Search results for: miRNA:mRNA target prediction

Authors: A. Afrashi

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Introduction: Synaesthesia is a term denoting the perception or description of the perception of one sense modality in terms of another. In literature, synaesthesia refers to a technique adopted by writers to present ideas, characters or places in such a manner that they appeal to more than one sense like hearing, seeing, smell etc. at a given time. In everyday language too we find many examples of synaesthesia. We commonly hear phrases like ‘loud colors’, ‘frozen silence’ and ‘warm colors’, ‘bitter cold’ etc. Empirical cognitive studies have proved that synaesthetic representations both in literature and everyday languages are constrained ie. they do not map randomly among sensory domains. From the beginning of the 20th century Synaesthesia has been a research domain both in literature and structural linguistics. However the exploration of cognitive mechanisms motivating synaesthesia, have made it an important topic in 21st century cognitive linguistics and literary studies. Synaesthetic metaphors are linguistic representations of those mental mechanisms, the study of which reveals invaluable facts about perception, cognition and conceptualization. According to the main tenets of cognitive approach to language and literature, unified and similar cognitive mechanisms are active both in everyday language and literature, and synaesthesia is one of those cognitive mechanisms. Main objective of the present research is to answer the following questions: What types of sense transfers are accessible in Persian synaesthetic metaphors. How are these types of sense transfers cognitively explained. What are the results of cross-linguistic comparative study of synaestetic metaphors based on the existing observations? Methodology: The present research employs a cognitive - corpus based method, and the theoretical framework adopted to analyze linguistic synaesthesia is the contemporary theory of metaphor, where conceptual metaphor is the result of systemic mappings across cognitive domains. Persian Language Data- base (PLDB) in the Institute for Humanities and Cultural Studies which consists mainly of Persian modern prose, is searched for synaesthetic metaphors. Then for each metaphorical structure, the source and target domains are determined. Then sense transfers are identified and the types of synaesthetic metaphors recognized. Findings: Persian synaesthetic metaphors conform to the hierarchical distribution principle, according to which transfers tend to go from touch to taste to smell to sound and to sight, not vice versa. In other words mapping from more accessible or basic concepts onto less accessible or less basic ones seems more natural. Furthermore the most frequent target domain in Persian synaesthetic metaphors is sound. Certain characteristics of Persian synaesthetic metaphors are comparable with existing related researches carried on English, French, Hungarian and Chinese synaesthetic metaphors. Conclusion: Cognitive corpus based approaches to linguistic synaesthesia, are applicable to stylistics and literary criticism and this recent research domain is an efficient approach to study cross linguistic variations to find out which of the five senses is dominant cross linguistically and cross culturally as the target domain in metaphorical mappings , and so forth receiving dominance in conceptualizations.

Keywords: cognitive semantics, conceptual metaphor, synaesthesia, corpus based approach

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Authors: Qin Yao

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Content and language integrated instruction provides second language learners instruction in subject matter and language, and is greatly valued, particularly in the language immersion classroom where a language other than students’ first language is the vehicle for teaching school curriculum. Corrective feedback is an essential instructional technique for teachers to integrate a focus on language into their content instruction. This study aims to fill a gap in the literature on immersion—the lack of studies examining corrective feedback in Chinese immersion classrooms, by studying learning opportunities brought by oral corrective feedback in a Chinese immersion classroom. Specifically, it examines what is the distribution of different types of teacher corrective feedback and how students respond to each feedback type, as well as how the focus of the teacher-student interactional exchanges affect the effect of feedback. Two Chinese immersion teachers and their immersion classes were involved, and data were collected through classroom observations interviews. Observations document teachers’ provision of oral corrective feedback and students’ responses following the feedback in class, and interviews with teachers collected teachers’ reflective thoughts about their teaching. A primary quantitative and qualitative analysis of the data revealed that, among different types of corrective feedback, recast occurred most frequently. Metalinguistic clue and repetition were the least occurring feedback types. Clarification request lead to highest percentage of learner uptake manifested by learners’ oral production immediately following the feedback, while explicit correction came the second and recast the third. In addition, the results also showed the interactional context played a role in the effectiveness of the feedback: teachers were most likely to give feedback in conversational exchanges that focused on explicit language and content, while students were most likely to use feedback in exchanges that focused on explicit language. In conclusion, the results of this study indicate recasts are preferred by Chinese immersion teachers, confirming results of previous studies on corrective feedback in non-Chinese immersion classrooms; and clarification request and explicit language instruction elicit more target language production from students and are facilitative in their target language development, thus should not be overlooked in immersion and other content and language integrated classrooms.

