Search results for: statistical modeling

Authors: Ifeoma C. Afiaenyi, Elizabeth K. Ngwu, Rufina N. B. Ayogu

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Diabetes has crept into the rural areas of Nigeria, causing devastating effects on its sufferers; most of them could not afford diabetic medications. Moringa oleifera has been used extensively in animal models to demonstrate its antilipidaemic and haematinic qualities; however, there is a scarcity of data on the effect of graded levels of Moringa oleifera leaves on the lipid profile and hematological parameters in human diabetic subjects. The study determined the effect of Moringa oleifera leaves on the lipid profile and hematological parameters of type 2 diabetic subjects in Ukehe, a rural Nigerian community. Twenty-four adult male and female diabetic subjects were purposively selected for the study. These subjects were shared into four groups of six subjects each. The diets used in the study were isocaloric. A control group (diabetics, group 1) was fed diets without Moringa oleifera leaves. Experimental groups 2, 3 and 4 received 20g, 40g and 60g of Moringa oleifera leaves daily, respectively, in addition to the diets. The subjects' lipid profile and hematological parameters were measured prior to the feeding trial and at the end of the feeding trial. The feeding trial lasted for fourteen days. The data obtained were analyzed using the computer program Statistical Product for Service Solution (SPSS) for windows version 21. A Paired-samples t-test was used to compare the means of values collected before and after the feeding trial within the groups and significance was accepted at p < 0.05. There was a non-significant (p > 0.05) decrease in the mean total cholesterol of the subjects in groups 1, 2 and 3 after the feeding trial. There was a non-significant (p > 0.05) decrease in the mean triglyceride levels of the subjects in group 1 after the feeding trial. Groups 1 and 3 subjects had a non-significant (p > 0.05) decrease in their mean low-density lipoprotein (LDL) cholesterol after the feeding trial. Groups 1, 2 and 4 had a significant (p < 0.05) increase in their mean high-density lipoprotein (HDL) cholesterol after the feeding trial. A significant (p < 0.05) decrease in the mean hemoglobin level was observed only in group 4 subjects. Similarly, there was a significant (p < 0.05) decrease in the mean packed cell volume of group 4 subjects. It was only in group 4 that a significant (p < 0.05) decrease in the mean white blood cells of the subjects was also observed. The changes observed in the parameters assessed were not dose-dependent. Therefore, a similar study of longer duration and more samples is imperative to authenticate these results.

Keywords: anemia, diabetic subjects, lipid profile, moringa oleifera

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Authors: Zhi Hao Chow, Cian Mulligan, Jack Walsh, Antonio Garzon Vico, Dimitar Krastev

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Drug development is resource-intensive, time-consuming, and increasingly expensive with each developmental stage. The success rates of drug development are also relatively low, and the resources committed are wasted with each failed candidate. As such, a reliable method of predicting the success of drug development is in demand. The hypothesis was that some examples of failed drug candidates are pushed through developmental pipelines based on false confidence and may possess common linguistic features identifiable through sentiment analysis. Here, the concept of using text analysis to discover such features in research publications and investor reports as predictors of success was explored. R studios were used to perform text mining and lexicon-based sentiment analysis to identify affective phrases and determine their frequency in each document, then using SPSS to determine the relationship between our defined variables and the accuracy of predicting outcomes. A total of 161 publications were collected and categorised into 4 groups: (i) Cancer treatment, (ii) Neurodegenerative disease treatment, (iii) Vaccines, and (iv) Others (containing all other drugs that do not fit into the 3 categories). Text analysis was then performed on each document using 2 separate datasets (BING and AFINN) in R within the category of drugs to determine the frequency of positive or negative phrases in each document. A relative positivity and negativity value were then calculated by dividing the frequency of phrases with the word count of each document. Regression analysis was then performed with SPSS statistical software on each dataset (values from using BING or AFINN dataset during text analysis) using a random selection of 61 documents to construct a model. The remaining documents were then used to determine the predictive power of the models. Model constructed from BING predicts the outcome of drug performance in clinical trials with an overall percentage of 65.3%. AFINN model had a lower accuracy at predicting outcomes compared to the BING model at 62.5% but was not effective at predicting the failure of drugs in clinical trials. Overall, the study did not show significant efficacy of the model at predicting outcomes of drugs in development. Many improvements may need to be made to later iterations of the model to sufficiently increase the accuracy.

Keywords: data analysis, drug development, sentiment analysis, text-mining

Procedia PDF Downloads 158

Authors: Meaghan Bowman

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Soldiers are taught about the importance of effective communication with repetition of the phrase, “Communication is key.” They receive training in preparing for, and carrying out, interactions between foreign and domestic leaders to gain crucial information about a mission. These interactions are known as Key Leader Engagements (KLEs). For the training of KLEs, doctrine mandates the skills needed to conduct these “engagements” such as how to: behave appropriately, identify key leaders, and employ effective strategies. Army officers in training learn how to confront leaders, what information to gain, and how to ask questions respectfully. Unfortunately, soldiers rarely learn how to formulate questions optimally. Since less complex questions are easier to understand, we hypothesize that semantic complexity affects content understanding, and that age and education levels may have an effect on one’s ability to form paraphrases and judge their quality. In this study, we looked at paraphrases of queries as well as judgments of both the paraphrases’ naturalness and their semantic similarity to the query. Queries were divided into three complexity categories based on the number of relations (the first number) and the number of knowledge graph edges (the second number). Two crowd-sourced tasks were completed by Amazon volunteer participants, also known as turkers, to answer the research questions: (i) Are more complex queries harder to paraphrase and judge and (ii) Do age and education level affect the ability to understand complex queries. We ran statistical tests as follows: MANOVA for query understanding and two-way ANOVA to understand the relationship between query complexity and education and age. A probe of the number of given-level queries selected for paraphrasing by crowd-sourced workers in seven age ranges yielded promising results. We found significant evidence that age plays a role and marginally significant evidence that education level plays a role. These preliminary tests, with output p-values of 0.0002 and 0.068, respectively, suggest the importance of content understanding in a communication skill set. This basic ability to communicate, which may differ by age and education, permits reproduction and quality assessment and is crucial in training soldiers for effective participation in KLEs.

