Search results for: neural smith predictor

Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

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Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

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Authors: George Mahalu

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The Chua diode has been configured over time in various ways, using electronic structures like operational amplifiers (AOs) or devices with gas or semiconductors. When discussing the use of semiconductor devices, tunnel diodes (Esaki diodes) are most often considered, and more recently, transistorized configurations such as lambda diodes. The paperwork proposed here uses in the modeling a lambda diode type configuration consisting of two junction field effect transistors (JFET). The original scheme is created in the MULTISIM electronic simulation environment and is analyzed in order to identify the conditions for the appearance of evolutionary unpredictability specific to nonlinear dynamic systems with chaos-induced behavior. The chaotic deterministic oscillator is one autonomous type, a fact that places it in the class of Chua’s type oscillators, the only significant and most important difference being the presence of a nonlinear device like the one mentioned structure above. The chaotic behavior is identified both by means of strange attractor-type trajectories and visible during the simulation and by highlighting the hypersensitivity of the system to small variations of one of the input parameters. The results obtained through simulation and the conclusions drawn are useful in the further research of ways to implement such constructive electronic solutions in theoretical and practical applications related to modern small signal amplification structures, to systems for encoding and decoding messages through various modern ways of communication, as well as new structures that can be imagined both in modern neural networks and in those for the physical implementation of some requirements imposed by current research with the aim of obtaining practically usable solutions in quantum computing and quantum computers.

Keywords: chua, diode, memristor, chaos

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Authors: Peng, Chun-Yi, Chen, Wei-Hsuan, Ueng, Shyh-Kuang

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Samir Kumar Pal

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Background: Targeted rapid degradation of bilirubin has the potential to thwart incipient bilirubin encephalopathy. Uncontrolled hyperbilirubinemia is a potential problem in developing countries, including India, because of the lack of reliable healthcare institutes for conventional phototherapy. In India, most of the rural subjects duel in the exchange limit during transport, leading to a risk of kernicterus when they arrive at the treatment centre. Thus, an alternative pharmaceutical agent is needed for the hours. Objective: Exploration of a distinct therapeutic strategy for the control of neonatal hyperbilirubinemia compared to conventional phototherapy in a clinical setting. Method: We synthesized, characterized and investigated a spinel-structured Manganese citrate nanocomplex (C-Mn₃O₄ NC, the nanodrug) along with conventional phototherapy in neonatal subjects. We have also observed BIND scores in order to assess neurological dysfunctions. Results: Our observational study clearly reveals that the rate of declination of bilirubin in neonatal subjects with nanodrug oral administration and phototherapy is faster compared to that in the case of phototherapy only. The associated neural dysfunctions were also found to be significantly lower in the case of combined therapy. Conclusion: This study demonstrates that combined therapy works better than conventional phototherapy only for the control of hyperbilirubinemia. We have observed that a significant portion of neonatal subjects requiring blood exchange has been prevented with the combined therapeutic strategy. Further compilation of a drug-safety-dossier is warranted to translate this novel therapeutic chemo preventive approach to clinical settings.

Keywords: nanodrug, nanoparticle, Neonatal hyperbilirubinemia, alternative to phototherapy, redox modulation, redox medicine

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Authors: N. Šarlija, S. Pfeifer, M. Jeger, A. Bilandžić

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Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.

Keywords: growth measurement constructs, logistic regression, prediction of growth potential, small and medium-sized enterprises