Keywords: Chinese immersion, content and language integrated instruction, corrective feedback, interaction

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Authors: Moldir Makenova

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Alongside the implementation of trilingual education in schools, the Ministry of Education and Science of the Republic of Kazakhstan innovated the school curriculum in 2013 to include a Content and Language Integrated Learning (CLIL) approach. In this regard, some transition issues have arisen, such as unprepared teachers, a need for more awareness of the CLIL approach, and teaching resources. Some teachers view it as a challenge due to its combination of both content and language. This often creates anxiety among teachers who are knowledgeable about their subject areas in Kazakh or Russian but are deficient in delivering the subject’s content in English. Thus, with this new teaching approach, teachers encounter to choose the role of language and answer how language works in the CLIL classroom. This study aimed to explore how teachers experience the changing role of language in the curriculum and to find out what challenges teachers face related to CLIL implementation and how their language proficiency influences their teaching practices. A qualitative comparative case study was conducted in an X Lyceum and a mainstream school piloting CLIL. Data collection procedures were conducted via semi-structured interviews, classroom observations, and document analysis. Eight content teachers were chosen from these two schools as the target group of this study. Subject teachers, rather than language teachers, were chosen as the target group to grasp how the language-related issues in the new curriculum are interpreted by educators who do not necessarily identify themselves as language experts at the outset. The findings showed that mainstream teachers prioritize content over language because, as content teachers, the knowledge of content is more essential for them rather than the language. In contrast, most X Lyceum teachers balance language and content and additionally showed their preferences to support the ‘English language only' policy among 10-11 graders. Moreover, due to the low-level English proficiency, mainstream teachers did highlight the necessity of CLIL training and further collaboration with language teachers. This study will be beneficial for teachers and policy-makers to enable them to solve the issues mentioned above related to the implementation of CLIL. Larger-scale research conducted in the future would further inform its successful deployment country-wide.

Keywords: role of language, trilingual education, updated curriculum, teacher practices

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Authors: Habtamu Ayenew Asegie

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Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

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Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Sonia Ben Hamza, Sabra Habli, Nejla Mahjoub Said, Hervé Bournot, Georges Le Palec

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In this work, we determine the turbulent dynamic structure of pollutant dispersion in two-phase free surface flow. The numerical simulation was performed using ANSYS Fluent. The flow study is three-dimensional, unsteady and isothermal. The study area has been endowed with a rectangular obstacle to analyze its influence on the hydrodynamic variables and progression of the pollutant. The numerical results show that the hydrodynamic model provides prediction of the dispersion of a pollutant in an open channel flow and reproduces the recirculation and trapping the pollutant downstream near the obstacle.

Keywords: CFD, free surface, polluant dispersion, turbulent flows

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Authors: Aslı Yörük, Mevla Yahya, Banu Tavat

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It is well established that bilinguals outperform monolinguals on executive function tasks. However, the effects of bilingualism on prospective memory (PM), which also requires executive functions, have not been investigated vastly. This study aimed to compare bi and monolingual participants' PM performance in focal and non-focal PM tasks. Considering that bilinguals have greater executive function abilities than monolinguals, we predict that bilinguals’ PM performance would be higher than monolinguals on the non-focal PM task, which requires controlled monitoring processes. To investigate these predictions, we administered the focal and non-focal PM task and measured the PM and ongoing task performance. Forty-eight Turkish-English bilinguals residing in North Macedonia and forty-eight Turkish monolinguals living in Turkey between the ages of 18-30 participated in the study. They were instructed to remember responding to rarely appearing PM cues while engaged in an ongoing task, i.e., spatial working memory task. The focality of the task was manipulated by giving different instructions for PM cues. In the focal PM task, participants were asked to remember to press an enter key whenever a particular target stimulus appeared in the working memory task; in the non-focal PM task, instead of responding to a specific target shape, participants were asked to remember to press the enter key whenever the background color of the working memory trials changes to a specific color (yellow). To analyze data, we performed a 2 × 2 mixed factorial ANOVA with the task (focal versus non-focal) as a within-subject variable and language group (bilinguals versus monolinguals) as a between-subject variable. The results showed no direct evidence for a bilingual advantage in PM. That is, the group’s performance did not differ in PM accuracy and ongoing task accuracy. However, bilinguals were overall faster in the ongoing task, yet this was not specific to PM cue’s focality. Moreover, the results showed a reversed effect of PM cue's focality on the ongoing task performance. That is, both bi and monolinguals showed enhanced performance in the non-focal PM cue task. These findings raise skepticism about the literature's prevalent findings and theoretical explanations. Future studies should investigate possible alternative explanations.