Keywords: engagement, key leader, paraphrasing, query complexity, understanding

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Authors: M. A. Hernández-Ceballos, M. Marín-Ferrer, G. Cinelli, L. De Felice, T. Tollefsen, E. Nweke, P. V. Tognoli, S. Vanzo, M. De Cort

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This study analyses the latitudinal impact of the spatial and temporal distribution on the cosmogenic isotope 7Be in surface air along Europe. The long-term database of the 6 sampling sites (Ivalo, Helsinki, Berlin, Freiburg, Sevilla and La Laguna), that regularly provide data to the Radioactivity Environmental Monitoring (REM) network managed by the Joint Research Centre (JRC) in Ispra, were used. The selection of the stations was performed attending to different factors, such as 1) heterogeneity in terms of latitude and altitude, and 2) long database coverage. The combination of these two parameters ensures a high degree of representativeness of the results. In the later, the temporal coverage varies between stations, being used in the present study sampling stations with a database more or less continuously from 1984 to 2011. The mean values of 7Be activity concentration presented a spatial distribution value ranging from 2.0 ± 0.9 mBq/m3 (Ivalo, north) to 4.8 ± 1.5 mBq/m3 (La Laguna, south). An increasing gradient with latitude was observed from the north to the south, 0.06 mBq/m3. However, there was no correlation with altitude, since all stations are sited within the atmospheric boundary layer. The analyses of the data indicated a dynamic range of 7Be activity for solar cycle and phase (maximum or minimum), having been observed different impact on stations according to their location. The results indicated a significant seasonal behavior, with the maximum concentrations occurring in the summer and minimum in the winter, although with differences in the values reached and in the month registered. Due to the large heterogeneity in the temporal pattern with which the individual radionuclide analyses were performed in each station, the 7Be monthly index was calculated to normalize the measurements and perform the direct comparison of monthly evolution among stations. Different intensity and evolution of the mean monthly index were observed. The knowledge of the spatial and temporal distribution of this natural radionuclide in the atmosphere is a key parameter for modeling studies of atmospheric processes, which are important phenomena to be taken into account in the case of a nuclear accident.

Keywords: Berilium-7, latitudinal impact in Europe, seasonal and monthly variability, solar cycle

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

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Authors: Emmanuel Jaluchimike Iloputaife, Kelechi Nkechinyere Mbah-Omeje

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This study was designed to determine the antimicrobial activities and minimum inhibitory concentration of three different batches (Fresh, Oven dried and Sundried) of Ass Hay extracted with water, ethanol and methanolagainst selected human pathogenic microorganisms (Escherichia coli, Klebsiella Pneumonia, Staphylococcus aureus, Aspergillus niger and Candidaalbicans). All extracts were reconstituted with peptone water and tested for antimicrobial activity. The antimicrobial activity, the Minimum Inhibitory Concentration and Minimum Bactericidal/Fungicidal concentrations were determined by agar well diffusion methodagainst test organismsin which aseptic conditions were observed. The antimicrobial activities of the different batches of Ass Hay on the test organisms varied considerably. The highest inhibition zone diameter at 200 mg/ml for the different batches of Ass Hay was recorded by sundried methanol extract against Escherichia coli at 36.4 ± 0.2 mm while fresh methanol extract inhibited Klebsiela pneumonia with the least inhibition zone diameter at 20.1 ± 0.1mm. At 100 mg/ml the highest inhibition zone diameter was recorded by oven dried water extract against Escherichia coli at 30.3 ± 0.3 mm while sun dried water extract inhibited Staphylococcus aureus with the least inhibition zone diameter at 15.1 ± 0.1 mm. At 50mg/ml, the highest inhibition zone diameter was recorded by fresh water extract against Escherichia coli at 25.9 ± 0.1 mm while oven dried water extract inhibited Klebsiela pneumonia with least inhibition zone diameter at 12.1 ± 0.2 mm. At 25mg/ml, the highest inhibition zone diameter was recorded by fresh water extract against Escherichia coli at 18.3 ± 0.2 mm while sun dried ethanol extract inhibited Escherichia coli with least inhibition zone diameter at 10.1 ± 0.1 mm. The MIC and MBC result of ethanol extract of fresh Ass Hay showed a uniform value of 6.25 mg/ml and 12.5 mg/ml respectively for all test bacterial isolates. The Minimum Inhibitory concentration and Minimum bactericidal concentration results of Oven dried ethanol Ass Hay extract showed a uniform value of 3.125 mg/ml and 6.25 mg/ml respectively for all test bacterial isolates and Minimum fungicidal concentration value of 12.5 mg/ml for Aspergillus niger. Statistical analysis showed there is significant difference in mean zone inhibition diameter of the products at p < 0.05, p = 0.019. This study has shown there is antimicrobial potential in Ass Hay and at such there is need to further exploit Donkey Ass Hay in order to maximize the potential.

Keywords: microorganisms, Ass Hay, antimicrobial activity, extracts

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Authors: Saniya Ablatt, Matthew Jacobsson, Jamie Whisler, Austin Forbes

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Postoperative pancreatic fistula (POPF) is a complication that occurs in up to 41% of patients after pancreaticoduodenectomy. Although certain characteristics such as individual patient anatomy are known risk factors for POPF, the effect of barbed suture techniques remains underexplored. This study examines whether the use of Stratafix barbed suture versus PDS impacts the risk of developing POPF. After obtaining IRB exemption, a retrospective chart review was initiated involving patients who underwent pancreaticoduodenectomy for the treatment of malignant or premalignant lesions of the pancreas at our institution between April 1st 2020 and April 30th 2022. Patients were stratified into 2 groups respective to the technique used to suture the pancreatico-jejunal anastomosis: Group 1 was composed to patients in which 4.0 Stratafix® suture was used n=41. Group 1 was composed to patients in which 4.0 PDS suture was used n=42. Data regarding patient age, sex, BMI, presence or absence of biochemical leak, presence or absence of grade B & C postoperative pancreatic fistulas, rate and type of in hospital complication, rate of reoperation, 30 day readmission rate, 90 day mortality, and total mortality were compared between groups. 83 patients were included in our study with 42 receiving Stratafix and 41 receiving PDS (50.6% vs 49.4%). Stratafix patients had less biochemical leaks (0.0% vs 4.8%, p=0.19) and higher rates of POPF but this was not statistically significant (7.2% vs 2.4%, p=0.26). Additionally, there was no difference between the use of stratafix versus PDS on the risk of clinically relevant grade B or C POPF (p=0.26, OR=3.25 [CI= 0.74-16.43]). Of the independent variables including age, race, sex, BMI, and ASA class, BMI greater than 25 increased the risk of clinically relevant POPF by 7.7 times compared to patients with BMI less than 25 (p=0.03, OR=7.79 [1.04-88.51]). Despite no significant difference in primary outcomes, the Stratafix group had lower rates of secondary outcomes including 90-day mortality; bleeding, cardiac, and infectious complications; reoperation; and 30-day readmission. On statistical analysis, Stratafix decreased the risk of 30-day readmission (p=0.04, OR=0.21, CI=0.04-0.97) and had a marginally significant effect on the risk of reoperation (p=0.08, OR=0.24, CI=0.04-1.26). There was no difference between the use of Stratafix versus PDS on the risk of POPF (p=0.26). However, Stratafix decreased the risk of 30-day readmission (p=0.04) and BMI greater than 25 increased the risk of clinically relevant POPF (p=0.03).