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Jasna Hrncic

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Poor family relations predict depression, but also to other mental health issues. Mediating effect of self-efficacy and differentiation of self and moderating effect of decreased accessibility and/or success of other adaptive and defensive mechanisms for overcoming social disadvantages could explain depression as a specific outcome of dysfunctional family relations. The present study analyzes the mediation effect of self-efficacy and differentiation of self from poor family functioning to depressive symptoms and the moderation effect of deprivation of liberty on the listed mediation effect. Deprivation of liberty has, as a general consequence, a decreased accessibility and/or success of many adaptive and defensive mechanisms. It is hypothesized that: 1) self-efficacy and differentiation of self will mediate between family functioning and depressiveness in the total sample, and 2) deprivation of liberty will moderate the stated relations. Cross-sectional study was conducted among 323 male juveniles in Serbia divided in three groups: 98 adolescents deprived of their liberty due to antisocial behavior (incarcerated antisocial group - IAG), 121 adolescents with antisocial behavior in their natural setting (antisocial control group - CAG) and 105 adolescents in general population (general control group - CGG). The CAG was included along with GCG to control the possible influence that comorbidity of antisocial behavior and depressiveness could have on results. Instruments for family relations assessment were: for a whole family of origin the emotional exchange scale and individuation scale from GRADIR by Knezevic, and for a relationship with mother PCS-YSR and CRPBI by barber, and intimacy, rejection, sacrifice, punishment, demands, control and internal control by Opacic and Kos. Differentiation of self (DOS) is measured by emotional self scale (Opacic), self-efficacy (SE) by general incompetence scale by Bezinovic, and depression by BDI (Back), CES-D (Radloff) and D6R (Momirovic). Two-path structural equation modeling based on most commonly reported fit indices, showed that the mediation model had unfavorable fit to our data for total sample [(χ2 (1, N = 324) = 13.73); RMSEA= .20 (90% CI= [.12, .30]); CFI= .98; NFI= .97; AIC=31.73]. Path model provided an adequate fit to the data only for AIG - and not to the data from ACG and GCG. SE and DOS mediated the relationship between PFF and depressiveness. Test of the indirect effects revealed that 23.85% of PFF influences on depressiveness is mediated by these two mediators (the quotient of mediated effect = .24). Test of specific indirect effects showed that SE mediates 22.17%, while DOS mediates 1.67% of PFF influence on depressiveness. Lack of expected mediation effect could be explained by missing other potential mediators (i.e., relationship with that father, social skills, self-esteem) and lower variability of both predictor and criterion variable due to their low levels on the whole sample and on control subsamples. Results suggested that inaccessibility and/or successfulness of other adaptive and defensive mechanisms for overcoming social disadvantages has a strong impact on the mediation effect of self/efficacy and differentiation of self from poor family functioning to depressive symptoms. Further researches could include other potential mediators and a sample of clinically depressed people.

Keywords: antisocial behavior, mediating effect, moderating effect, natural setting, incarceration

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Shabadini Sampath, Jinglang Feng

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With huge kinetic energy, space debris poses a major threat to astronauts’ space activities and spacecraft in orbit if a collision happens. The active removal of space debris is required in order to avoid frequent collisions that would occur. In addition, the amount of space debris will increase uncontrollably, posing a threat to the safety of the entire space system. But the safe and reliable removal of large-scale space debris has been a huge challenge to date. While capturing and deorbiting space debris, the space manipulator has to achieve high control precision. However, due to uncertainties and unknown disturbances, there is difficulty in coordinating the control of the space manipulator. To address this challenge, this paper focuses on developing a robust and intelligent control algorithm that controls joint movement and restricts it on the sliding manifold by reducing uncertainties. A neural network adaptive sliding mode controller (NNASMC) is applied with the objective of finding the control law such that the joint motions of the space manipulator follow the given trajectory. A computed torque control (CTC) is an effective motion control strategy that is used in this paper for computing space manipulator arm torque to generate the required motion. Based on the Lyapunov stability theorem, the proposed intelligent controller NNASMC and CTC guarantees the robustness and global asymptotic stability of the closed-loop control system. Finally, the controllers used in the paper are modeled and simulated using MATLAB Simulink. The results are presented to prove the effectiveness of the proposed controller approach.

Keywords: GNC, active removal of space debris, AI controllers, MatLabSimulink

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Authors: Muziwandile Luthuli

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Although numerous factors predicting adherence to antiretroviral therapy (ART) among people living with HIV/AIDS (PLWHA) have been broadly studied on both regional and global level, up-to-date adherence of patients to ART remains an overarching, dynamic and multifaceted problem that needs to be investigated over time and across various contexts. There is a rarity of empirical data in the literature on interactive mechanisms by which psychosocial factors influence adherence to ART among PLWHA within the South African context. Therefore, this study was designed to investigate the relationship between depression, HIV stigma, and adherence to ART among adult patients living with HIV at a tertiary hospital in Durban, South Africa, and the mediating roles of self-efficacy and social support. The health locus of control theory and the social support theory were the underlying theoretical frameworks for this study. Using a cross-sectional research design, a total of 201 male and female adult patients aged between 18-75 years receiving ART at a tertiary hospital in Durban, KwaZulu-Natal were sampled, using time location sampling (TLS). A self-administered questionnaire was employed to collect the data in this study. Data were analysed through SPSS version 27. Several statistical analyses were conducted in this study, namely univariate statistical analysis, correlational analysis, Pearson’s chi-square analysis, cross-tabulation analysis, binary logistic regression analysis, and mediational analysis. Univariate analysis indicated that the sample mean age was 39.28 years (SD=12.115), while most participants were females 71.0% (n=142), never married 74.2% (n=147), and most were also secondary school educated 48.3% (n=97), as well as unemployed 65.7% (n=132). The prevalence rate of participants who had high adherence to ART was 53.7% (n=108), and 46.3% (n=93) of participants had low adherence to ART. Chi-square analysis revealed that employment status was the only statistically significant socio-demographic influence of adherence to ART in this study (χ2 (3) = 8.745; p < .033). Chi-square analysis showed that there was a statistically significant difference found between depression and adherence to ART (χ2 (4) = 16.140; p < .003), while between HIV stigma and adherence to ART, no statistically significant difference was found (χ2 (1) = .323; p >.570). Binary logistic regression indicated that depression was statistically associated with adherence to ART (OR= .853; 95% CI, .789–.922, P < 001), while the association between self-efficacy and adherence to ART was statistically significant (OR= 1.04; 95% CI, 1.001– 1.078, P < .045) after controlling for the effect of depression. However, the findings showed that the effect of depression on adherence to ART was not significantly mediated by self-efficacy (Sobel test for indirect effect, Z= 1.01, P > 0.31). Binary logistic regression showed that the effect of HIV stigma on adherence to ART was not statistically significant (OR= .980; 95% CI, .937– 1.025, P > .374), but the effect of social support on adherence to ART was statistically significant, only after the effect of HIV stigma was controlled for (OR= 1.017; 95% CI, 1.000– 1.035, P < .046). This study promotes behavioral and social change effected through evidence-based interventions by emphasizing the need for additional research that investigates the interactive mechanisms by which psychosocial factors influence adherence to ART. Depression is a significant predictor of adherence to ART. Thus, to alleviate the psychosocial impact of depression on adherence to ART, effective interventions must be devised, along with special consideration of self-efficacy and social support. Therefore, this study is helpful in informing and effecting change in health policy and healthcare services through its findings

