Search results for: error estimates

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

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

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

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Authors: A. Alyaaria, b, J. P. Wignerona, A. Ducharneb, Y. Kerrc, P. de Rosnayd, R. de Jeue, A. Govinda, A. Al Bitarc, C. Albergeld, J. Sabaterd, C. Moisya, P. Richaumec, A. Mialonc

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Global Level-3 surface soil moisture (SSM) maps from the passive microwave soil moisture and Ocean Salinity satellite (SMOSL3) have been released. To further improve the Level-3 retrieval algorithm, evaluation of the accuracy of the spatio-temporal variability of the SMOS Level 3 products (referred to here as SMOSL3) is necessary. In this study, a comparative analysis of SMOSL3 with a SSM product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) computed by implementing the Land Parameter Retrieval Model (LPRM) algorithm, referred to here as AMSRM, is presented. The comparison of both products (SMSL3 and AMSRM) were made against SSM products produced by a numerical weather prediction system (SM-DAS-2) at ECMWF (European Centre for Medium-Range Weather Forecasts) for the 03/2010-09/2011 period at global scale. The latter product was considered here a 'reference' product for the inter-comparison of the SMOSL3 and AMSRM products. Three statistical criteria were used for the evaluation, the correlation coefficient (R), the root-mean-squared difference (RMSD), and the bias. Global maps of these criteria were computed, taking into account vegetation information in terms of biome types and Leaf Area Index (LAI). We found that both the SMOSL3 and AMSRM products captured well the spatio-temporal variability of the SM-DAS-2 SSM products in most of the biomes. In general, the AMSRM products overestimated (i.e., wet bias) while the SMOSL3 products underestimated (i.e., dry bias) SSM in comparison to the SM-DAS-2 SSM products. In term of correlation values, the SMOSL3 products were found to better capture the SSM temporal dynamics in highly vegetated biomes ('Tropical humid', 'Temperate Humid', etc.) while best results for AMSRM were obtained over arid and semi-arid biomes ('Desert temperate', 'Desert tropical', etc.). When removing the seasonal cycles in the SSM time variations to compute anomaly values, better correlation with the SM-DAS-2 SSM anomalies were obtained with SMOSL3 than with AMSRM, in most of the biomes with the exception of desert regions. Eventually, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (slightly better) SSM products correlate well with the SM-DAS2 products over regions with sparse vegetation for values of LAI < 1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI>1, SMOSL3 outperformed AMSRM with respect to SM-DAS-2: SMOSL3 had almost consistent performances up to LAI = 6, whereas AMSRM performance deteriorated rapidly with increasing values of LAI.

Keywords: remote sensing, microwave, soil moisture, AMSR-E, SMOS

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: U. Waqas, M. F. Ahmed, A. Mehmood, M. A. Rashid

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In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data quality, Null hypothesis, Seismic lines, Seismic reflection survey

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.

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In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor Hobson Talysurf tester, micro Vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer.

Keywords: hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness

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Authors: Anna Słupik, Patryk Wąsowski, Anna Mosiołek, Dariusz Białoszewski

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Introduction: Kinesiology Taping is one of the most popular techniques used for treatment and supporting physiological processes in sports medicine and physiotherapy. Often it is used to sensorimotor skills of lower limbs by athletes. The aim of the study was to determine the effect of the application of muscle Kinesiology Taping to feel the position setting in motion the joint active. Material and methods: The study involved 50 healthy people between 18 and 30 years of age, 30 men and 20 women (mean age 23.24 years). The participants were divided into two groups. The study group was qualified for Kinesiology Taping application (muscle application, type Y, for quadriceps femoris muscle), while the remaining people used the application made of plaster (placebo group). Testing was performed prior to applying taping, with the applied application (after 30 minutes), then 24 hours after wearing, and after removing the tape. Each evaluated joint position sense - Error of Active Reproduction of Joint Position. Results: The survey revealed no significant differences in measurement between the study group and the placebo group (p> 0.05). No significant differences in time taking into account all four measurements in the group with the applied CT application, which was supported by pairs (p> 0.05). Also in the placebo group showed no significant differences over time (p> 0.05). There was no significant difference between the errors committed in the direction of flexion and extension. Conclusions: 1. Application muscle Kinesiology Taping had no significant effect on the knee joint proprioception. Its use in order to improve sensorimotor seems therefore unjustified. 2. There are no differences between applications Kinesiology Taping and placebo indicates that the clinical effect of stretch tape is minimal or absent. 3. The results are the basis for the continuation of prospective, randomized trials of numerous and study group.