Keywords: pancreas, hepatobiliary surgery, hepatobiliary, pancreatic leak, biochemical leak, fistula, pancreatic fistula

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Authors: G. Krishnamoorthy, S. Anandhakumar

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The effect of D-Lysine (D-Lys) on collagen with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide(EDC)/N-hydroxysuccinimide(NHS) initiated cross linking using experimental and modelling tools are evaluated. The results of the Coll-D-Lys-EDC/NHS scaffold also indicate an increase in the tensile strength (TS), percentage of elongation (% E), denaturation temperature (Td), and decrease the decomposition rate compared to L-Lys-EDC/NHS. Scanning electron microscopic (SEM) and atomic force microscopic (AFM) analyses revealed a well ordered with properly oriented and well-aligned structure of scaffold. The D-Lys stabilizes the scaffold against degradation by collagenase than L-Lys. The cell assay showed more than 98% fibroblast viability (NIH3T3) and improved cell adhesions, protein adsorption after 72h of culture when compared with native scaffold. Cell attachment after 74h was robust, with cytoskeletal analysis showing that the attached cells were aligned along the fibers assuming a spindle-shape appearance, despite, gene expression analyses revealed no apparent alterations in mRNA levels, although cell proliferation was not adversely affected. D-Lysine (D-Lys) plays a pivotal role in the self-assembly and conformation of collagen fibrils. The D-Lys assisted EDC/NHS initiated cross-linking induces the formation of an carboxamide by the activation of the side chain -COOH group, followed by aminolysis of the O-iso acylurea intermediates by the -NH2 groups are directly joined via an isopeptides bond. This leads to the formation of intra- and inter-helical cross links. Modeling studies indicated that D-Lys bind with collagen-like peptide (CLP) through multiple H-bonding and hydrophobic interactions. Orientational changes in collagenase on CLP-D-Lys are observed which may decrease its accessibility to degradation and stabilize CLP against the action of the former. D-Lys has lowest binding energy and improved fibrillar-assembly and staggered alignment without the undesired structural stiffness and aggregations. The proteolytic machinery is not well equipped to deal with Coll-D-Lys than Coll-L-Lys scaffold. The information derived from the present study could help in designing collagenolytically stable heterochiral collagen based scaffold for biomedical applications.

Keywords: collagen, collagenase, collagen like peptide, D-lysine, heterochiral collagen scaffold

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Sukruthai Sapniwat, Falan Srisuriyachai

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Polymer Flooding is one of the most well-known methods in Enhanced Oil Recovery (EOR) technology which can be implemented after either primary or secondary recovery, resulting in favorable conditions for the displacement mechanism in order to lower the residual oil in the reservoir. Polymer substances can lower the mobility ratio of the whole process by increasing the viscosity of injected water. Therefore, polymer flooding can increase volumetric sweep efficiency, which leads to a better recovery factor. Moreover, polymer adsorption onto rock surface can help decrease reservoir permeability contrast with high heterogeneity. Due to the reduction of the absolute permeability, effective permeability to water, representing flow ability of the injected fluid, is also reduced. Once polymer is adsorbed onto rock surface, polymer molecule can be desorbed when different fluids are injected. This study is performed to evaluate the effects of the adsorption and desorption process of polymer solutions to yield benefits on the oil recovery mechanism. A reservoir model is constructed by reservoir simulation program called STAR® commercialized by the Computer Modeling Group (CMG). Various polymer concentrations, starting times of polymer flooding process and polymer injection rates were evaluated with selected values of polymer desorption degrees including 0, 25, 50, 75 and 100%. The higher the value, the more adsorbed polymer molecules to return back to flowing fluid. According to the results, polymer desorption lowers polymer consumption, especially at low concentrations. Furthermore, starting time of polymer flooding and injection rate affect the oil production. The results show that waterflooding followed by earlier polymer flooding can increase the oil recovery factor while the higher injection rate also enhances the recovery. Polymer concentration is related to polymer consumption due to the two main benefits of polymer flooding control described above. Therefore, polymer slug size should be optimized based on polymer concentration. Polymer desorption causes polymer re-employment that is previously adsorbed onto rock surface, resulting in an increase of sweep efficiency in the further period of polymer flooding process. Even though waterflooding supports polymer injectivity, water cut at the producer can prematurely terminate the oil production. The injection rate decreases polymer adsorption due to decreased retention time of polymer flooding process.

Keywords: enhanced oil recovery technology, polymer adsorption and desorption, polymer flooding, reservoir simulation

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Authors: Piyawan Srisuruk, Janya Boonmeeprasert, Romwarin Gamlunglert, Benjamaporn Choikhruea, Ornjira Jaraepram, Jarin Boonsuchat, Sakdadech Singkibud, Kusalaporn Chaiudomsom, Chanatiporn Chonprai, Pornchanaka Tana, Suchat Paholpak

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Objective: To develop an Intensive and comprehensive program (ICP) for the Inclusive Class Teacher (ICPICT) to teach elementary students (ES) with ASD in order to enhance the students’ academic and social skills (ASS) and to study the effect of the teaching program. Methods: The purposive sample included 15 Khon Kaen inclusive class teachers and their 15 elementary students. All the students were diagnosed by a child and adolescent psychiatrist to have DSM-5 level 1 ASD. The study tools included 1) an ICP to teach teachers about ASD, a teaching method to enhance academic and social skills for ES with ASD, and an assessment tool to assess the teacher’s knowledge before and after the ICP. 2) an ICPICT to teach ES with ASD to enhance their ASS. The project taught 10 sessions, 3 hours each. The ICPICT had its teaching structure. Teaching media included: pictures, storytelling, songs, and plays. The authors taught and demonstrated to the participant teachers how to teach with the ICPICT until the participants could display the correct teaching method. Then the teachers taught ICPICT at school by themselves 3) an assessment tool to assess the students’ ASS before and after the completion of the study. The ICP to teach the teachers, the ICPICT, and the relevant assessment tools were developed by the authors and were adjusted until consensus agreed as appropriate for researching by 3 curriculum of teaching children with ASD experts. The data were analyzed by descriptive and analytic statistics via SPSS version 26. Results: After the briefing, the teachers increased the mean score, though not with statistical significance, of knowledge of ASD and how to teach ES with ASD on ASS (p = 0.13). Teaching ES with ASD with the ICPICT could increase the mean scores of the students’ skills in learning and expressing social emotions, relationships with a friend, transitioning, and skills in academic function 3.33, 2.27, 2.94, and 3.00 scores (full scores were 18, 12, 15 and 12, Paired T-Test p = 0.007, 0.013, 0.028 and 0.003 respectively). Conclusion: The program to teach academic and social skills simultaneously in an intensive and comprehensive structure could enhance both the academic and social skills of elementary students with ASD. Keywords: Elementary students, autism spectrum, academic skill, social skills, intensive program, comprehensive program, integration.

Keywords: academica and social skills, students with autism, intensive and comprehensive, teaching program

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Authors: Asif Arshid, Ying Huang, Denver Tolliver

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Finite Element (FE) method coupled with ever-increasing computational powers has substantially advanced the reliability of deterministic three dimensional structural analyses of a structure with uniform material properties. However, railways trackbed is made up of diverse group of materials including steel, wood, rock and soil, while each material has its own varying levels of heterogeneity and imperfections. It is observed that the application of probabilistic methods for trackbed structural analysis while incorporating the material and geometric variabilities is deeply underworked. The authors developed and validated a 3-dimensional FE based numerical trackbed model and in this study, they investigated the influence of variability in Young modulus and thicknesses of granular layers (Ballast and Subgrade) on the reliability index (-index) of the subgrade layer. The influence of these factors is accounted for by changing their Coefficients of Variance (COV) while keeping their means constant. These variations are formulated using Gaussian Normal distribution. Two failure mechanisms in subgrade namely Progressive Shear Failure and Excessive Plastic Deformation are examined. Preliminary results of risk-based probabilistic analysis for Progressive Shear Failure revealed that the variations in Ballast depth are the most influential factor for vertical stress at the top of subgrade surface. Whereas, in case of Excessive Plastic Deformations in subgrade layer, the variations in its own depth and Young modulus proved to be most important while ballast properties remained almost indifferent. For both these failure moods, it is also observed that the reliability index for subgrade failure increases with the increase in COV of ballast depth and subgrade Young modulus. The findings of this work is of particular significance in studying the combined effect of construction imperfections and variations in ground conditions on the structural performance of railroad trackbed and evaluating the associated risk involved. In addition, it also provides an additional tool to supplement the deterministic analysis procedures and decision making for railroad maintenance.