Keywords: ART adherence, depression, HIV/AIDS, PLWHA

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Lindsey D. Sams, Dean R. Gorman, Cathy D. Lirgg, Steve W. Dittmore, Jack C. Kern

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Golf is a lifetime sport that provides numerous physical and psychological benefits. The game has struggled with attrition and retention within minority groups and this has exposed the lack of a modified introduction to the game that is uniformly accessible and developmentally appropriate. Factors that have been related to sport participatory behaviors include perceived competence, enjoyment and intention. The purpose of this study was to examine self-reported perception differences in competence and enjoyment between learners using modified and traditional equipment as well as the potential effects these factors could have on intent for future participation. For this study, SNAG Golf was chosen to serve as the scaled equipment used by the modified equipment group. The participants in this study were 99 children (24 traditional equipment users/ 75 modified equipment users) located across the U.S. with ages ranging from 7 to 12 years (2nd-5th grade). Utilizing a convenience sampling method, data was obtained on a voluntary basis through surveys measuring children’s golf participation and self-perceptions concerning perceived competence, enjoyment and intention to continue participation. The scales used for perceived competence and enjoyment included Susan Harter’s Self-Perception Profile for Children (SPPC) along with the Physical Activity Enjoyment Scale (PACES). Analysis revealed no significant differences for enjoyment, perceived competence or intention between children learning with traditional golf equipment and modified golf equipment. This was true even though traditional equipment users reported significantly higher experience levels than that of modified users. Intention was regressed on the enjoyment and perceived competence variables. Congruent with current literature, enjoyment was a strong predictor of intention to continue participation, for both groups. Modified equipment users demonstrated significantly lower experience levels but reported similar levels of competence, enjoyment and intent to continue participation as reported by the more experienced, and potentially more skilled, traditional users. The ability to immediately generate these positive affects suggests the potential adoption of a more effective way to learn golf and a method that is conducive to participatory behaviors related to attrition and retention. These implications in turn, highlight an equipment candidate ideal for inception into physical education programs where new learners are introduced to various sports in safe and developmentally appropriate environments. A major goal of this study was to provide foundational research that instigates the further examination of golf’s introductory teaching methodologies, as there is a lack of its presence in current literature. Future research recommendations range from improvements in the current research design to expansive approaches related to the topic, such as progressive skill development, knowledge of the game’s tactical and strategic concepts, playing ability and teaching effectiveness when utilizing modified versus traditional equipment.

Keywords: adaptive sports, enjoyment, golf participation, modified equipment, perceived competence, SNAG golf

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

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Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: climate change, Coastal Vulnerability Index, global warming, sea level rise

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Authors: Karen Koellner, Nanette Seago, Jennifer Jacobs, Helen Garnier