Keywords: bilingualism, executive functions, focality, prospective memory

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Authors: Adel Alblihy, Reem Ali, Mashael Algethami, Ahmed Shoqafi, Michael S. Toss, Juliette Brownlie, Natalie J. Tatum, Ian Hickson, Paloma Ordonez Moran, Anna Grabowska, Jennie N. Jeyapalan, Nigel P. Mongan, Emad A. Rakha, Srinivasan Madhusudan

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Platinum resistance is a clinical challenge in ovarian cancer. Platinating agents induce DNA damage which activate Mre11 nuclease directed DNA damage signalling and response (DDR). Upregulation of DDR may promote chemotherapy resistance. Here we have comprehensively evaluated Mre11 in epithelial ovarian cancers. In clinical cohort that received platinum- based chemotherapy (n=331), Mre11 protein overexpression was associated with aggressive phenotype and poor progression free survival (PFS) (p=0.002). In the ovarian cancer genome atlas (TCGA) cohort (n=498), Mre11 gene amplification was observed in a subset of serous tumours (5%) which correlated highly with Mre11 mRNA levels (p<0.0001). Altered Mre11 levels was linked with genome wide alterations that can influence platinum sensitivity. At the transcriptomic level (n=1259), Mre11 overexpression was associated with poor PFS (p=0.003). ROC analysis showed an area under the curve (AUC) of 0.642 for response to platinum-based chemotherapy. Pre-clinically, Mre11 depletion by gene knock down or blockade by small molecule inhibitor (Mirin) reversed platinum resistance in ovarian cancer cells and in 3D spheroid models. Importantly, Mre11 inhibition was synthetically lethal in platinum sensitive XRCC1 deficient ovarian cancer cells and 3D-spheroids. Selective cytotoxicity was associated with DNA double strand break (DSB) accumulation, S-phase cell cycle arrest and increased apoptosis. We conclude that pharmaceutical development of Mre11 inhibitors is a viable clinical strategy for platinum sensitization and synthetic lethality in ovarian cancer.

Keywords: MRE11; XRCC1, ovarian cancer, platinum sensitization, synthetic lethality

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Authors: Sahar Nasr, Lin Li, Edwin Wang

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Bladder cancer (BLCA) is known as the 13th cause of death among cancer patients worldwide, and ~575,000 new BLCA cases are diagnosed each year. Urothelial carcinoma (UC) is the most prevalent subtype among BLCA patients, which can be categorized into muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC). Currently, various therapeutic options are available for UC patients, including (1) transurethral resection followed by intravesical instillation of chemotherapeutics or Bacillus Calmette-Guérin for NMIBC patients, (2) neoadjuvant platinum-based chemotherapy (NAC) plus radical cystectomy is the standard of care for localized MIBC patients, and (3) systematic chemotherapy for metastatic UC. However, conventional treatments may lead to several challenges for treating patients. As an illustration, some patients may suffer from recurrence of the disease after the first line of treatment. Recently, immune checkpoint therapy (ICT) has been introduced as an alternative treatment strategy for the first or second line of treatment in advanced or metastatic BLCA patients. Although ICT showed lucrative results for a fraction of BLCA patients, ~80% of patients were not responsive to it. Therefore, novel treatment methods are required to augment the ICI response rate within BLCA patients. It has been shown that the infiltration of T-cells into the tumor microenvironment (TME) is positively correlated with the response to ICT within cancerous patients. Therefore, the goal of this study is to enhance the infiltration of cytotoxic T-cells into TME through the identification of target genes within the tumor that are responsible for the non-T-cell inflamed TME and their inhibition. BLCA bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) and immune score for TCGA samples were used to determine the Pearson correlation score between the expression of different genes and immune score for each sample. The genes with strong negative correlations were selected (r < -0.2). Thereafter, the correlation between the expression of each gene and survival in BLCA patients was calculated using the TCGA data and Cox regression method. The genes that are common in both selected gene lists were chosen for further analysis. Afterward, BLCA bulk and single-cell RNA-sequencing data were ranked based on the expression of each selected gene and the top and bottom 25% samples were used for pathway enrichment analysis. If the pathways related to the T-cell infiltration (e.g., antigen presentation, interferon, or chemokine pathways) were enriched within the low-expression group, the gene was included for downstream analysis. Finally, the selected genes will be used to calculate the correlation between their expression and the infiltration rate of the activated CD+8 T-cells, natural killer cells and the activated dendric cells. A list of potential target genes has been identified and ranked based on the above-mentioned analysis and criteria. SUN-1 got the highest score within the gene list and other identified genes in the literature as benchmarks. In conclusion, inhibition of SUN1 may increase the tumor-infiltrating lymphocytes and the efficacy of ICI in BLCA patients. BLCA tumor cells with and without SUN-1 CRISPR/Cas9 knockout will be injected into the syngeneic mouse model to validate the predicted SUN-1 effect on increasing tumor-infiltrating lymphocytes.