Keywords: joint position sense, kinesiology taping, knee joint, proprioception

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Authors: Jianan Sun, Ziwen Ye

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Multidimensional computerized adaptive testing (MCAT) is a popular research topic in psychometrics. It is important for practitioners to clearly know the item-trait patterns of administered items when a test like MCAT is operated. Item-trait pattern recognition refers to detecting which latent traits in a psychological test are measured by each of the specified items. If the item-trait patterns of the replenished items in MCAT item pool are well detected, the interpretability of the items can be improved, which can further promote the abilities of the examinees who attending the MCAT to be accurately estimated. This research explores to solve the item-trait pattern recognition problem of the replenished items in MCAT item pool from the perspective of statistical variable selection. The popular multidimensional item response theory model, multidimensional two-parameter logistic model, is assumed to fit the response data of MCAT. The proposed method uses the least absolute shrinkage and selection operator (LASSO) to detect item-trait patterns of replenished items based on the essential information of item responses and ability estimates of examinees collected from a designed MCAT procedure. Several advantages of the proposed method are outlined. First, the proposed method does not strictly depend on the relative order between the replenished items and the selected operational items, so it allows the replenished items to be mixed into the operational items in reasonable order such as considering content constraints or other test requirements. Second, the LASSO used in this research improves the interpretability of the multidimensional replenished items in MCAT. Third, the proposed method can exert the advantage of shrinkage method idea for variable selection, so it can help to check item quality and key dimension features of replenished items and saves more costs of time and labors in response data collection than traditional factor analysis method. Moreover, the proposed method makes sure the dimensions of replenished items are recognized to be consistent with the dimensions of operational items in MCAT item pool. Simulation studies are conducted to investigate the performance of the proposed method under different conditions for varying dimensionality of item pool, latent trait correlation, item discrimination, test lengths and item selection criteria in MCAT. Results show that the proposed method can accurately detect the item-trait patterns of the replenished items in the two-dimensional and the three-dimensional item pool. Selecting enough operational items from the item pool consisting of high discriminating items by Bayesian A-optimality in MCAT can improve the recognition accuracy of item-trait patterns of replenished items for the proposed method. The pattern recognition accuracy for the conditions with correlated traits is better than those with independent traits especially for the item pool consisting of comparatively low discriminating items. To sum up, the proposed data-driven method based on the LASSO can accurately and efficiently detect the item-trait patterns of replenished items in MCAT.

Keywords: item-trait pattern recognition, least absolute shrinkage and selection operator, multidimensional computerized adaptive testing, variable selection

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Authors: Rishi Pal Singh

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In Indian Scenario, Organic agriculture is growing by the conscious efforts of inspired people who are able to create the best promising relationship between the earth and men. Nowadays, the major challenge is its entry into the policy-making framework, its entry into the global market and weak sensitization among the farmers. But, during the last two decades, the contamination in environment and food which is linked with the bad agricultural potential/techniques has diverted the mind set of farmers towards the organic farming. In the view of above concept, a small-scale project has been installed to promote the 20 farmers from the Kunjura and surrounding villages for organic farming. This project is working since from the last 3 crops (starting from October, 2016) and found that it can meet both demands and complete development of rural areas. Farmers of this concept are working on the principles such that the nature never demands unreasonable quantities of water, mining and to destroy the microbes and other organisms. As per details of Organic Monitor estimates, global sales reached in billion in the present analysis. In this initiative, firstly, wheat and rice were considered for farming and observed that the production of crop has grown almost 10-15% per year from the last crop production. This is not linked only with the profit or loss but also emphasized on the concept of health, ecology, fairness and care of soil enrichment. Several techniques were used like use of biological fertilizers instead of chemicals, multiple cropping, temperature management, rain water harvesting, development of own seed, vermicompost and integration of animals. In the first year, to increase the fertility of the land, legumes (moong, cow pea and red gram) were grown in strips for the 60, 90 and 120 days. Simultaneously, the mixture of compost and vermicompost in the proportion of 2:1 was applied at the rate of 2.0 ton per acre which was enriched with 5 kg Azotobacter and 5 kg Rhizobium biofertilizer. To complete the amount of phosphorus, 250 kg rock phosphate was used. After the one month, jivamrut can be used with the irrigation water or during the rainy days. In next season, compost-vermicompost mixture @ 2.5 ton/ha was used for all type of crops. After the completion of this treatment, now the soil is ready for high value ordinary/horticultural crops. The amount of above stated biofertilizers, compost-vermicompost and rock phosphate may be increased for the high alternative fertilizers. The significance of the projects is that now the farmers believe in cultural alternative (use of disease-free their own seed, organic pest management), maintenance of biodiversity, crop rotation practices and health benefits of organic farming. This type of organic farming projects should be installed at the level of gram/block/district administration.

Keywords: organic farming, Kunjpura, compost, bio-fertilizers

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Authors: Ephraim Ugwu, Christopher Ehinomen

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Despite efforts by the Nigerian government to harmonize the macroeconomic policy implementations by establishing various committees to resolve disputes between the fiscal and monetary authorities, it is still evident that the federal government had continued its expansionary policy by increasing spending, thus creating huge budget deficit. This study evaluates the effect of macroeconomic policy coordination on economic growth uncertainty in Nigeria from 1980 to 2020. Employing the Auto regressive distributed lag (ARDL) bound testing procedures, the empirical results shows that the error correction term, ECM(-1), indicates a negative sign and is significant statistically with the t-statistic value of (-5.612882 ). Therefore, the gap between long run equilibrium value and the actual value of the dependent variable is corrected with speed of adjustment equal to 77% yearly. The long run coefficient results showed that the estimated coefficients of the intercept term indicates that other things remains the same (ceteris paribus), the economics growth uncertainty will continue reduce by 7.32%. The coefficient of the fiscal policy variable, PUBEXP, indicates a positive sign and significant statistically. This implies that as the government expenditure increases by 1%, economic growth uncertainty will increase by 1.67%. The coefficient of monetary policy variable MS also indicates a positive sign and insignificant statistically. The coefficients of merchandise trade variable, TRADE and exchange rate EXR show negative signs and significant statistically. This indicate that as the country’s merchandise trade and the rate of exchange increases by 1%, the economic growth uncertainty reduces by 0.38% and 0.06%, respectively. This study, therefore, advocate for proper coordination of monetary, fiscal and exchange rate policies in order to actualize the goal of achieving a stable economic growth.