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Although studies of teacher professional development (PD) are prevalent, surprisingly most have only produced incremental shifts in teachers’ learning and their impact on students. There is a critical need to understand what teachers take up and use in their classroom practice after attending PD and why we often do not see greater changes in learning and practice. This paper is based on a mixed methods efficacy study of the Learning and Teaching Geometry (LTG) video-based mathematics professional development materials. The extent to which the materials produce a beneficial impact on teachers’ mathematics knowledge, classroom practices, and their students’ knowledge in the domain of geometry through a group-randomized experimental design are considered. In this study, we examine a small group of teachers to better understand their interpretations of the workshops and their classroom uptake. The participants included 103 secondary mathematics teachers serving grades 6-12 from two states in different regions. Randomization was conducted at the school level, with 23 schools and 49 teachers assigned to the treatment group and 18 schools and 54 teachers assigned to the comparison group. The case study examination included twelve treatment teachers. PD workshops for treatment teachers began in Summer 2016. Nine full days of professional development were offered to teachers, beginning with the one-week institute (Summer 2016) and four days of PD throughout the academic year. The same facilitator-led all of the workshops, after completing a facilitator preparation process that included a multi-faceted assessment of fidelity. The overall impact of the LTG PD program was assessed from multiple sources: two teacher content assessments, two PD embedded assessments, pre-post-post videotaped classroom observations, and student assessments. Additional data was collected from the case study teachers including additional videotaped classroom observations and interviews. Repeated measures ANOVA analyses were used to detect patterns of change in the treatment teachers’ content knowledge before and after completion of the LTG PD, relative to the comparison group. No significant effects were found across the two groups of teachers on the two teacher content assessments. Teachers were rated on the quality of their mathematics instruction captured in videotaped classroom observations using the Math in Common Observation Protocol. On average, teachers who attended the LTG PD intervention improved their ability to engage students in mathematical reasoning and to provide accurate, coherent, and well-justified mathematical content. In addition, the LTG PD intervention and instruction that engaged students in mathematical practices both positively and significantly predicted greater student knowledge gains. Teacher knowledge was not a significant predictor. Twelve treatment teachers were self-selected to serve as case study teachers to provide additional videotapes in which they felt they were using something from the PD they learned and experienced. Project staff analyzed the videos, compared them to previous videos and interviewed the teachers regarding their uptake of the PD related to content knowledge, pedagogical knowledge and resources used.

Keywords: teacher learning, professional development, pedagogical content knowledge, geometry

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Authors: Kenan Morani, Esra Kaya Ayana

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This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Vangmayee. V. Subban, Somashekara H. S, Shwetha Prabhu, Jayashree S. Bhat

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Introduction and Need: Phonological processing is critical in learning to read alphabetical and non-alphabetical languages. However, its role in learning to read Kannada an alphasyllabary is equivocal. The literature has focused on the developmental role of phonological awareness on reading. To the best of authors knowledge, the role of phonological memory and phonological naming has not been addressed in alphasyllabary Kannada language. Therefore, there is a need to evaluate the comprehensive role of the phonological processing skills in Kannada on word decoding skills during the early years of schooling. Aim and Objectives: The present study aimed to explore the phonological processing abilities and their role in learning to decode pseudowords in children learning to read the Kannada language during initial years of formal schooling between 5.6 to 8.6 years. Method: In this cross sectional study, 60 typically developing Kannada speaking children, 20 each from Grade I, Grade II, and Grade III between the age range of 5.6 to 6.6 years, 6.7 to 7.6 years and 7.7 to 8.6 years respectively were selected from Kannada medium schools. Phonological processing abilities were assessed using an assessment tool specifically developed to address the objectives of the present research. The assessment tool was content validated by subject experts and had good inter and intra-subject reliability. Phonological awareness was assessed at syllable level using syllable segmentation, blending, and syllable stripping at initial, medial and final position. Phonological memory was assessed using pseudoword repetition task and phonological naming was assessed using rapid automatized naming of objects. Both phonological awareneness and phonological memory measures were scored for the accuracy of the response, whereas Rapid Automatized Naming (RAN) was scored for total naming speed. Results: The mean scores comparison using one-way ANOVA revealed a significant difference (p ≤ 0.05) between the groups on all the measures of phonological awareness, pseudoword repetition, rapid automatized naming, and pseudoword reading. Subsequent post-hoc grade wise comparison using Bonferroni test revealed significant differences (p ≤ 0.05) between each of the grades for all the tasks except (p ≥ 0.05) for syllable blending, syllable stripping, and pseudoword repetition between Grade II and Grade III. The Pearson correlations revealed a highly significant positive correlation (p=0.000) between all the variables except phonological naming which had significant negative correlations. However, the correlation co-efficient was higher for phonological awareness measures compared to others. Hence, phonological awareness was chosen a first independent variable to enter in the hierarchical regression equation followed by rapid automatized naming and finally, pseudoword repetition. The regression analysis revealed syllable awareness as a single most significant predictor of pseudoword reading by explaining the unique variance of 74% and there was no significant change in R² when RAN and pseudoword repetition were added subsequently to the regression equation. Conclusion: Present study concluded that syllable awareness matures completely by Grade II, whereas the phonological memory and phonological naming continue to develop beyond Grade III. Amongst phonological processing skills, phonological awareness, especially syllable awareness is crucial for word decoding than phonological memory and naming during initial years of schooling.