Keywords: data analysis, gene expression analysis, gene identification, immunoinformatic, functional genomics, transcriptomics

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Authors: Salman Akrokayan

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Synthetic cDNA (ScDNA) of granulocyte colony-stimulating factor (G-CSF) was constructed using a DNA synthesizer with the aim to increase its expression level. 5' end of the ScDNA of G-CSF coding region was modified by decreasing the GC content without altering the predicted amino acids sequence. The identity of the resulting protein from ScDNA was confirmed by the highly specific enzyme-linked immunosorbent assay. In conclusion, a synthetic G-CSF cDNA in combination with the recombinant DNA protocol offers a rapid and reliable strategy for synthesizing the target protein. However, the commercial utilization of this methodology requires rigorous validation and quality control.

Keywords: synthetic cDNA, recombinant G-CSF, cloning, gene expression

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Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

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A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

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Authors: Joseph Fitoussi, Mohammadali Shirinbayan, Abbas Tcharkhtchi

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Due to material flow during processing, short fiber reinforced composites structures obtained by injection or compression molding generally present strong spatial microstructure variation. On the other hand, quasi-static, dynamic, and fatigue behavior of these materials are highly dependent on microstructure parameters such as fiber orientation distribution. Indeed, because of complex damage mechanisms, SFRC structures design is a key challenge for safety and reliability. In this paper, we propose a micromechanical model allowing prediction of damage behavior of real structures as a function of microstructure spatial distribution. To this aim, a statistical damage criterion including strain rate and fatigue effect at the local scale is introduced into a Mori and Tanaka model. A critical local damage state is identified, allowing fatigue life prediction. Moreover, the multi-scale model is coupled with an experimental intrinsic link between damage under monotonic loading and fatigue life in order to build an abacus giving Tsai-Wu failure criterion parameters as a function of microstructure and targeted fatigue life. On the other hand, the micromechanical damage model gives access to the evolution of the anisotropic stiffness tensor of SFRC submitted to complex thermomechanical loading, including quasi-static, dynamic, and cyclic loading with temperature and amplitude variations. Then, the latter is used to fill out microstructure dependent material cards in finite element analysis for design optimization in the case of complex loading history. The proposed methodology is illustrated in the case of a real automotive component made of sheet molding compound (PSA 3008 tailgate). The obtained results emphasize how the proposed micromechanical methodology opens a new path for the automotive industry to lighten vehicle bodies and thereby save energy and reduce gas emission.

Keywords: short fiber reinforced composite, structural design, damage, micromechanical modelling, fatigue, strain rate effect

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Authors: Sudip Kumar Sinha, Saptarshi Ghosh