Keywords: finite element analysis, numerical modeling, probabilistic methods, risk and reliability analysis, subgrade

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Authors: Jui-Teng Liao

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The selected-response (SR) format has been commonly adopted to assess academic reading in both formal and informal testing (i.e., standardized assessment and classroom assessment) because of its strengths in content validity, construct validity, as well as scoring objectivity and efficiency. When developing a second language (L2) reading test, researchers indicate that the longer the test (e.g., more test items) is, the higher reliability and validity the test is likely to produce. However, previous studies have not provided specific guidelines regarding the optimal length of a test or the most suitable number of test items or reading passages. Additionally, reading tests often include different question types (e.g., factual, vocabulary, inferential) that require varying degrees of reading comprehension and cognitive processes. Therefore, it is important to investigate the impact of question types on the number of items in relation to the score reliability of L2 reading tests. Given the popularity of the SR question format and its impact on assessment results on teaching and learning, it is necessary to investigate the degree to which such a question format can reliably measure learners’ L2 reading comprehension. The present study, therefore, adopted the generalizability (G) theory to investigate the score reliability of the SR format in L2 reading tests focusing on how many test items a reading test should include. Specifically, this study aimed to investigate the interaction between question types and the number of items, providing insights into the appropriate item count for different types of questions. G theory is a comprehensive statistical framework used for estimating the score reliability of tests and validating their results. Data were collected from 108 English as a second language student who completed an English reading test comprising factual, vocabulary, and inferential questions in the SR format. The computer program mGENOVA was utilized to analyze the data using multivariate designs (i.e., scenarios). Based on the results of G theory analyses, the findings indicated that the number of test items had a critical impact on the score reliability of an L2 reading test. Furthermore, the findings revealed that different types of reading questions required varying numbers of test items for reliable assessment of learners’ L2 reading proficiency. Further implications for teaching practice and classroom-based assessments are discussed.

Keywords: second language reading assessment, validity and reliability, Generalizability theory, Academic reading, Question format

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Authors: Linjie Zhang, Baifen Ren, Xi Zhang, Genwang Liu

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Ship detection is of great significance for both military and civilian applications. Synthetic aperture radar (SAR) with all-day, all-weather, ultra-long-range characteristics, has been used widely. In view of the low time resolution of low orbit SAR and the needs for high time resolution SAR data, GEO (Geosynchronous orbit) SAR is getting more and more attention. Since GEO SAR has short revisiting period and large coverage area, it is expected to be well utilized in marine ship targets monitoring. However, the height of the orbit increases the time of integration by almost two orders of magnitude. For moving marine vessels, the utility and efficacy of GEO SAR are still not sure. This paper attempts to find the feasibility of GEO SAR by giving a GEO SAR simulator of moving ships. This presented GEO SAR simulator is a kind of geometrical-based radar imaging simulator, which focus on geometrical quality rather than high radiometric. Inputs of this simulator are 3D ship model (.obj format, produced by most 3D design software, such as 3D Max), ship's velocity, and the parameters of satellite orbit and SAR platform. Its outputs are simulated GEO SAR raw signal data and SAR image. This simulating process is accomplished by the following four steps. (1) Reading 3D model, including the ship rotations (pitch, yaw, and roll) and velocity (speed and direction) parameters, extract information of those little primitives (triangles) which is visible from the SAR platform. (2) Computing the radar scattering from the ship with physical optics (PO) method. In this step, the vessel is sliced into many little rectangles primitives along the azimuth. The radiometric calculation of each primitive is carried out separately. Since this simulator only focuses on the complex structure of ships, only single-bounce reflection and double-bounce reflection are considered. (3) Generating the raw data with GEO SAR signal modeling. Since the normal ‘stop and go’ model is not available for GEO SAR, the range model should be reconsidered. (4) At last, generating GEO SAR image with improved Range Doppler method. Numerical simulation of fishing boat and cargo ship will be given. GEO SAR images of different posture, velocity, satellite orbit, and SAR platform will be simulated. By analyzing these simulated results, the effectiveness of GEO SAR for the detection of marine moving vessels is evaluated.

Keywords: GEO SAR, radar, simulation, ship

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Authors: Zuzanna Miodonska, Michal Krecichwost, Pawel Badura

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Sigmatism (lisping) is a speech disorder in which sibilant consonants are mispronounced. The diagnosis of this phenomenon is usually based on the auditory assessment. However, the progress in speech analysis techniques creates a possibility of developing computer-aided sigmatism diagnosis tools. The aim of the study is to statistically verify whether specific acoustic features of sibilant sounds may be related to pronunciation correctness. Such knowledge can be of great importance while implementing classifiers and designing novel tools for automatic sibilants pronunciation evaluation. The study covers analysis of various speech signal measures, including features proposed in the literature for the description of normative sibilants realization. Amplitudes and frequencies of three fricative formants (FF) are extracted based on local spectral maxima of the friction noise. Skewness, kurtosis, four normalized spectral moments (SM) and 13 mel-frequency cepstral coefficients (MFCC) with their 1st and 2nd derivatives (13 Delta and 13 Delta-Delta MFCC) are included in the analysis as well. The resulting feature vector contains 51 measures. The experiments are performed on the speech corpus containing words with selected sibilant sounds (/ʃ, ʒ/) pronounced by 60 preschool children with proper pronunciation or with natural pathologies. In total, 224 /ʃ/ segments and 191 /ʒ/ segments are employed in the study. The Mann-Whitney U test is employed for the analysis of stigmatism and normative pronunciation. Statistically, significant differences are obtained in most of the proposed features in children divided into these two groups at p < 0.05. All spectral moments and fricative formants appear to be distinctive between pathology and proper pronunciation. These metrics describe the friction noise characteristic for sibilants, which makes them particularly promising for the use in sibilants evaluation tools. Correspondences found between phoneme feature values and an expert evaluation of the pronunciation correctness encourage to involve speech analysis tools in diagnosis and therapy of sigmatism. Proposed feature extraction methods could be used in a computer-assisted stigmatism diagnosis or therapy systems.