Keywords: phonological awareness, phonological memory, phonological naming, phonological processing, pseudo-word decoding

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Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

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Authors: Amber Hatch, Eric Wright, Feihong Wang

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Research suggests that attachment in close relationships affects one’s emotional processes, mindfulness, conflict-management behaviors, and interpersonal interactions. Attachment insecurity is often associated with maladaptive social interactions and suboptimal relationship qualities. Past studies have considered how the nature of emotion regulation and mindfulness in teachers may be related to student or classroom outcomes. Still, no research has examined how the relationship between such internal experiences and classroom management outcomes may also be related to teachers’ attachment insecurity. This study examined the interrelationships between teachers’ attachment insecurity, mindfulness tendencies, emotion regulation abilities, and classroom management efficacy as indexed by students’ classroom behavior and teachers’ response effectiveness. Teachers’ attachment insecurity was evaluated using the global ECRS-SF, which measures both attachment anxiety and avoidance. The present study includes a convenient sample of 357 American elementary school teachers who responded to a survey regarding their classroom management efficacy, attachment in/security, dispositional mindfulness, emotion regulation strategies, and difficulties in emotion regulation, primarily assessed via pre-existing instruments. Good construct validity was demonstrated for all scales used in the survey. Sample demographics, including gender (94% female), race (92% White), age (M = 41.9 yrs.), years of teaching experience (M = 15.2 yrs.), and education level were similar to the population from which it was drawn, (i.e., American elementary school teachers). However, white women were slightly overrepresented in our sample. Correlational results suggest that teacher attachment insecurity is associated with poorer classroom management efficacy as indexed by students’ disruptive behavior and teachers’ response effectiveness. Attachment anxiety was a much stronger predictor of adverse student behaviors and ineffective teacher responses to adverse behaviors than attachment avoidance. Mindfulness, emotion regulation abilities, and years of teaching experience predicted positive classroom management outcomes. Attachment insecurity and mindfulness were more strongly related to frequent adverse student behaviors, while emotion regulation abilities were more strongly related to teachers’ response effectiveness. The teaching experience was negatively related to attachment insecurity and positively related to mindfulness and emotion regulation abilities. Although the data were cross-sectional, path analyses revealed that attachment insecurity is directly related to classroom management efficacy. Through two routes, this relationship is further mediated by emotion regulation and mindfulness in teachers. The first route of indirect effect suggests double mediation by teacher’s emotion regulation and then teacher mindfulness in the relationship between teacher attachment insecurity and classroom management efficacy. The second indirect effect suggests mindfulness directly mediated the relationship between attachment insecurity and classroom management efficacy, resulting in improved model fit statistics. However, this indirect effect is much smaller than the double mediation route through emotion regulation and mindfulness in teachers. Given the significant predication of teacher attachment insecurity, mindfulness, and emotion regulation on teachers’ classroom management efficacy both directly and indirectly, the authors recommend improving teachers’ classroom management efficacy via a three-pronged approach aiming at enhancing teachers’ secure attachment and supporting their learning adaptive emotion regulation strategies and mindfulness techniques.

Keywords: Classroom management efficacy, student behavior, teacher attachment, teacher emotion regulation, teacher mindfulness

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Authors: George Mahalu

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The Chua diode has been configured over time in various ways, using electronic structures like as operational amplifiers (OAs) or devices with gas or semiconductors. When discussing the use of semiconductor devices, tunnel diodes (Esaki diodes) are most often considered, and more recently, transistorized configurations such as lambda diodes. The paper-work proposed here uses in the modeling a lambda diode type configuration consisting of two Junction Field Effect Transistors (JFET). The original scheme is created in the MULTISIM electronic simulation environment and is analyzed in order to identify the conditions for the appearance of evolutionary unpredictability specific to nonlinear dynamic systems with chaos-induced behavior. The chaotic deterministic oscillator is one autonomous type, a fact that places it in the class of Chua’s type oscillators, the only significant and most important difference being the presence of a nonlinear device like the one mentioned structure above. The chaotic behavior is identified both by means of strange attractor-type trajectories and visible during the simulation and by highlighting the hypersensitivity of the system to small variations of one of the input parameters. The results obtained through simulation and the conclusions drawn are useful in the further research of ways to implement such constructive electronic solutions in theoretical and practical applications related to modern small signal amplification structures, to systems for encoding and decoding messages through various modern ways of communication, as well as new structures that can be imagined both in modern neural networks and in those for the physical implementation of some requirements imposed by current research with the aim of obtaining practically usable solutions in quantum computing and quantum computers.