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While there’s a perpetual buzz around zinc oxide (ZnO) superstructures for their unique optical features, the versatile material has been constantly utilized to manifest tailored electronic properties through rendition of distinct morphologies. And yet, the unorthodox approach of implementing the novel 1D nanostructures of ZnO (pristine or doped) for volatile sensing applications has ample scope to accommodate new unconventional morphologies. In the last two decades, solid-state sensors have attracted much curiosity for their relevance in identifying pollutant, toxic and other industrial gases. In particular gas sensors based on metal oxide semiconducting (wide Eg) nanomaterials have recently attracted intensive attention owing to their high sensitivity and fast response and recovery time. These materials when exposed to air, the atmospheric O2 dissociates and get absorb on the surface of the sensors by trapping the outermost shell electrons. Finally a depleted zone on the surface of the sensors is formed, that enhances the potential barrier height at grain boundary . Once a target gas is exposed to the sensor, the chemical interaction between the chemisorbed oxygen and the specific gas liberates the trapped electrons. Therefore altering the amount of adsorbate is a considerable approach to improve the sensitivity of any target gas/vapour molecule. Likewise, this study presents a spontaneous but self catalytic creation of Sn-doped ZnO hexagonal nanoprisms on Si (100) substrates through thermal evaporation-condensation method, and their subsequent deployment for volatile sensing. In particular, the sensors were utilized to detect molecules of ethanol, acetone and ammonia below their permissible exposure limits which returned sensitivities of around 85%, 80% and 50% respectively. The influence of Sn concentration on the growth, microstructural and optical properties of the nanoprisms along with its role in augmenting the sensing parameters has been detailed. The single-crystalline nanostructures have a typical diameter ranging from 300 to 500 nm and a length that extends up to few micrometers. HRTEM images confirmed the hexagonal crystallography for the nanoprisms, while SAED pattern asserted the single crystalline nature. The growth habit is along the low index <0001>directions. It has been seen that the growth mechanism of the as-deposited nanostructures are directly influenced by varying supersaturation ratio, fairly high substrate temperatures, and specified surface defects in certain crystallographic planes, all acting cooperatively decide the final product morphology. Room temperature photoluminescence (PL) spectra of this rod like structures exhibits a weak ultraviolet (UV) emission peak at around 380 nm and a broad green emission peak in the 505 nm regime. An estimate of the sensing parameters against dispensed target molecules highlighted the potential for the nanoprisms as an effective volatile sensing material. The Sn-doped ZnO nanostructures with unique prismatic morphology may find important applications in various chemical sensors as well as other potential nanodevices.

Keywords: gas sensor, HRTEM, photoluminescence, ultraviolet, zinc oxide

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Authors: R. B. Mishra

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Hidden Markov Model (HMM) has been used in prediction and determination of states that generate different neural activations as well as mental working conditions. This paper addresses two applications of HMM; one to determine the optimal sequence of states for two neural states: Active (AC) and Inactive (IA) for the three emission (observations) which are for No Working (NW), Waiting (WT) and Working (W) conditions of human beings. Another is for the determination of optimal sequence of intentionality i.e. Believe (B), Desire (D), and Intention (I) as the states and three observational sequences: NW, WT and W. The computational results are encouraging and useful.

Keywords: hiden markov model, believe desire intention, neural activation, simulation

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Authors: B. Afsharipoor, E. Nazemi

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Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented.

Keywords: neural networks, image processing, segmentation, object recognition, image understanding, optimization, MANN

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Authors: Roni Naor-Hofri

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This paper shows how self-inflicted pain enabled the expression of love for God among Christian monastic ascetics in medieval central Europe. As scholars have shown, being in a state of pain leads to a change in or destruction of language, an essential feature of the self. The author argues that this transformation allows the self to transcend its boundaries as an object, even if only temporarily and in part. The epistemic achievement of love for God, a non-object, would not otherwise have been possible. To substantiate her argument, the author shows that the self’s transformation into a non-object enables the imitation of God: not solely in the sense of imitatio Christi, of physical and visual representations of God incarnate in the flesh of His son Christ, but also in the sense of the self’s experience of being a non-object, just like God, the target of the self’s love.

Keywords: love for God , pain, philosophy, religion

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Authors: Ching-Hua Li, Sheng-Hui Chen

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Sputtering is a popular technique with many advantages for thin film deposition. To fabricate a hydrogenated silicon thin film using sputtering process for solar cell applications, the ion bombardment during sputtering will generate microstructures (voids and columnar structures) to form silicon dihydride bodings as defects. The properties of heterojunction silicon solar cells were studied by using boron grains and silicon-boron targets. Finally, an 11.7% efficiency of solar cell was achieved by using all sputtering process.

Keywords: solar cell, sputtering process, pvd, alloy target

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Authors: Chin-Chen Chang, I-Ta Lee, Tsung-Ta Ke, Wen-Kai Tai

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Common methods for resizing image size include scaling and cropping. However, these two approaches have some quality problems for reduced images. In this paper, we propose an image resizing algorithm by separating the main objects and the background. First, we extract two feature maps, namely, an enhanced visual saliency map and an improved gradient map from an input image. After that, we integrate these two feature maps to an importance map. Finally, we generate the target image using the importance map. The proposed approach can obtain desired results for a wide range of images.