Keywords: computer-aided pronunciation evaluation, sigmatism diagnosis, speech signal analysis, statistical verification

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Authors: Iman Farasat, Howard M. Salis

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The CRISPR/Cas9 system has revolutionized genome engineering by enabling site-directed and high-throughput genome editing, genome insertion, and gene knockdowns in several species, including bacteria, yeast, flies, worms, and human cell lines. This technology has the potential to enable human gene therapy to treat genetic diseases and cancer at the molecular level; however, the current CRISPR/Cas9 system suffers from seemingly sporadic off-target genome mutagenesis that prevents its use in gene therapy. A comprehensive mechanistic model that explains how the CRISPR/Cas9 functions would enable the rational design of the guide-RNAs responsible for target site selection while minimizing unexpected genome mutagenesis. Here, we present the first quantitative model of the CRISPR/Cas9 genome mutagenesis system that predicts how guide-RNA sequences (crRNAs) control target site selection and cleavage activity. We used statistical thermodynamics and law of mass action to develop a five-step biophysical model of cas9 cleavage, and examined it in vivo and in vitro. To predict a crRNA's binding specificities and cleavage rates, we then compiled a nearest neighbor (NN) energy model that accounts for all possible base pairings and mismatches between the crRNA and the possible genomic DNA sites. These calculations correctly predicted crRNA specificity across 5518 sites. Our analysis reveals that cas9 activity and specificity are anti-correlated, and, the trade-off between them is the determining factor in performing an RNA-mediated cleavage with minimal off-targets. To find an optimal solution, we first created a scheme of safe-design criteria for Cas9 target selection by systematic analysis of available high throughput measurements. We then used our biophysical model to determine the optimal Cas9 expression levels and timing that maximizes on-target cleavage and minimizes off-target activity. We successfully applied this approach in bacterial and mammalian cell lines to reduce off-target activity to near background mutagenesis level while maintaining high on-target cleavage rate.

Keywords: biophysical model, CRISPR, Cas9, genome editing

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Authors: Emilienne Lardy, Eric Ballot, Mariam Lafkihi

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Efficient urban logistics requires a comprehensive understanding of traffic dynamics, particularly as it pertains to kinetic parameters influencing energy consumption and trip duration estimations. While real-time traffic information is increasingly accessible, current high-precision forecasting services embedded in route planning often function as opaque 'black boxes' for users. These services, typically relying on AI-processed counting data, fall short in accommodating open design parameters essential for management studies, notably within Supply Chain Management. This work revisits the modelling of traffic conditions in the context of city logistics, emphasizing its significance from the user’s point of view, with two focuses. Firstly, the focus is not on the vehicle flow but on the vehicles themselves and the impact of the traffic conditions on their driving behaviour. This means opening the range of studied indicators beyond vehicle speed, to describe extensively the kinetic and dynamic aspects of the driving behaviour. To achieve this, we leverage the Art. Kinema parameters are designed to characterize driving cycles. Secondly, this study examines how the driving context (i.e., exogenous factors to the traffic flow) determines the mentioned driving behaviour. Specifically, we explore how accurately the kinetic behaviour of a vehicle can be predicted based on a limited set of exogenous factors, such as time, day, road type, orientation, slope, and weather conditions. To answer this question, statistical analysis was conducted on real-world driving data, which includes high-frequency measurements of vehicle speed. A Factor Analysis and a Generalized Linear Model have been established to link kinetic parameters with independent categorical contextual variables. The results include an assessment of the adjustment quality and the robustness of the models, as well as an overview of the model’s outputs.

Keywords: factor analysis, generalised linear model, real world driving data, traffic congestion, urban logistics, vehicle kinematics

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Authors: Kambiz Rasoulkhani, Jeanne Cole, Sybil Sharvelle, Ali Mostafavi

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Dual water distribution systems have been proposed as solutions to enhance the sustainability and resilience of urban water systems by improving performance and decreasing energy consumption. The objective of this study was to evaluate the long-term resilience and robustness of dual water distribution systems versus singular water distribution systems under various stressors such as demand fluctuation, aging infrastructure, and funding constraints. To this end, the long-term dynamics of these infrastructure systems was captured using a simulation model that integrates institutional agency decision-making processes with physical infrastructure degradation to evaluate the long-term transformation of water infrastructure. A set of model parameters that varies for dual and singular distribution infrastructure based on the system attributes, such as pipes length and material, energy intensity, water demand, water price, average pressure and flow rate, as well as operational expenditures, were considered and input in the simulation model. Accordingly, the model was used to simulate various scenarios of demand changes, funding levels, water price growth, and renewal strategies. The long-term resilience and robustness of each distribution infrastructure were evaluated based on various performance measures including network average condition, break frequency, network leakage, and energy use. An ecologically-based resilience approach was used to examine regime shifts and tipping points in the long-term performance of the systems under different stressors. Also, Classification and Regression Tree analysis was adopted to assess the robustness of each system under various scenarios. Using data from the City of Fort Collins, the long-term resilience and robustness of the dual and singular water distribution systems were evaluated over a 100-year analysis horizon for various scenarios. The results of the analysis enabled: (i) comparison between dual and singular water distribution systems in terms of long-term performance, resilience, and robustness; (ii) identification of renewal strategies and decision factors that enhance the long-term resiliency and robustness of dual and singular water distribution systems under different stressors.

Keywords: complex systems, dual water distribution systems, long-term resilience performance, multi-agent modeling, sustainable and resilient water systems

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Authors: Eva L. Suarez, Daniel E. Meeroff, Yan Yong

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Current flooding risk modeling focuses on resilience, defined as the probability of recovery from a severe flooding event. However, the long-term damage to property and well-being by nuisance flooding and its long-term effects on communities are not typically included in risk assessments. An approach was developed to address the probability of recovering from a severe flooding event combined with the probability of community performance during a nuisance event. A consolidated model, namely the conflation flooding recovery (&FR) model, evaluates risk-coping mitigation strategies for communities based on the recovery time from catastrophic events, such as hurricanes or extreme surges, and from everyday nuisance flooding events. The &FR model assesses the variation contribution of each independent input and generates a weighted output that favors the distribution with minimum variation. This approach is especially useful if the input distributions have dissimilar variances. The &FR is defined as a single distribution resulting from the product of the individual probability density functions. The resulting conflated distribution resides between the parent distributions, and it infers the recovery time required by a community to return to basic functions, such as power, utilities, transportation, and civil order, after a flooding event. The &FR model is more accurate than averaging individual observations before calculating the mean and variance or averaging the probabilities evaluated at the input values, which assigns the same weighted variation to each input distribution. The main disadvantage of these traditional methods is that the resulting measure of central tendency is exactly equal to the average of the input distribution’s means without the additional information provided by each individual distribution variance. When dealing with exponential distributions, such as resilience from severe flooding events and from nuisance flooding events, conflation results are equivalent to the weighted least squares method or best linear unbiased estimation. The combination of severe flooding risk with nuisance flooding improves flood risk management for highly populated coastal communities, such as in South Florida, USA, and provides a method to estimate community flood recovery time more accurately from two different sources, severe flooding events and nuisance flooding events.