Keywords: chaos, lambda diode, strange attractor, nonlinear system

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Authors: Fatin Khalida Abdul Khadir, Husna Takaijudin

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Bioretention system is one of the alternatives to approach the conventional stormwater management, low impact development (LID) strategy for best management practices (BMPs). Incorporating both filtration and infiltration, initial research on bioretention systems has shown that this practice extensively decreases runoff volumes and peak flows. The LID-BMP treatment train is one of the latest LID-BMPs for stormwater treatments in urbanized watersheds. The treatment train is developed to overcome the drawbacks that arise from conventional LID-BMPs and aims to enhance the performance of the existing practices. In addition, it is also used to improve treatments in both water quality and water quantity controls as well as maintaining the natural hydrology of an area despite the current massive developments. The objective of this paper is to review the effectiveness of the conventional LID-BMPS on hydrologic and hydraulic performances through column studies in different configurations. The previous studies on the applications of LID-BMP treatment train that were developed to overcome the drawbacks of conventional LID-BMPs are reviewed and use as the guidelines for implementing this system in Universiti Teknologi Petronas (UTP) and elsewhere. The reviews on the analysis conducted for hydrologic and hydraulic performances using the artificial neural network (ANN) model are done in order to be utilized in this study. In this study, the role of the LID-BMP treatment train is tested by arranging bioretention cells in series in order to be implemented for controlling floods that occurred currently and in the future when the construction of the new buildings in UTP completed. A summary of the research findings on the performances of the system is provided which includes the proposed modifications on the designs.

Keywords: bioretention system, LID-BMP treatment train, hydrological and hydraulic performance, ANN analysis

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Authors: Izabela P. Vatanabe, Rafaela Peron, Patricia Manzine, Marcia R. Cominetti

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Introduction: Considering the increase in life expectancy of world population, there is an emerging concern in health services to allocate better care and care to elderly, through promotion, prevention and treatment of health. It has been observed that frailty syndrome is prevalent in elderly people worldwide and this complex and heterogeneous clinical syndrome consist of the presence of physical frailty associated with cognitive dysfunction, though in absence of dementia. This can be characterized by exhaustion, unintentional weight loss, decreased walking speed, weakness and low level of physical activity, in addition, each of these symptoms may be a predictor of adverse outcomes such as hospitalization, falls, functional decline, institutionalization, and death. Cognitive frailty is a recent concept in literature, which is defined as the presence of physical frailty associated with mild cognitive impairment (MCI) however in absence of dementia. This new concept has been considered as a subtype of frailty, which along with aging process and its interaction with physical frailty, accelerates functional declines and can result in poor quality of life of the elderly. MCI represents a risk factor for Alzheimer's disease (AD) in view of high conversion rate for this disease. Comorbidities and physical frailty are frequently found in AD patients and are closely related to heterogeneity and clinical manifestations of the disease. The decreased platelets ADAM10 levels in AD patients, compared to cognitively healthy subjects, matched by sex, age and education. Objective: Based on these previous results, this study aims to evaluate whether ADAM10 platelet levels of could act as a biomarker of cognitive frailty. Methods: The study was approved by Ethics Committee of Federal University of São Carlos (UFSCar) and conducted in the municipality of São Carlos, headquarters of Federal University of São Carlos (UFSCar). Biological samples of subjects were collected, analyzed and then stored in a biorepository. ADAM10 platelet levels were analyzed by western blotting technique in subjects with MCI and compared to subjects without cognitive impairment, both with and without presence of frailty. Statistical tests of association, regression and diagnostic accuracy were performed. Results: The results have shown that ADAM10/β-actin ratio is decreased in elderly individuals with cognitive frailty compared to non-frail and cognitively healthy controls. Previous studies performed by this research group, already mentioned above, demonstrated that this reduction is still higher in AD patients. Therefore, the ADAM10/β-actin ratio appears to be a potential biomarker for cognitive frailty. The results bring important contributions to an accurate diagnosis of cognitive frailty from the perspective of ADAM10 as a biomarker for this condition, however, more experiments are being conducted, using a high number of subjects, and will help to understand the role of ADAM10 as biomarker of cognitive frailty and contribute to the implementation of tools that work in the diagnosis of cognitive frailty. Such tools can be used in public policies for the diagnosis of cognitive frailty in the elderly, resulting in a more adequate planning for health teams and better quality of life for the elderly.