Keywords: energy map, visual saliency, gradient map, seam carving

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: F. Saleh, F. Lyall, A. Abdulsid, L. Marks

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Background: Labour in human is a complex biological process that involves interactions of neurological, hormonal and inflammatory pathways, with the placenta being a key regulator of these pathways. It is known that uterine contractions and labour pain cause physiological changes in gene expression in maternal and fetal blood, and in placenta during labour. Oxidative and inflammatory stress pathways are implicated in labour and they may cause alteration of placental gene expression. Additionally, in placental tissues, labour increases the expression of genes involved in placental oxidative stress, inflammatory cytokines, angiogenic regulators and apoptosis. Recently, Hydrogen Sulphide (H2S) has been considered as an endogenous gaseous mediator which promotes vasodilation and exhibits cytoprotective anti-inflammatory properties. The endogenous H2S is synthesised predominantly by two enzymes: cystathionine β-synthase (CBS) and cystathionine γ-lyase (CSE). As the H2S pathway has anti-oxidative and anti-inflammatory characteristics thus, we hypothesised that the expression of CBS and CSE in placental tissues would alter during labour. Methods: CBS and CSE expressions were examined in placentas using western blotting and RT-PCR in inner, middle and outer placental zones in placentas obtained from healthy non labouring women who delivered by caesarian section. These were compared with the equivalent zone of placentas obtained from women who had uncomplicated labour and delivered vaginally. Results: No differences in CBS and CSE mRNA or protein levels were found between the different sites within placentas in either the labour or non-labour group. There were no significant differences in either CBS or CSE expression between the two groups at the inner site and middle site. However, at the outer site there was a highly significant decrease in CBS protein expression in the labour group when compared to the non-labour group (p = 0.002). Conclusion: To the best of author’s knowledge, this is the first report to suggest that, CBS is expressed in a spatial manner within the human placenta. Further work is needed to clarify the precise function and mechanism of this spatial regulation although it is likely that inflammatory pathways regulation is a complex process in which this plays a role.

Keywords: anti-inflammatory, hydrogen sulphide, labour, oxidative stress

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Authors: Umi Pudji Astuti, Dedi Sugandi

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The effectiveness of agricultural extension is determined by the component in the agricultural extension system among others are agricultural extension methods. Effective methods should be selected and defined based on the characteristics of the target, the resources, the materials, and the objectives to be achieved. Citrus agribusiness development in Lebong is certainly supported by the role of stakeholders and citrus farmers, as well as the proper dissemination methods. Adoption in the extension process substantially can be interpreted as the changes of behavior process such as knowledge (cognitive), attitudes (affective), and skill (psycho-motoric) in a person after receiving "innovation" from extension submitted by target communities. Knowledge and perception are needed as a first step in adopting a innovation, especially of citrus agribusiness development in Lebong. The process of Specific technology adoption is influenced by internal factors and farmer perceptions of technological innovation. Internal factors such as formal education, experience trying to farm, owned land, production farm goods. The output of this study: 1) to analyze the effectiveness of field trial methods in improving cognitive and affective farmers; 2) Knowing the relationship of adoption level and knowledge of farmers; 3) to analyze the factors that influence farmers' adoption of citrus technology innovation. The method of this study is through the survey to 40 respondents in Rimbo Pengadang Sub District, Lebong District in 2014. Analyzing data is done by descriptive and statistical parametric (multiple linear functions). The results showed that: 1) Field trip method is effective to improve the farmer knowledge (23,17% ) and positively affect the farmer attitude; 2) the knowledge level of PTKJS innovation farmers "positively and very closely related".; 3) the factors that influence the level of farmers' adoption are internal factors (education, knowledge, and the intensity of training), and external factors respondents (distance from the house to the garden and from the house to production facilities shop).

Keywords: affect, adoption technology, citrus gerga, effectiveness dissemination

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Authors: Nidhi Gopal

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Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module.

Keywords: data packets, networking, router, routing

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Authors: Fei Ye, Åsa Barrefelt, Manuchehr Abedi-Valugerdi, Khalid M. Abu-Salah, Salman A. Alrokayan, Mamoun Muhammed, Moustapha Hassan