Keywords: macroeconomic, policy coordination, growth uncertainty, ARDL, Nigeria

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Authors: Biki Sarmah, Priyanko Raj Mudiar

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In the modern world of remotely interactive virtual reality-based learning and teaching, including professional skill-building training and acquisition practices, as well as data acquisition and robotic systems, the revolutionary application or implementation of field-programmable neurostimulator aids and first-hand interactive sensitisation techniques into 3D holographic audio-visual platforms have been a coveted dream of many scholars, professionals, scientists, and students. Integration of 'kinaesthetic vibrotactile haptic perception' along with an actuated step magnitude contact profiloscopy in augmented reality-based learning platforms and professional training can be implemented by using an extremely calculated and well-coordinated image telemetry including remote data mining and control technique. A real-time, computer-aided (PLC-SCADA) field calibration based algorithm must be designed for the purpose. But most importantly, in order to actually realise, as well as to 'interact' with some 3D holographic models displayed over a remote screen using remote laser image telemetry and control, all spatio-physical parameters like cardinal alignment, gyroscopic compensation, as well as surface profile and thermal compositions, must be implemented using zero-order type 1 actuators (or transducers) because they provide zero hystereses, zero backlashes, low deadtime as well as providing a linear, absolutely controllable, intrinsically observable and smooth performance with the least amount of error compensation while ensuring the best ergonomic comfort ever possible for the users.

Keywords: haptic feedback, kinaesthetic vibrotactile 3D design, medical simulation training, piezo diaphragm based actuator

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Authors: Nilson Rogério Da Silva, Ingrid Casagrande, Isabela Chicarelli Amaro Santos

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Introduction: People with Autism Spectrum Disorder (ASD) may face difficulties in social inclusion in different segments of society, especially in entering and staying at work. In Brazil, although there is legislation that equates it to the condition of disability, the number of people at work is still low. The United Nations estimates that more than 80 percent of adults with autism are jobless. In Brazil, the scenario is even more nebulous because there is no control and tracking of accurate data on the number of individuals with autism and how many of these are inserted in the labor market. Pereira and Goyos (2019) found that there is practically no scientific production about people with ASD in the labor market. Objective: To describe the experience of people with ASD inserted in the work, facilities and difficulties found in the professional exercise and the strategies used to maintain the job. Methodology: The research was approved by the Research Ethics Committee. As inclusion criteria for participation, the professional should accept to participate voluntarily, be over 18 years of age and have had some experience with the labor market. As exclusion criteria, being under 18 years of age and having never worked in a work activity. Participated in the research of 04 people with a diagnosis of ASD, aged 22 to 32 years. For data collection, an interview script was used that addressed: 1) General characteristics of the participants; 2) Family support; 3) School process; 4) Insertion in the labor market; 5) Exercise of professional activity; (6) Future and Autism; 7) Possible coping strategies. For the analysis of the data obtained, the full transcription of the interviews was performed and the technique of Content Analysis was performed. Results: The participants reported problems in different aspects: In the school environment: difficulty in social relationships, and Bullying. Lack of adaptation to the school curriculum and the structure of the classroom; In the Faculty: difficulty in following the activities, ealizar group work, meeting deadlines and establishing networking; At work: little adaptation in the work environment, difficulty in establishing good professional bonds, difficulty in accepting changes in routine or operational processes, difficulty in understanding veiled social rules. Discussion: The lack of knowledge about what disability is and who the disabled person is leads to misconceptions and negatives regarding their ability to work and in this context, people with disabilities need to constantly prove that they are able to work, study and develop as a human person, which can be classified as ableism. The adaptations and the use of technologies to facilitate the performance of people with ASD, although guaranteed in national legislation, are not always available, highlighting the difficulties and prejudice. Final Considerations: The entry and permanence of people with ASD at work still constitute a challenge to be overcome, involving changes in society in general, in companies, families and government agencies.

Keywords: autism spectrum disorder (ASD), work, disability, autism

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Authors: Haruo Okabayashi

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Human biological signals (pulse wave and brain wave, etc.) have a rhythm which shows fluctuations. This study investigates the relationship between fluctuations of biological signals which are shown by a finger plethysmogram (i.e., finger pulse wave) in conversation and anthropophobic tendency, and identifies whether the fluctuation could be an index of mental health. 32 college students participated in the experiment. The finger plethysmogram of each subject was measured in the following conversation situations: Fun memory talking/listening situation and regrettable memory talking/ listening situation for three minutes each. Lyspect 3.5 was used to collect the data of the finger plethysmogram. Since Lyspect calculates the Lyapunov spectrum, it is possible to obtain the largest Lyapunov exponent (LLE). LLE is an indicator of the fluctuation and shows the degree to which a measure is going away from close proximity to the track in a dynamical system. Before the finger plethysmogram experiment, each participant took the psychological test questionnaire “Anthropophobic Scale.” The scale measures the social phobia trend close to the consciousness of social phobia. It is revealed that there is a remarkable relationship between the fluctuation of the finger plethysmography and anthropophobic tendency scale in talking about a regrettable story in conversation: The participants (N=15) who have a low anthropophobic tendency show significantly more fluctuation of finger pulse waves than the participants (N=17) who have a high anthropophobic tendency (F (1, 31) =5.66, p<0.05). That is, the participants who have a low anthropophobic tendency make conversation flexibly using large fluctuation of biological signal; on the other hand, the participants who have a high anthropophobic tendency constrain a conversation because of small fluctuation. Therefore, fluctuation is not an error but an important drive to make better relationships with others and go towards the development of interaction. In considering mental health, the fluctuation of biological signals would be an important indicator.