Keywords: community resilience, conflation, flood risk, nuisance flooding

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Authors: Antoinette Maarawi, Zoe Anxionnaz-Minvielle, Pierre Coste, Nathalie Di Miceli Raimondi, Michel Cabassud

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Milli-structured heat exchanger/reactors have been recently widely used, especially in the chemical industry, due to their enhanced performances in heat and mass transfer compared to conventional apparatuses. In our work, the ‘DeanHex’ heat exchanger/reactor with a 2D-meandering channel is investigated both experimentally and numerically. The square cross-sectioned channel has a hydraulic diameter of 2mm. The aim of our study is to model local physico-chemical phenomena (heat and mass transfer, axial dispersion, etc.) for a liquid-liquid two-phase flow in our lab-scale meandering channel, which represents the central part of the heat exchanger/reactor design. The numerical approach of the reactor is based on a 1D model for the flow channel encapsulated in a 3D model for the surrounding solid, using COMSOL Multiphysics V5.5. The use of the 1D approach to model the milli-channel reduces significantly the calculation time compared to 3D approaches, which are generally focused on local effects. Our 1D/3D approach intends to bridge the gap between the simulation at a small scale and the simulation at the reactor scale at a reasonable CPU cost. The heat transfer process between the 1D milli-channel and its 3D surrounding is modeled. The feasibility of this 1D/3D coupling was verified by comparing simulation results to experimental ones originated from two previous works. Temperature profiles along the channel axis obtained by simulation fit the experimental profiles for both cases. The next step is to integrate the liquid-liquid mass transfer model and to validate it with our experimental results. The hydrodynamics of the liquid-liquid two-phase system is modeled using the ‘mixture model approach’. The mass transfer behavior is represented by an overall volumetric mass transfer coefficient ‘kLa’ correlation obtained from our experimental results in the millimetric size meandering channel. The present work is a first step towards the scale-up of our ‘DeanHex’ expecting future industrialization of such equipment. Therefore, a generalized scaled-up model of the reactor comprising all the transfer processes will be built in order to predict the performance of the reactor in terms of conversion rate and energy efficiency at an industrial scale.

Keywords: liquid-liquid mass transfer, milli-structured reactor, 1D/3D model, process intensification

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Authors: Vasupradaa Manivannan, Santosh Maruthy

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Background/Aims: Preliminary evidence suggests that code-switching and code-mixing may act as one of the voluntary coping behavior to avoid the stuttering characteristics in children and adults; however, less is known about the types and patterns of code-mixing (CM) and code-switching (CS). Further, it is not known how it is different between children to adults who stutter. This study aimed to identify and compare the CM and CS patterns between Kannada-English bilingual children and adults who stutter. Method: A standard group comparison was made between five children who stutter (CWS) in the age range of 9-13 years and five adults who stutter (AWS) in the age range of 20-25 years. The participants who are proficient in Kannada (first language- L1) and English (second language- L2) were considered for the study. There were two tasks given to both the groups, a) General conversation (GC) with 10 random questions, b) Narration task (NAR) (Story / General Topic, for example., A Memorable Life Event) in three different conditions {Mono Kannada (MK), Mono English (ME), and Bilingual (BIL) Condition}. The children and adults were assessed online (via Zoom session) with a high-quality internet connection. The audio and video samples of the full assessment session were auto-recorded and manually transcribed. The recorded samples were analyzed for the percentage of dysfluencies using SSI-4 and CM, and CS exhibited in each participant using Matrix Language Frame (MLF) model parameters. The obtained data were analyzed using the Statistical Package for the Social Sciences (SPSS) software package (Version 20.0). Results: The mean, median, and standard deviation values were obtained for the percentage of dysfluencies (%SS) and frequency of CM and CS in Kannada-English bilingual children and adults who stutter for various parameters obtained through the MLF model. The inferential results indicated that %SS significantly varied between population (AWS vs CWS), languages (L1 vs L2), and tasks (GC vs NAR) but not across free (BIL) and bound (MK, ME) conditions. It was also found that the frequency of CM and CS patterns varies between CWS and AWS. The AWS had a lesser %SS but greater use of CS patterns than CWS, which is due to their excessive coping skills. The language mixing patterns were more observed in L1 than L2, and it was significant in most of the MLF parameters. However, there was a significantly higher (P<0.05) %SS in L2 than L1. The CS and CS patterns were more in conditions 1 and 3 than 2, which may be due to the higher proficiency of L2 than L1. Conclusion: The findings highlight the importance of assessing the CM and CS behaviors, their patterns, and the frequency of CM and CS between CWS and AWS on MLF parameters in two different tasks across three conditions. The results help us to understand CM and CS strategies in bilingual persons who stutter.

Keywords: bilinguals, code mixing, code switching, stuttering

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Authors: Sunday B. Alabi, Edikan P. Felix, Aniediong M. Umo

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Crystal size distribution is of great importance in the sugar factories. It determines the market value of granulated sugar and also influences the cost of production of sugar crystals. Typically, sugar is produced using fed-batch vacuum evaporative crystallizer. The crystallization quality is examined by crystal size distribution at the end of the process which is quantified by two parameters: the average crystal size of the distribution in the mean aperture (MA) and the width of the distribution of the coefficient of variation (CV). Lack of real-time measurement of the sugar crystal size hinders its feedback control and eventual optimisation of the crystallization process. An attractive alternative is to use a soft sensor (model-based method) for online estimation of the sugar crystal size. Unfortunately, the available models for sugar crystallization process are not suitable as they do not contain variables that can be measured easily online. The main contribution of this paper is the development of a regression model for estimating the sugar crystal size as a function of input variables which are easy to measure online. This has the potential to provide real-time estimates of crystal size for its effective feedback control. Using 7 input variables namely: initial crystal size (Lo), temperature (T), vacuum pressure (P), feed flowrate (Ff), steam flowrate (Fs), initial super-saturation (S0) and crystallization time (t), preliminary studies were carried out using Minitab 14 statistical software. Based on the existing sugar crystallizer models, and the typical ranges of these 7 input variables, 128 datasets were obtained from a 2-level factorial experimental design. These datasets were used to obtain a simple but online-implementable 6-input crystal size model. It seems the initial crystal size (Lₒ) does not play a significant role. The goodness of the resulting regression model was evaluated. The coefficient of determination, R² was obtained as 0.994, and the maximum absolute relative error (MARE) was obtained as 4.6%. The high R² (~1.0) and the reasonably low MARE values are an indication that the model is able to predict sugar crystal size accurately as a function of the 6 easy-to-measure online variables. Thus, the model can be used as a soft sensor to provide real-time estimates of sugar crystal size during sugar crystallization process in a fed-batch vacuum evaporative crystallizer.