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With appropriate encapsulation in functional nanoparticles drugs are more stable in physiological environment and the kinetics of the drug can be more carefully controlled and monitored. Furthermore, targeted drug delivery can be developed to improve chemotherapy in cancer treatment, not only by enhancing intracellular uptake by target cells but also by reducing the adverse effects in non-target organs. Inorganic imaging agents, delivered together with anti-cancer drugs, enhance the local imaging contrast and provide precise diagnosis as well as evaluation of therapy efficacy. We have developed biodegradable polymeric vesicles as a nanocarrier system for multimodal bio-imaging and anticancer drug delivery. The poly (lactic-co-glycolic acid) PLGA) vesicles were fabricated by encapsulating inorganic imaging agents of superparamagnetic iron oxide nanoparticles (SPION), manganese-doped zinc sulfide (MN:ZnS) quantum dots (QDs) and the anticancer drug busulfan into PLGA nanoparticles via an emulsion-evaporation method. T2-weighted magnetic resonance imaging (MRI) of PLGA-SPION-Mn:ZnS phantoms exhibited enhanced negative contrast with r2 relaxivity of approximately 523 s-1 mM-1 Fe. Murine macrophage (J774A) cellular uptake of PLGA vesicles started fluorescence imaging at 2 h and reached maximum intensity at 24 h incubation. The drug delivery ability PLGA vesicles was demonstrated in vitro by release of busulfan. PLGA vesicles degradation was studied in vitro, showing that approximately 32% was degraded into lactic and glycolic acid over a period of 5 weeks. The biodistribution of PLGA vesicles was investigated in vivo by MRI in a rat model. Change of contrast in the liver could be visualized by MRI after 7 min and maximal signal loss detected after 4 h post-injection of PLGA vesicles. Histological studies showed that the presence of PLGA vesicles in organs was shifted from the lungs to the liver and spleen over time.

Keywords: biodegradable polymers, multifunctional nanoparticles, quantum dots, anticancer drugs

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Authors: Sadeq Al Yaari, Ayman Al Yaari, Adham Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Sajedah Al Yaari

Abstract:

Objective: This research aims at investigating the research ethics in the field of science. Method: It is an exploratory research wherein the researchers attempted to cover the phenomenon at hand from all specialists’ viewpoints. Results Discussion is based upon the findings resulted from the analysis the researcher undertook. Concerning the results’ prediction, the researcher needs first to seek highly qualified people in the field of research as well as in the field of statistics who share the philosophy of the research. Then s/he should make sure that s/he is adequately trained in the specific techniques, methods and statically programs that are used at the study. S/he should also believe in continually analysis for the data in the most current methods.

Keywords: research ethics, legal, rights, psychoneurolinguistics

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Authors: S. Srinivas, N. Ramesh Babu

Abstract:

This paper presents a model to predict the depth of penetration in polycrystalline ceramic material cut by abrasive waterjet. The proposed model considered the interaction of cylindrical jet with target material in upper region and neglected the role of threshold velocity in lower region. The results predicted with the proposed model are validated with the experimental results obtained with Silicon Carbide (SiC) blocks.

Keywords: abrasive waterjet cutting, analytical modeling, ceramics, micro-cutting and inter-grannular cracking

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Authors: Leila Keshavarz Afshar, Hedieh Sajedi

Abstract:

Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

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Authors: Kadda Boumediene, Mohamed Bouzit

Abstract:

The prediction of the flow around marine propellers and vessel hulls propeller interaction is one of the challenges of Computational fluid dynamics (CFD). The CFD has emerged as a potential tool in recent years and has promising applications. The objective of the current study is to predict the hydrodynamic performances of HSP marine propeller in open water and behind a vessel. The unsteady 3-D flow was modeled numerically along with respectively the K-ω standard and K-ω SST turbulence models for steady and unsteady cases. The hydrodynamic performances such us a torque and thrust coefficients and efficiency show good agreement with the experiment results.

Keywords: seiun maru propeller, steady, unstead, CFD, HSP

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Authors: Minjuan Sun

Abstract:

Ever since the late 1990s, China has experienced explosive growth in consumer lending, especially in short-term consumer loans, among which, the growth rate of non-bank lending has surpassed bank lending due to the development in financial technology. On the other hand, China does not have a universal credit scoring and registration system that can guide lenders during the processes of credit evaluation and risk control, for example, an individual’s bank credit records are not available for online lenders to see and vice versa. Given this context, the purpose of this paper is three-fold. First, we explore if and how alternative digital footprint data can be utilized to assess borrower’s creditworthiness. Then, we perform a comparative analysis of machine learning methods for the canonical problem of credit default prediction. Finally, we analyze, from an institutional point of view, the necessity of establishing a viable and nationally universal credit registration and scoring system utilizing online digital footprints, so that more people in China can have better access to the consumption loan market. Two different types of digital footprint data are utilized to match with bank’s loan default records. Each separately captures distinct dimensions of a person’s characteristics, such as his shopping patterns and certain aspects of his personality or inferred demographics revealed by social media features like profile image and nickname. We find both datasets can generate either acceptable or excellent prediction results, and different types of data tend to complement each other to get better performances. Typically, the traditional types of data banks normally use like income, occupation, and credit history, update over longer cycles, hence they can’t reflect more immediate changes, like the financial status changes caused by the business crisis; whereas digital footprints can update daily, weekly, or monthly, thus capable of providing a more comprehensive profile of the borrower’s credit capabilities and risks. From the empirical and quantitative examination, we believe digital footprints can become an alternative information source for creditworthiness assessment, because of their near-universal data coverage, and because they can by and large resolve the "thin-file" issue, due to the fact that digital footprints come in much larger volume and higher frequency.