Keywords: ADAM10, biomarkers, cognitive frailty, elderly

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Authors: Adriana Salgado, Sandra Castaneda, Ivan Perez

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Learning to read and write is a complex process involving several cognitive skills, contextual, and cultural environments. The basis of this development is linguistic skills, such as the ability to name and understand vocabulary, retell a story, phonological awareness, letter knowledge, among others. In children with autism spectrum disorder (ASD), one of the main concerns is related to language disorders. Nevertheless, most of the children with ASD are able to decode written information but have difficulties in reading comprehension. The research of these processes in the Spanish-speaking population is limited. However, the increasing prevalence of this diagnosis (1 in 115 children) in Mexico has implications at different levels. Educational research is an important area of interest in ASD children, such as emergent literacy. Reading and writing expand the possibilities of academic, cultural, and social information access. Taking this information into account, the objective of this research was to identify the relationship between language skills, alphabet knowledge, phonological awareness, and early reading and writing in ASD Spanish-speaking children. The method used for this research was based on tasks that were selected, adapted and in some cases designed to measure initial reading and writing, as well as language skills (naming, receptive vocabulary, and narrative skills), phonological awareness (similar phonological word pairs, beginning sound awareness and spelling) and letter knowledge, in a sample of 45 children (38 boys and 7 girls) with prior diagnosis of ASD. Descriptive analyses, as well as bivariate correlations, cluster analysis, and canonical correspondence, were obtained for the data results. Results showed that variability was large; however, it was possible to characterize the sample in low, medium, and high score groups regarding children performance. The low score group (46.7% of the sample), had a null or deficient performance in language skills and phonological awareness, some could identify up to five letters of the alphabet, showed no early reading skills but they could scribble. The middle score group was characterized by a highly variable performance in different tasks, with better language skills in receptive and naming vocabulary, some narrative, letter knowledge, and phonological awareness (beginning sound awareness) skills. The high score group, (24.4% of the sample) had the best performance in language skills in relation to the sample data, as well as in the rest of the measured skills. Finally, scores were canonically correlated between naming, receptive vocabulary, narrative, phonological awareness, letter knowledge and initial learning of reading and writing skills for the high score group and letter knowledge, naming and receptive vocabulary for the lower score group, which is consistent with previous research in typical and ASD children. In conclusion, the obtained data is consistent with previous studies. Despite large variability, it was possible to identify performance profiles and relations based on linguistic, phonological awareness, and letter knowledge skills. These skills were predictor variables of the initial development of reading and writing. The above has implications for a future program and strategies development that may benefit the acquisition of reading and writing in ASD children.

Keywords: autism, autism spectrum disorders, early literacy, emergent literacy

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Authors: Sanjeev K. Singh, Maloy B. Mandal, Revand R.

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The physiology of baroreceptors and chemoreceptors present in large blood vessels of the heart is well known in regulation of cardiorespiratory functions. Since large blood vessels and peripheral blood vessels are of same mesodermal origin, therefore, involvement of the latter in regulation of cardiorespiratory system is expected. Role of perivascular nerves in mediating cardiorespiratory alterations produced after intra-arterial injection of a nociceptive agent (bradykinin) was examined in urethane anesthetized male rats. Respiratory frequency, blood pressure, and heart rate were recorded for 30 min after the retrograde injection of bradykinin/saline in the femoral artery. In addition, paw edema was determined and water content was expressed as percentage of wet weight. Injection of bradykinin produced immediate tachypnoeic, hypotensive and bradycardiac responses of shorter latency (5-8 s) favoring the neural mechanisms involved in it. Injection of equi-volume of saline did not produce any responses and served as time matched control. Paw edema was observed in the ipsilateral hind limb. Pretreatment with diclofenac sodium significantly attenuated the bradykinin-induced responses and also blocked the paw edema. Ipsilateral femoral and sciatic nerve sectioning attenuated bradykinin-induced responses significantly indicating the origin of responses from the local vascular bed. Administration of bradykinin in the segment of an artery produced reflex cardiorespiratory changes by stimulating the perivascular nociceptors involving prostaglandins. This is a novel study exhibiting the role of peripheral blood vessels in regulation of cardiorespiratory system.