Keywords: crystal size, regression model, soft sensor, sugar, vacuum evaporative crystallizer

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Authors: Mariya Dayana, Budhmal Jain

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Kannur International Airport Ltd. (KIAL) is a new Greenfield airport project with airside development on an undulating terrain with an average height of 90m above Mean Sea Level (MSL) and a maximum height of 142m. To accommodate the desired Runway length and Runway End Safety Area (RESA) at both the ends along the proposed alignment, it resulted in 45.5 million cubic meters in cutting and filling. The insufficient availability of land for the construction of free slope embankment at RESA 07 end resulted in the design and construction of Reinforced Soil Slope (RSS) with a maximum slope of 65 degrees. An embankment fill of average 70m height with steep slopes located in high rainfall area is a unique feature of this project. The design and construction was challenging being asymmetrical with curves and bends. The fill was reinforced with high strength Uniaxial geogrids laid perpendicular to the slope. Weld mesh wrapped with coir mat acted as the facia units to protect it against surface failure. Face anchorage were also provided by wrapping the geogrids along the facia units where the slope angle was steeper than 45 degrees. Considering high rainfall received on this table top airport site, extensive drainage system was designed for the high embankment fill. Gabion wall up to 10m height were also designed and constructed along the boundary to accommodate the toe of the RSS fill beside the jeepable track at the base level. The design of RSS fill was done using ReSSA software and verified in PLAXIS 2D modeling. Both slip surface failure and wedge failure cases were considered in static and seismic analysis for local and global failure cases. The site won excavated laterite soil was used as the fill material for the construction. Extensive field and laboratory tests were conducted during the construction of RSS system for quality assurance. This paper represents a case study detailing the design and construction of a very high embankment using geosynthetics for the provision of Runway length and RESA area.

Keywords: airport, embankment, gabion, high strength uniaxial geogrid, kial, laterite soil, plaxis 2d

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Authors: Maressa Aires de Proenca

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The inclusion of a gender perspective in peace operations is based on a series of conventions, treaties, and resolutions designed to protect and include women addressing gender mainstreaming. The UN Security Council recognizes that women's participation and gender equality within peace operations are indispensable for achieving sustainable development and peace. However, the participation of women in the field of peace and security is still embryonic. There are gaps when we think about female participation in conflict resolution and peace promotion spaces, and it does not seem clear how women are present in these spaces. This absence may correspond to silence about representation and the guarantee of the female perspective within the context of peace promotion. Thus, the present research aimed to describe the panorama of the participation of women who are currently active in the 14 active UN peace operations, which are: 1) MINUJUSTH, Haiti, 2) MINURSO, Western Sahara, 3) MINUSCA, Central African Republic, 4) MINUSMA, Mali, 5) MONUSCO, the Democratic Republic of the Congo, 6) UNAMID, Darfur, 7) UNDOF, Golan, 8) UNFICYP, Cyprus, 9) UNIFIL, Lebanon, 10) UNISFA, Abyei, 11) UNMIK, Kosovo, 12) UNMISS, South Sudan, 13) UNMOGIP, India, and Pakistan, and 14) UNTSO, Middle East. A database was constructed that reported: (1) position held by the woman in the peace operation, (2) her profession, (3) educational level, (4) marital status, (5) religion, (6) nationality, (8) number of years working with peace operations, (9) whether the operation in which it operates has provided training on gender issues. For the construction of this database, official reports and statistics accessed through the UN Peacekeeping Resource Hub were used; The United Nations Statistical Commission, Peacekeeping Master Open Datasets, The Armed Conflict Database (ACD), The International Institute for Strategic Studies (IISS) database; Armed Conflict Location & Event Data Project (ACLED) database; from the Evidence and Data for Gender Equality (EDGE) database. In addition to access to databases, peacekeeping operations will be contacted directly, and data requested individually. The database showed that the presence of women in these peace operations is still incipient, but growing. There are few women in command positions, and most of them occupy administrative or human-care positions.

Keywords: women, peace and security, peacekeeping operations, peace studies

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Authors: Marina Petrova

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Language policy issues related to the preservation and development of the native languages of the Russian peoples and the state languages of the national republics are increasingly becoming the focus of recent attention of educators and parents, public and national figures. Is it legal to teach the national language or the mother tongue as the state language? Due to that dispute language phobia moods easily evolve into xenophobia among the population. However, a civilized, intelligent multicultural personality can only be formed if the country develops bilingualism and multilingualism, and languages as a political tool help to find ‘keys’ to sufficiently closed national communities both within a poly-ethnic state and in internal relations of multilingual countries. The purpose of this study is to design and theoretically substantiate an efficient model of language education in the innovatively developing Republic of Sakha. 800 participants from different educational institutions of Yakutia worked at developing a multilingual model of education. This investigation is of considerable practical importance because researchers could build a methodical system designed to create conditions for the formation of a cultural language personality and the development of the multilingual communicative competence of Yakut youth, necessary for communication in native, Russian and foreign languages. The selected methodology of humane-personal and competence approaches is reliable and valid. Researchers used a variety of sources of information, including access to related scientific fields (philosophy of education, sociology, humane and social pedagogy, psychology, effective psychotherapy, methods of teaching Russian, psycholinguistics, socio-cultural education, ethnoculturology, ethnopsychology). Of special note is the application of theoretical and empirical research methods, a combination of academic analysis of the problem and experienced training, positive results of experimental work, representative series, correct processing and statistical reliability of the obtained data. It ensures the validity of the investigation’s findings as well as their broad introduction into practice of life-long language education.

Keywords: intercultural communication, language policy, multilingual and multicultural education, the Sakha Republic of Yakutia

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Authors: Svenja Pieritz, Jakab Pilaszanovich

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Collaborative Problem Solving (CPS) is recognized as being one of the most important skills of the 21st century with having a potential impact on education, job selection, and collaborative systems design. Therefore, CPS has been adopted in several standardized tests, including the Programme for International Student Assessment (PISA) in 2015. A significant challenge of evaluating CPS is the underlying interplay of cognitive and social skills, which requires a more holistic assessment. However, the majority of the existing tests are using a questionnaire-based assessment, which oversimplifies this interplay and undermines ecological validity. Two major difficulties were identified: Firstly, the creation of a controllable, real-time environment allowing natural behaviors and communication between at least two people. Secondly, the development of an appropriate method to collect and synthesize both cognitive and social metrics of collaboration. This paper proposes a more holistic and automated approach to the assessment of CPS. To address these two difficulties, a multiplayer problem-solving game called Sea of Light was developed: An environment allowing students to deploy a variety of measurable collaborative strategies. This controlled environment enables researchers to monitor behavior through the analysis of game actions and chat. The according solution for the statistical model is a combined approach of Natural Language Processing (NLP) and Bayesian network analysis. Social exchanges via the in-game chat are analyzed through NLP and fed into the Bayesian network along with other game actions. This Bayesian network synthesizes evidence to track and update different subdimensions of CPS. Major findings focus on the correlations between the evidences collected through in- game actions, the participants’ chat features and the CPS self- evaluation metrics. These results give an indication of which game mechanics can best describe CPS evaluation. Overall, Sea of Light gives test administrators control over different problem-solving scenarios and difficulties while keeping the student engaged. It enables a more complete assessment based on complex, socio-cognitive information on actions and communication. This tool permits further investigations of the effects of group constellations and personality in collaborative problem-solving.

