predicting the critical locations prone for interface failure. The numerical stress-based interface fatigue model presented in this work uses the Drucker-Prager criterion to compute three different damage indices corresponding to the two interface shear tractions and the outward normal traction. The two-parameter Drucker-Prager model was chosen because of its ability to consider shear strength enhancement under compression and shear strength reduction under tension. The governing interface damage index is taken as the maximum of the triple. The damage indices are computed through the well-known linear Palmgren-Miner rule after separate rain flow-counting of the equivalent shear stress history and the equivalent pure normal stress history. The equivalent stress signals are obtained by self-similar scaling of the Drucker-Prager surface whose shape is defined by the uniaxial tensile strength and the shear strength such that it intersects with the stress point at every time step. This approach implicitly assumes that the damage caused by the prevailing multiaxial stress state is the same as the damage caused by an amplified equivalent uniaxial stress state in the three interface directions. The model was implemented as Python plug-in for the commercially available finite element code Abaqus for its use with solid elements. The model was used to predict the interface damage of an adhesively bonded, tapered glass-epoxy composite cantilever I-beam tested by LM Wind Power under constant amplitude compression-compression tip load in the high cycle fatigue regime. Results show that the model was able to predict the location of debonding in the adhesive interface between the webfoot and the cap. Moreover, with a set of two different constant life diagrams namely in shear and tension, it was possible to predict both the fatigue lifetime and the failure mode of the sub-component with reasonable accuracy. It can be concluded that the fidelity, robustness and computational efficiency of the proposed model make it especially suitable for rapid fatigue damage screening of large 3D finite element models subject to complex dynamic load histories.

Keywords: adhesive, fatigue, interface, multiaxial stress

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

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