Keywords: credit score, digital footprint, Fintech, machine learning

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Authors: Phalla Miech, William Leung, Ty Chhay, Sina Vor, Arata Hidano

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Smallholder pig farms (SPFs) are prevalent in Cambodia but are vulnerable to disease impacts, as evidenced by the recent incursion of African swine fever into the region. As part of the ‘PigFluCam+’ project, we sought to provide an updated picture of pig husbandry and biosecurity practices among SPFs in south-central Cambodia. A multi-stage sampling design was adopted to select study districts and villages within four provinces: Phnom Penh, Kandal, Takeo, and Kampong Speu. Structured interviews were conductedbetween October 2020 - May 2021 among all consenting households keeping pigs in 16 target villages. Recruited SPFs (n=176) kept 6.8 pigs on average (s.d.=7.7), with most (88%) keeping cross-bred varieties of sows (77%), growers/finishers (39%), piglets/weaners (22%), and few keeping boars (5%). Chickens (83%) and waterfowl (56%) were commonly raised and could usually contact pigs directly (79%). Pigs were the primary source of household income for 28% of participants. While pigs tended to be housed individually (40%) or in groups (33%), 13% kept pigs free-ranging/tethered. Pigs were commonly fed agricultural by-products (80%), commercial feed (60%), and, notably, household waste (59%). Under half of SPFs vaccinated their pigs (e.g., against classical swine fever, Aujesky’s, and pasteurellosis, although the target disease was often unknown). Among 20 SPFs who experienced pig morbidities/mortalities within the past 6 months, only 3 (15%) reported to animal health workers, and disease etiology was rarely known. Common biosecurity measures included nets covering pig pens (62%) and restricting access to the site/pens (46%). Boot dips (0.6%) and PPE (1.2%) were rarely used. Pig smallholdings remain an important contributor to rural livelihoods. Current practices and biosecurity challenges increase risk pathways for a range of disease threats of both local and global concern. Ethnographic studies are needed to better understand local determinants and develop context-appropriate strategies.

Keywords: smallholder production, swine, biosecurity practices, Cambodia, African swine fever

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Authors: Adhitya Mangala, Yobel

Abstract:

Bittuang geothermal prospect area located at Tana Toraja district, South Sulawesi. The geothermal system of the area related to Karua Volcano eruption product. This area has surface manifestation such as fumarole, hot springs, sinter silica and mineral alteration. Those prove that there are hydrothermal activities in the subsurface. However, the project and development of the area have not implemented yet. One of the important elements in geothermal exploration is to determine upflow and outflow zone. This information very useful to identify the target for geothermal wells and development which it is a risky task. The methods used in this research were Mercury (Hg) anomalies in soil, soil and manifestation temperature distribution and fault fracture density from 93 km² research area. Hg anomalies performed to determine the distribution of hydrothermal alteration. Soil and manifestation temperature distribution were conducted to estimate heat distribution. Fault fracture density (FFD) useful to determine fracture intensity and trend from surface observation. Those deliver Hg anomaly map, soil and manifestation temperature map that combined overlayed to fault fracture density map and geological map. Then, the conceptual model made from north – south, and east – west cross section to delineate upflow and outflow zone in this area. The result shows that upflow zone located in northern – northeastern of the research area with the increase of elevation and decrease of Hg anomalies and soil temperature. The outflow zone located in southern - southeastern of the research area which characterized by chloride, chloride - bicarbonate geothermal fluid type, higher soil temperature, and Hg anomalies. The range of soil temperature distribution from 16 – 19 °C in upflow and 19 – 26.5 °C in the outflow. The range of Hg from 0 – 200 ppb in upflow and 200 – 520 ppb in the outflow. Structural control of the area show northwest – southeast trend. The boundary between upflow and outflow zone in 1550 – 1650 m elevation. This research delivers the conceptual model with innovative methods that useful to identify a target for geothermal wells, project, and development in Bittuang geothermal prospect area.

Keywords: Bittuang geothermal prospect area, Hg anomalies, soil temperature, upflow and outflow zone

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