Keywords: bayesian network, collaborative problem solving, game-based assessment, natural language processing

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Authors: Faizah Asiri, Mohammed Fathy, Saeed Alghamdi, Nahlah Ayoub, Faisal Asiri

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Background: - The strategies for this study were based on the toxic effect of long-term inhalation of Benzene on hormones and liver enzymes and various parameters related to it. The following databases were searched: benzene, hepatotoxic, benzene metabolism, hormones, testosterone, hemotoxic, and prolonged exposure. A systematic strategy is designed to search the literature that links benzene with the multiplicity and different types of intoxication or the medical abbreviations of diseases relevant to benzene exposure. Evidence suggests that getting rid of inhaled gasoline is by exhalation. Absorbed benzene is metabolized by giving phenolic acid as well as meconic acid, followed by urinary excretion of conjugate sulfates and glucuronides. Materials and Methods :- This work was conducted in the Al-Khadra laboratory in Taif 2020/2021 and aimed to measure some of the possible endocrinal and liver toxicities associated with benzene's long-term exposure in Saudi Arabia at the station workers who are considered the most exposed category to gasoline. One hundred ten station workers were included in this study. They were divided into four patient groups according to the chronic exposure rate to benzene, one control group, and three other groups of exposures. As follows: patient Group 1 (controlled group), patient Group 2 (exposed less than 1y), patient Group 3 (exposed 1-5 y), patient Group 4 (more than 5). Each group is compared with blood sample parameters (ALT, FSH and Testosterone, TSH). Blood samples were drawn from the participants, and statistical tests were performed. Significant change (p≤0.05) was examined compared to the control group. Workers' exposure to benzene led to a significant change in hematological, hormonal, and hepatic factors compared to the control group. Results:- The results obtained a relationship between long-term exposure to benzene and a decrease in the level of testosterone and FSH hormones, including that it poses a toxic risk in the long term (p≤0.05) when compared to the control. We obtained results confirming that there is no significant coloration between years of exposure and TSH level (p≤0.05) when compared to the control. Conclusion:- We conclude that some hormones and liver enzymes are affected by chronic doses of benzene through inhalation after our study was on the group most exposed to benzene, which is gas station workers.

Keywords: toxicities, benzene, hormones, station workers

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Authors: Ahmad Haidar

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Modern economic systems are characterized by growing complexity, and addressing their challenges requires innovative approaches. This study examines the implications of artificial intelligence (AI) on unemployment in Europe from a macroeconomic perspective, employing data modeling techniques to understand the relationship between AI integration and labor market dynamics. To understand the AI-unemployment nexus comprehensively, this research considers factors such as sector-specific AI adoption, skill requirements, workforce demographics, and geographical disparities. The study utilizes a panel data model, incorporating data from European countries over the last two decades, to explore the potential short-term and long-term effects of AI implementation on unemployment rates. In addition to investigating the direct impact of AI on unemployment, the study also delves into the potential indirect effects and spillover consequences. It considers how AI-driven productivity improvements and cost reductions might influence economic growth and, in turn, labor market outcomes. Furthermore, it assesses the potential for AI-induced changes in industrial structures to affect job displacement and creation. The research also highlights the importance of policy responses in mitigating potential negative consequences of AI adoption on unemployment. It emphasizes the need for targeted interventions such as skill development programs, labor market regulations, and social safety nets to enable a smooth transition for workers affected by AI-related job displacement. Additionally, the study explores the potential role of AI in informing and transforming policy-making to ensure more effective and agile responses to labor market challenges. In conclusion, this study provides a comprehensive analysis of the macroeconomic implications of AI on unemployment in Europe, highlighting the importance of understanding the nuanced relationships between AI adoption, economic growth, and labor market outcomes. By shedding light on these relationships, the study contributes valuable insights for policymakers, educators, and researchers, enabling them to make informed decisions in navigating the complex landscape of AI-driven economic transformation.

Keywords: artificial intelligence, unemployment, macroeconomic analysis, european labor market

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Authors: Sherelle Lou Sumera Icutan, Sheila P. Cayabyab, Mary Jane Laruan, Paulo V. Cenas, Agustina R. Tactay

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This study critically analyzed the recent theses and dissertations of the Basic Education Curriculum (BEC) teachers who finished their graduate programs in selected higher educational institutions in Region I and CAR to be able to come up with a unified result from the varied results of the analyzed research works. All theses and dissertations completed by the educators/teachers/school personnel in the secondary and elementary public and private schools in Region 1 and CAR from AY 2003–2004 to AY 2007–2008 were classified first–as to work or non-work related; second–as to the different aspects of the curriculum: implementation, content, instructional materials, assessment instruments, learning, teaching, and others; third–as to being eligible for meta-analysis or not. Only studies found eligible for meta-analysis were subjected to the procedure. Aside from documentary analysis, the statistical treatments used in meta-analysis include the standardized effect size, Pearson’s correlation (r), the chi-square test of homogeneity and the inverse of the Fisher transformation. This study found out that the BEC-teachers usually probe on work-related researchers with topics that are focused on the learning performances of the students and on factors related to teaching. The development of instructional materials and assessment of implemented programs are also equally explored. However, there are only few researches on content and assessment instrument. Research findings on the areas of learning and teaching are the only aspects that are meta-analyzable. The research findings across studies in Region I and CAR of BEC teachers that focused on similar variables correlated to teaching do not vary significantly. On the contrary, research findings across studies in Region I and CAR that focused on variables correlated to learning performance significantly vary. Within each region, variations on the findings of research works related to learning performance that considered similar variables still exist. The combined finding on the effect size or relationship of the variables that are correlated to learning performance are low which means that effect is small but definite while the combined findings on the relationship of the variables correlated to teaching are slight or almost negligible.

Keywords: meta-analysis, BEC teachers, work-related research,

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Authors: Ella Tyuryumina, Alexey Neznanov

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This paper is devoted to mathematical modelling of the progression and stages of breast cancer. We propose Consolidated mathematical growth model of primary tumor and secondary distant metastases growth in patients with lymph nodes metastases (CoM-III) as a new research tool. We are interested in: 1) modelling the whole natural history of primary tumor and secondary distant metastases growth in patients with lymph nodes metastases; 2) developing adequate and precise CoM-III which reflects relations between primary tumor and secondary distant metastases; 3) analyzing the CoM-III scope of application; 4) implementing the model as a software tool. Firstly, the CoM-III includes exponential tumor growth model as a system of determinate nonlinear and linear equations. Secondly, mathematical model corresponds to TNM classification. It allows to calculate different growth periods of primary tumor and secondary distant metastases growth in patients with lymph nodes metastases: 1) ‘non-visible period’ for primary tumor; 2) ‘non-visible period’ for secondary distant metastases growth in patients with lymph nodes metastases; 3) ‘visible period’ for secondary distant metastases growth in patients with lymph nodes metastases. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. Thus, the CoM-III model and predictive software: a) detect different growth periods of primary tumor and secondary distant metastases growth in patients with lymph nodes metastases; b) make forecast of the period of the distant metastases appearance in patients with lymph nodes metastases; c) have higher average prediction accuracy than the other tools; d) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoM-III: the number of doublings for ‘non-visible’ and ‘visible’ growth period of secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of secondary distant metastases. The CoM-III enables, for the first time, to predict the whole natural history of primary tumor and secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on primary tumor sizes. Summarizing: a) CoM-III describes correctly primary tumor and secondary distant metastases growth of IA, IIA, IIB, IIIB (T1-4N1-3M0) stages in patients with lymph nodes metastases (N1-3); b) facilitates the understanding of the appearance period and inception of secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, primary tumor, secondary metastases, survival

Procedia PDF Downloads 302