Keywords: anthropophobic tendency, finger plethymogram, fluctuation of biological signal, LLE

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Authors: Van-Thanh Ho, Jaiyoung Ryu

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In soft robotics, the optimization of fluid dynamics through pneumatic methods plays a pivotal role in enhancing operational efficiency and reducing energy loss. This is particularly crucial when replacing conventional techniques such as cable-driven electromechanical systems. The pneumatic model employed in this study represents a sophisticated framework designed to efficiently channel pressure from a high-pressure reservoir to various muscle locations on the robot's body. This intricate network involves a branching system of tubes. The study introduces a comprehensive pneumatic model, encompassing the components of a reservoir, tubes, and Pneumatically Actuated Muscles (PAM). The development of this model is rooted in the principles of shock tube theory. Notably, the study leverages experimental data to enhance the understanding of the interplay between the PAM structure and the surrounding fluid. This improved interactive approach involves the use of morphing motion, guided by a contraction function. The study's findings demonstrate a high degree of accuracy in predicting pressure distribution within the PAM. The model's predictive capabilities ensure that the error in comparison to experimental data remains below a threshold of 10%. Additionally, the research employs a machine learning model, specifically a surrogate model based on the Kriging method, to assess and quantify uncertainty factors related to the initial reservoir pressure and tube diameter. This comprehensive approach enhances our understanding of pneumatic soft robotics and its potential for improved operational efficiency.

Keywords: pneumatic artificial muscles, pressure drop, morhing motion, branched network, surrogate model

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Authors: Kumar Dron Shrivastav, Ankan Mukherjee Das, Arti Taneja, Harpreet Singh, Priya Ranjan, Rajiv Janardhanan

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Cervical cancer is one of the most common cancer among women worldwide which can be cured if detected early. Manual pathology which is typically utilized at present has many limitations. The current gold standard for cervical cancer diagnosis is exhaustive and time-consuming because it relies heavily on the subjective knowledge of the oncopathologists which leads to mis-diagnosis and missed diagnosis resulting false negative and false positive. To reduce time and complexities associated with early diagnosis, we require an interactive diagnostic tool for early detection particularly in developing countries where cervical cancer incidence and related mortality is high. Incorporation of digital pathology in place of manual pathology for cervical cancer screening and diagnosis can increase the precision and strongly reduce the chances of error in a time-specific manner. Thus, we propose a robust and interactive cervical cancer image analysis and diagnostic tool, which can categorically process both histopatholgical and cytopathological images to identify abnormal cells in the least amount of time and settings with minimum resources. Furthermore, incorporation of a set of specific parameters that are typically referred to for identification of abnormal cells with the help of open source software -’R’ is one of the major highlights of the tool. The software has the ability to automatically identify and quantify the morphological features, color intensity, sensitivity and other parameters digitally to differentiate abnormal from normal cells, which may improve and accelerate screening and early diagnosis, ultimately leading to timely treatment of cervical cancer.

Keywords: cervical cancer, early detection, digital Pathology, screening

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Authors: Yusuf Dawa Sidi, Abdulrahman Bulama Bizi

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The most dependable and precise source of climate data is often gauge observation. However, long-term records of gauge observations, on the other hand, are unavailable in many regions around the world. In recent years, a number of gridded climate datasets with high spatial and temporal resolutions have emerged as viable alternatives to gauge-based measurements. However, it is crucial to thoroughly evaluate their performance prior to utilising them in hydroclimatic applications. Therefore, this study aims to assess the effectiveness of NASA Prediction of Worldwide Energy Resources (NASA POWER) and Climate Research Unit (CRU) datasets in accurately estimating precipitation and temperature patterns within the dry region of Nigeria from 1990 to 2020. The study employs widely used statistical metrics and the Standardised Precipitation Index (SPI) to effectively capture the monthly variability of precipitation and temperature and inter-annual anomalies in rainfall. The findings suggest that CRU exhibited superior performance compared to NASA POWER in terms of monthly precipitation and minimum and maximum temperatures, demonstrating a high correlation and much lower error values for both RMSE and MAE. Nevertheless, NASA POWER has exhibited a moderate agreement with gauge observations in accurately replicating monthly precipitation. The analysis of the SPI reveals that the CRU product exhibits superior performance compared to NASA POWER in accurately reflecting inter-annual variations in rainfall anomalies. The findings of this study indicate that the CRU gridded product is often regarded as the most favourable gridded precipitation product.