Keywords: vasosensory reflex, cardiorespiratory changes, nociceptive agent, bradykinin, VR1 receptors

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Authors: Sarah Elshahat, Tina Moffat

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Suboptimal mental health is a considerable global public health challenge that leads to considerable inequalities worldwide. Newcomers are at elevated risk for developing mental health issues as a result of social exclusion, stigmatization, racism, unequal employment opportunities, and discrimination. The problem can be especially serious amongst Arabic-speaking immigrants and refugees (ASIR) whose mental wellness may have already been affected by exposure to political violence, persecution, hunger or war in their countries of origin. A scoping review was conducted to investigate pre- and post-migration mental health determinants amongst ASIR in North America (the U.S. and Canada), who are a rapidly growing population in both regions. Pertinent peer-reviewed papers and grey literature were located through a systematic search of five electronic databases (Medline, Embase, PsycINFO, Anthropology Plus, and Sociology Database). A stakeholder consultation was implemented to validate the analyzed findings of the included 44 studies. About 80% of the studies were carried out in the US, underscoring a lack of Canadian ASIR-mental health research. A gap in qualitative, mixed-method, and longitudinal research was detected, where approximately two-thirds of the studies adopted a cross-sectional method. Pre-migration determinants of mental health were related to the political unrest, violence and armed conflict in the Arab world, increasing post-traumatic stress disorder and psychological distress levels among ASIR. English language illiteracy and generational variations in acculturation patterns were major post-migration mental health triggering factors. Exposure to domestic violence, stigmatization, poverty, racialization, and harassment were significant post-migration mental health determinants that stem from social inequalities, triggering depression, and distress amongst ASIR. Family conflicts linked to child-rearing and gendered norms were considered as both pre- and post-migration mental health triggering factors. Most post-migration mental health protective factors were socio-culturally related and included the maintenance of positive ethnic identity, faith, family support, and community cohesion. Individual resilience, articulated as self-esteem and hope, was a significant negative predictor of depression and psychological distress among ASIR. Community-engaged, mixed-methods, and longitudinal studies are required to address the current gap in mental health research among ASIR in North America. A more thorough determination of potential mental health triggers and protective factors would help inform the development of mental wellness and resilience-promoting programs that are culturally sensitive to ASIR. On the policy level, the Health in All Policies framework of the World Health Organization can be potentially useful for addressing social and health inequalities among ASIR, reducing mental health challenges.

Keywords: depression, post-traumatic stress disorder, psychological distress, resilience

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: O. S. Ebrahim, K. O. Shawky, M. O. S. Ebrahim, P. K. Jain

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Induction Motor (IM) driving pump is the main consumer of electricity in a typical fluid transportation system (FTS). It was illustrated that changing the connection of the stator windings from delta to star at no load could achieve noticeable active and reactive energy savings. This paper proposes a supervisory hysteresis liquid-level control with three-state energy saving mode (ESM) for IM in FTS including storage tank. The IM pump drive comprises modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to computer ESMs as follows: Sleeping mode in which the motor runs at no load with delta stator connection, hibernate mode in which the motor runs at no load with a star connection, and motor shutdown is the third energy saver mode. A logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction, considering the motor thermal capacity used. An artificial neural network (ANN) state estimator, based on the recurrent architecture, is constructed and learned in order to provide fault-tolerant capability for the supervisory controller. Sequential test of Wald is used for sensor fault detection. Theoretical analysis, preliminary experimental testing and, computer simulations are performed to show the effectiveness of the proposed control in terms of reliability, power quality and energy/coenergy cost reduction with the suggestion of power factor correction.

Keywords: ANN, ESM, IM, star/delta switch, supervisory control, FT, reliability, power quality

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Authors: Irene M. Arora

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Hypothesis: Attention Deficit Hyperactivity Disorder is the result of an underdeveloped prefrontal cortex which is the primary cause for the signs and symptoms seen as defining features of ADHD. Methods: Through ‘PubMed’, ‘Wiley’ and ‘Google Scholar’ studies published between 2011-2018 were evaluated, determining if a dysfunctional prefrontal cortex caused the characteristic symptoms associated with ADHD. The search terms "prefrontal cortex", "Attention-Deficit/Hyperactivity Disorder", "cognitive control", "frontostriatal tract" among others, were used to maximize the assortment of relevant studies. Excluded papers were systematic reviews, meta-analyses and publications published before 2010 to ensure clinical relevance. Results: Nine publications were analyzed in this review, all of which were non-randomized matched control studies. Three studies found a decrease in the functional integrity of the frontostriatal tract fibers in conjunction with four studies finding impaired frontal cortex stimulation. Prefrontal dysfunction, specifically medial and orbitofrontal areas, displayed abnormal functionality of reward processing in ADHD patients when compared to their normal counterparts. A total of 807 subjects were studied in this review, yielding that a little over half (54%) presented with remission of symptoms in adulthood. Conclusion: While the prefrontal cortex shows the highest consistency of impaired activity and thinner volumes in patients with ADHD, this is a heterogenous disorder implicating its pathophysiology to the dysfunction of other neural structures as well. However, remission of ADHD symptomatology in adulthood was found to be attributable to increased prefrontal functional connectivity and integration, suggesting a key role for the prefrontal cortex in the development of ADHD.

Keywords: prefrontal cortex, ADHD, inattentive, impulsivity, reward processing

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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