Keywords: CRU, climate change, precipitation, SPI, temperature

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Authors: E. Obreque-Slier, V. Contreras-Cortez, R. López-Solís

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Astringency is a drying, roughing, and sometimes puckering sensation that is experienced on the various oral surfaces during or immediately after tasting foods. This sensation has been closely related to the interaction and precipitation between salivary proteins and polyphenols, specifically flavanols or proanthocyanidins. In addition, the type and concentration of proanthocyanidin influences significantly the intensity of the astringency and consequently the protein/proanthocyanidin interaction. However, most of the studies are based on the interaction between saliva and highly complex polyphenols, without considering the effect of monomeric proanthoancyanidins present in different foods. The aim of this study was to evaluate the effect of different monomeric proanthocyanidins on the diffusion and precipitation of salivary proteins. Thus, solutions of catechin, epicatechin, epigallocatechin and gallocatechin (0, 2.0, 4.0, 6.0, 8.0 and 10 mg/mL) were mixed with human saliva (1: 1 v/v). After incubation for 5 min at room temperature, 15 µL aliquots of each mix were dotted on a cellulose membrane and allowed to dry spontaneously at room temperature. The membrane was fixed, rinsed and stained for proteins with Coomassie blue. After exhaustive washing in 7% acetic acid, the membrane was rinsed once in distilled water and dried under a heat lamp. Both diffusion area and stain intensity of the protein spots were semiqualitative estimates for protein-tannin interaction (diffusion test). The rest of the whole saliva-phenol solution mixtures of the diffusion assay were centrifuged, and 15-μL aliquots from each of the supernatants were dotted on a cellulose membrane. The membrane was processed for protein staining as indicated above. The blue-stained area of protein distribution corresponding to each of the extract dilution-saliva mixtures was quantified by Image J 1.45 software. Each of the assays was performed at least three times. Initially, salivary proteins display a biphasic distribution on cellulose membranes, that is, when aliquots of saliva are placed on absorbing cellulose membranes, and free diffusion of saliva is allowed to occur, a non-diffusible protein fraction becomes surrounded by highly diffusible salivary proteins. In effect, once diffusion has ended, a protein-binding dye shows an intense blue-stained roughly circular area close to the spotting site (non-diffusible fraction) (NDF) which becomes surrounded by a weaker blue-stained outer band (diffusible fraction) (DF). Likewise, the diffusion test showed that epicatechin caused the complete disappearance of DF from saliva with 2 mg/mL. Also, epigallocatechin and gallocatechin caused a similar effect with 4 mg/mL, while catechin generated the same effect at 8 mg/mL. In the precipitation test, the use of epicatechin and gallocatechin generated evident precipitates at the bottom of the Eppendorf tubes. In summary, the flavanol type differentially affects the diffusion and precipitation of saliva, which would affect the sensation of astringency perceived by consumers.

Keywords: astringency, polyphenols, tannins, tannin-protein interaction

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Authors: Tasir Khan, Ishfaq Ahmad, Yejuan Wang, Muhammad Salam

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Distribution models of the wind speed data are essential to assess the potential wind speed energy because it decreases the uncertainty to estimate wind energy output. Therefore, before performing a detailed potential energy analysis, the precise distribution model for data relating to wind speed must be found. In this research, material from numerous criteria goodness-of-fits, such as Kolmogorov Simonov, Anderson Darling statistics, Chi-Square, root mean square error (RMSE), AIC and BIC were combined finally to determine the wind speed of the best-fitted distribution. The suggested method collectively makes each criterion. This method was useful in a circumstance to fitting 14 distribution models statistically with the data of wind speed together at four sites in Pakistan. The consequences show that this method provides the best source for selecting the most suitable wind speed statistical distribution. Also, the graphical representation is consistent with the analytical results. This research presents three estimation methods that can be used to calculate the different distributions used to estimate the wind. In the suggested MLM, MOM, and MLE the third-order moment used in the wind energy formula is a key function because it makes an important contribution to the precise estimate of wind energy. In order to prove the presence of the suggested MOM, it was compared with well-known estimation methods, such as the method of linear moment, and maximum likelihood estimate. In the relative analysis, given to several goodness-of-fit, the presentation of the considered techniques is estimated on the actual wind speed evaluated in different time periods. The results obtained show that MOM certainly provides a more precise estimation than other familiar approaches in terms of estimating wind energy based on the fourteen distributions. Therefore, MOM can be used as a better technique for assessing wind energy.

Keywords: wind-speed modeling, goodness of fit, maximum likelihood method, linear moment

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Authors: Masoud Ghaemi, Mohammadjafar Hedayati, Faezeh Yousefzadeh, Hoseinali Heydarzadeh

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Careful monitoring in the earth dams to measure deformation caused by settlement and movement has always been a concern for engineers in the field. In order to measure settlement and deformation of earth dams, usually, the precision instruments of settlement set and combined Inclinometer that is commonly referred to IS instrument will be used. In some dams, because the thickness of alluvium is high and there is no possibility of alluvium removal (technically and economically and in terms of performance), there is no possibility of placing the end of IS instrument (precision instruments of Inclinometer-settlement set) in the rock foundation. Inevitably, have to accept installing pipes in the weak and deformable alluvial foundation that leads to errors in the calculation of the actual settlement (absolute settlement) in different parts of the dam body. The purpose of this paper is to present new and refine criteria for predicting settlement and deformation in earth dams. The study is based on conditions in three dams with a deformation quite alluvial (Agh Chai, Narmashir and Gilan-e Gharb) to provide settlement criteria affected by the alluvial foundation. To achieve this goal, the settlement of dams was simulated by using the finite difference method with FLAC3D software, and then the modeling results were compared with the reading IS instrument. In the end, the caliber of the model and validate the results, by using regression analysis techniques and scrutinized modeling parameters with real situations and then by using MATLAB software and CURVE FITTING toolbox, new criteria for the settlement based on elasticity modulus, cohesion, friction angle, the density of earth dam and the alluvial foundation was obtained. The results of these studies show that, by using the new criteria measures, the amount of settlement and deformation for the dams with alluvial foundation can be corrected after instrument readings, and the error rate in reading IS instrument can be greatly reduced.

Keywords: earth-fill dam, foundation, settlement, finite difference, MATLAB, curve fitting

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Authors: Du Kim, Nelson Baro

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According to a report of International Atomic Energy Agency (IAEA), there are approximately 437 nuclear power stations are in operation in the present around the world in order to meet increasing energy demands. Indeed, nearly, a third of the world’s energy demands are met through nuclear power because it is one of the most efficient and long-lasting sources of energy. However, there are also consequences when a major event takes place at a nuclear power station. Over the past years, a few major nuclear accidents have occurred around the world. According to a report of International Nuclear and Radiological Event Scale (INES), there are six nuclear accidents that are considered to be high level (risk) of the events: Fukushima Dai-chi (Level 7), Chernobyl (Level 7), Three Mile Island (Level 5), Windscale (Level 5), Kyshtym (Level 6) and Chalk River (Level 5). Today, many people still have doubt about using nuclear power. There is growing number of people who are against nuclear power after the serious accident occurred at the Fukushima Dai-chi nuclear power plant in Japan. In other words, there are public concerns about radiation impacts which emphasize Linear-No-Threshold (LNT) Issues, Radiation Health Effects, Radiation Protection and Social Impacts. This paper will address those keywords by looking back at the history of these major nuclear accidents worldwide, based on INES. This paper concludes that all major mistake from nuclear accidents are preventable due to the fact that most of them are caused by human error. In other words, the human factor has played a huge role in the malfunction and occurrence of most of those events. The correct handle of a crisis is determined, by having a good radiation protection program in place, it’s what has a big impact on society and determines how acceptable people are of nuclear.

Keywords: linear-no-threshold (LNT) issues, radiation health effects, radiation protection, social impacts

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Authors: Yakubu Suleiman, S. A. Musa

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The research was conducted to determine the physical characteristics of banana fruits that influenced consumer preferences for the fruit in Northern Nigeria. Socio-economic characteristics of the respondents were also identified. Simple descriptive statistics and Hedonic prices model were used to analyze the data collected for socio-economic and consumer preference respectively with the aid of 1000 structured questionnaires. The result revealed the value of R2 to be 0.633, meaning that, 63.3% of the variation in the banana price was brought about by the explanatory variables included in the model and the variables are: colour, size, degree of ripeness, softness, surface blemish, cleanliness of the fruits, weight, length, and cluster size of fruits. However, the remaining 36.7% could be attributed to the error term or random disturbance in the model. It could also be seen from the calculated result that the intercept was 1886.5 and was statistically significant (P < 0.01), meaning that about N1886.5 worth of banana fruits could be bought by consumers without considering the variables of banana included in the model. Moreover, consumers showed that they have significant preference for colours, size, degree of ripeness, softness, weight, length and cluster size of banana fruits and they were tested to be significant at either P < 0.01, P < 0.05, and P < 0.1 . Moreover, the result also shows that consumers did not show significance preferences to surface blemish, cleanliness and variety of the banana fruit as all of them showed non-significance level with negative signs. Based on the findings of the research, it is hereby recommended that plant breeders and research institutes should concentrate on the production of banana fruits that have those physical characteristics that were found to be statistically significance like cluster size, degree of ripeness,’ softness, length, size, and skin colour.

Keywords: analysis, consumers, preference, variables

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Authors: Titanilla Komenda

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Human-machine-collaboration defines a direct interaction between humans and machines to fulfil specific tasks. Those so-called collaborative machines are used without fencing and interact with humans in predefined workspaces. Even though, human-machine-collaboration enables a flexible adaption to variable degrees of freedom, industrial applications are rarely found. The reasons for this are not technical progress but rather limitations in planning processes ensuring safety for operators. Until now, humans and machines were mainly considered separately in the planning process, focusing on ergonomics and system performance respectively. Within human-machine-collaboration, those aspects must not be seen in isolation from each other but rather need to be analysed in interaction. Furthermore, a simulation model is needed that can validate the system performance and ensure the safety for the operator at any given time. Following on from this, a holistic simulation model is presented, enabling a simulative representation of collaborative tasks – including both, humans and machines. The presented model does not only include a geometry and a motion model of interacting humans and machines but also a numerical behaviour model of humans as well as a Boole’s probabilistic sensor model. With this, error scenarios can be simulated by validating system behaviour in unplanned situations. As these models can be defined on the basis of Failure Mode and Effects Analysis as well as probabilities of errors, the implementation in a collaborative model is discussed and evaluated regarding limitations and simulation times. The functionality of the model is shown on industrial applications by comparing simulation results with video data. The analysis shows the impact of considering human factors in the planning process in contrast to only meeting system performance. In this sense, an optimisation function is presented that meets the trade-off between human and machine factors and aids in a successful and safe realisation of collaborative scenarios.

Keywords: human-machine-system, human-robot-collaboration, safety, simulation

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Choki, Supatchai Boonpratham, Suwannee Luppanapornlarp

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The objective of this study was to find a correlation between cephalometric measurements and visual perception of facial profile in skeletal type II patients. In this study, 250 lateral cephalograms of female patients from age, 20 to 22 years were analyzed. The profile outlines of all the samples were hand traced and transformed into silhouettes by the principal investigator. Profile ratings were done by 9 orthodontists on Visual Analogue Scale from score one to ten (increasing level of convexity). 37 hard issue and soft tissue cephalometric measurements were analyzed by the principal investigator. All the measurements were repeated after 2 weeks interval for error assessment. At last, the rankings of visual perceptions were correlated with cephalometric measurements using Spearman correlation coefficient (P < 0.05). The results show that the increase in facial convexity was correlated with higher values of ANB (A point, nasion and B point), AF-BF (distance from A point to B point in mm), L1-NB (distance from lower incisor to NB line in mm), anterior maxillary alveolar height, posterior maxillary alveolar height, overjet, H angle hard tissue, H angle soft tissue and lower lip to E plane (absolute correlation values from 0.277 to 0.711). In contrast, the increase in facial convexity was correlated with lower values of Pg. to N perpendicular and Pg. to NB (mm) (absolute correlation value -0.302 and -0.294 respectively). From the soft tissue measurements, H angles had a higher correlation with visual perception than facial contour angle, nasolabial angle, and lower lip to E plane. In conclusion, the findings of this study indicated that the correlation of cephalometric measurements with visual perception was less than expected. Only 29% of cephalometric measurements had a significant correlation with visual perception. Therefore, diagnosis based solely on cephalometric analysis can result in failure to meet the patient’s esthetic expectation.

Keywords: cephalometric measurements, facial profile, skeletal type II, visual perception

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Authors: H. Taylan Selamlar, I. Yavuz, G. Yapar

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It is difficult to make predictions especially about the future and making accurate predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. Numerous number of forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), and still new methods are derived or inspired from them. After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness and accuracy as automatic forecasting procedures especially in the famous M-Competitions. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this study will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have similar structural forms and ATA can be easily adapted to all of the individual ES models however ATA has many advantages due to its innovative new weighting scheme. In this paper, the focus is on modeling the trend component and handling seasonality patterns by utilizing classical decomposition. Therefore, ATA method is expanded to higher order ES methods for additive, multiplicative, additive damped and multiplicative damped trend components. The proposed models are called ATA trended models and their predictive performances are compared to their counter ES models on the M3 competition data set since it is still the most recent and comprehensive time-series data collection available. It is shown that the models outperform their counters on almost all settings and when a model selection is carried out amongst these trended models ATA outperforms all of the competitors in the M3- competition for both short term and long term forecasting horizons when the models’ forecasting accuracies are compared based on popular error metrics.

Keywords: accuracy, exponential smoothing, forecasting, initial value

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Authors: Andreas Ateke Njoh, Shalom Tchokfe Ndoula, Amani Adidja, Germain Nguessan Menan, Annie Mengue, Eric Mboke, Hassan Ben Bachir, Sangwe Clovis Nchinjoh, Yauba Saidu, Laurent Cleenewerck De Kiev

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Vaccines serve a great deal in protecting the population globally. Vaccine products are subject to rigorous quality control and approval before use to ensure safety. Even if all actors take the required precautions, some people could still have adverse events following immunization (AEFI) caused by the vaccine composition or an error in its administration. AEFI underreporting is pronounced in low-income settings like Cameroon. The Country introduced electronic platforms to strengthen surveillance. With the introduction of many novel vaccines, like COVID-19 and the novel Oral Polio Vaccine (nOPV) 2, there was a need to monitor AEFI in the Country. A cross-sectional study was conducted from July to December 2022. Data on AEFI per region of Cameroon were reviewed for the past five years. Data were analyzed with MS Excel, and the results were presented in proportions. AEFI reporting was uncommon in Cameroon. With the introduction of novel vaccines in 2021, the health authorities engaged in new tools and training to capture cases. AEFI detected almost doubled using the open data kit (ODK) compared to previous platforms, especially following the introduction of the nOPV2 and COVID-19 vaccines. The AEFI rate was 1.9 and 160 per administered 100 000 doses of nOPV2 and COVID-19 vaccines, respectively. This mobile tool captured individual information for people with AEFI from all regions. The platform helped to identify common AEFI following the use of these new vaccines. The ODK mobile technology was vital in improving AEFI reporting and providing data to monitor using new vaccines in Cameroon.

Keywords: adverse events following immunization, cameroon, COVID-19 vaccines, nOPV, ODK

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Authors: Nguyen Chi Quang, Nguyen Duong Tri Nguyen

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This article is the first step to research and outline the structure of the geotechnical database in the geological survey of a power project; in the context of this report creating the database that has been carried out for the Bac Ai pumped storage hydropower project. For the purpose of providing a method of organizing and storing geological and topographic survey data and experimental results in a spatial database, the RockWorks software is used to bring optimal efficiency in the process of exploiting, using, and analyzing data in service of the design work in the power engineering consulting. Three-dimensional (3D) geotechnical models are created from the survey data: such as stratigraphy, lithology, porosity, etc. The results of the 3D geotechnical model in the case of Bac Ai pumped storage hydropower project include six closely stacked stratigraphic formations by Horizons method, whereas modeling of engineering geological parameters is performed by geostatistical methods. The accuracy and reliability assessments are tested through error statistics, empirical evaluation, and expert methods. The three-dimensional model analysis allows better visualization of volumetric calculations, excavation and backfilling of the lake area, tunneling of power pipelines, and calculation of on-site construction material reserves. In general, the application of engineering geological modeling makes the design work more intuitive and comprehensive, helping construction designers better identify and offer the most optimal design solutions for the project. The database always ensures the update and synchronization, as well as enables 3D modeling of geological and topographic data to integrate with the designed data according to the building information modeling. This is also the base platform for BIM & GIS integration.

Keywords: database, engineering geology, 3D Model, RockWorks, Bac Ai pumped storage hydropower project

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Authors: Wandera Stephen Ojumbo

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People living with hearing impairment in Uganda are one of the most excluded minority groups in the country. The Uganda National Association of the Deaf estimates that deaf people make up 3.4% of Uganda’s 43 million people. Deaf Children and youth often appear withdrawn because they face social stigma. In 2009, photojournalist Stephen Wandera Ojumbo conducted an exhibition in Kampala titled “Silent Voices with colourful Hearts” showcasing the life of deaf children at Uganda School for the Deaf, Ntinda, in order to create awareness of their plight, raising funds for the construction of a vocational centre for the deaf that didn’t continue their education due to: lack of funds, non-inclusive educational institutions, and for those who cannot read and write. These children, whose lives were exhibited in 2009, are currently youths. In Uganda, there are just five primary schools for the deaf (three of these are located in Kampala, the capital city), and barely five secondary schools for the deaf. At the moment, some deaf children only receive special needs training equivalent to primary seven levels and the majority don’t make it to secondary school education level due to the fact that English is a second language to them. There is a communication gap between speaking parents and deaf children, which leads to the breakage of family bonds. The deaf youth run away from their homes to form a community where they can communicate freely. Likewise, employment opportunities for the deaf are equally very limited. It’s for this reason that a follow-up photo exhibition was conducted to expose more about what the youthful deaf people and their guardians go through in Uganda to get jobs, live and fit in the community, how they communicate and get understood, bonding with families instead of running away to bond with fellow deaf persons. The photo exhibition under the theme “Loud Silence” was significant in showcasing the ability of deaf youths in Uganda and eliciting solutions to make a more inclusive society for the deaf. It is hoped that partners in development will join in for intervention. The methodology used included individual interviews with the deaf youth and their parents and caretakers; photography at household and community levels; document review at organizations working with the deaf; observations; and key informant interviews with relevant personnel working with the deaf. Some of the major findings include: i) Effective sign language communication is key in deaf education, family bonding, and developing a sense of belonging; ii) Love and intimacy can keep the deaf bound together; iii) Education is important; everybody should struggle even if alone; iv) Games and sports are a unifying factor and most loved among the deaf; and v) better communication skills build confidence in deaf youth. In conclusion, concerted efforts are still needed to make Uganda schools more inclusive for deaf persons. This will enable a secure future for deaf youths.

Keywords: deaf, education, excluded, photo exhibition

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Huixin Wei, Shibin Wang, Linan Li, Lei Zhou, Xinhao Tu

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Facial skin is a biomedical material with complex mechanical properties of anisotropy, viscoelasticity, and hyperelasticity. The mechanical properties of facial skin are crucial for a number of applications including facial plastic surgery, animation, dermatology, cosmetic industry, and impact biomechanics. Skin is a complex multi-layered material which can be broadly divided into three main layers, the epidermis, the dermis, and the hypodermis. Collagen fibers account for 75% of the dry weight of dermal tissue, and it is these fibers which are responsible for the mechanical properties of skin. Many research on the anisotropic mechanical properties are mainly concentrated on in vitro, but there is a great difference between in vivo and in vitro for mechanical properties of the skin. In this study, we presented a method to measure the mechanical properties of facial skin in vivo. Digital image correlation (DIC) and indentation tests were used to obtain the experiment data, including the deformation of facial surface and indentation force-displacement curve. Then, the experiment was simulated using a finite element (FE) model. Application of Computed Tomography (CT) and reconstruction techniques obtained the real tissue geometry. A three-dimensional FE model of facial skin, including a bi-layer system, was obtained. As the epidermis is relatively thin, the epidermis and dermis were regarded as one layer and below it was hypodermis in this study. The upper layer was modeled as a Gasser-Ogden-Holzapfel (GOH) model to describe hyperelastic and anisotropic behaviors of the dermis. The under layer was modeled as a linear elastic model. In conclusion, the material properties of two-layer were determined by minimizing the error between the FE data and experimental data.

Keywords: facial skin, indentation test, finite element, digital image correlation, computed tomography

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