Search results for: risk assessment model

Authors: Elizabeth M. Seabrook, Nikki S. Rickard

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Examining time-dependent measures of emotion such as variability, instability, and inertia, provide critical and complementary insights into mental health status. Observing changes in the pattern of emotional expression over time could act as a tool to identify meaningful shifts between psychological well- and ill-being. From a practical standpoint, however, examining emotion dynamics day-to-day is likely to be burdensome and invasive. Utilizing social media data as a facet of lived experience can provide real-world, temporally specific access to emotional expression. Emotional language on social media may provide accurate and sensitive insights into individual and community mental health and well-being, particularly with focus placed on the within-person dynamics of online emotion expression. The objective of the current study was to examine the dynamics of emotional expression on the social network platform Facebook for active users and their relationship with psychological well- and ill-being. It was expected that greater positive and negative emotion variability, instability, and inertia would be associated with poorer psychological well-being and greater depression symptoms. Data were collected using a smartphone app, MoodPrism, which delivered demographic questionnaires, psychological inventories assessing depression symptoms and psychological well-being, and collected the Status Updates of consenting participants. MoodPrism also delivered an experience sampling methodology where participants completed items assessing positive affect, negative affect, and arousal, daily for a 30-day period. The number of positive and negative words in posts was extracted and automatically collated by MoodPrism. The relative proportion of positive and negative words from the total words written in posts was then calculated. Preliminary analyses have been conducted with the data of 9 participants. While these analyses are underpowered due to sample size, they have revealed trends that greater variability in the emotion valence expressed in posts is positively associated with greater depression symptoms (r(9) = .56, p = .12), as is greater instability in emotion valence (r(9) = .58, p = .099). Full data analysis utilizing time-series techniques to explore the Facebook data set will be presented at the conference. Identifying the features of emotion dynamics (variability, instability, inertia) that are relevant to mental health in social media emotional expression is a fundamental step in creating automated screening tools for mental health that are temporally sensitive, unobtrusive, and accurate. The current findings show how monitoring basic social network characteristics over time can provide greater depth in predicting risk and changes in depression and positive well-being.

Keywords: emotion, experience sampling methods, mental health, social media

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Authors: Zilin Zhang

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Agent-based simulation has become a popular method of exploring the behavior of all kinds of urban systems. The city clearly is viewed as such a system. Many urban evolution processes, such as the development or the transaction of a piece of land, can be modeled with a set of rules. Such modeling approaches can be used to gain insight into urban-development and land market transactions in the real world. Our work contributes to such type of research by modeling the transactions of lands in a city and its surrounding suburbs. By replicating the demand and supply needs in the land market, we are able to demonstrate the different transaction patterns in three types of residential areas - downtown, city-suburban, and further suburban areas. In addition, we are also able to compare the vital roles of different activation conditions play in generating the various transaction patterns of the land market at the macro level. We use this simulation to loosely test our hypotheses about the nature of activation regimes by the replication of the Zi traders’ model. In the end, we hope our analytical results can be useful for city planners and policymakers to develop rational city plans and policies for shaping sustainable urban development.

Keywords: simulation, agent-based modeling, housing market, city

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Authors: Mohammad Sahadet Hossain, Shamsil Arifeen, Mehrab Hossian Likhon

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In this paper, we analyze a linear time invariant (LTI) descriptor system of large dimension. Since these systems are difficult to simulate, compute and store, we attempt to reduce this large system using Low Rank Cholesky Factorized Alternating Directions Implicit (LRCF-ADI) iteration followed by Square Root Balanced Truncation. LRCF-ADI solves the dual Lyapunov equations of the large system and gives low-rank Cholesky factors of the gramians as the solution. Using these cholesky factors, we compute the Hankel singular values via singular value decomposition. Later, implementing square root balanced truncation, the reduced system is obtained. The bode plots of original and lower order systems are used to show that the magnitude and phase responses are same for both the systems.

Keywords: low-rank cholesky factor alternating directions implicit iteration, LTI Descriptor system, Lyapunov equations, Square-root balanced truncation

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Alan N. A. Heberle, Salatiel W. Da Silva, Valentin Perez-Herranz, Andrea M. Bernardes

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Contaminants of emerging concern are substances widely used, such as pharmaceutical products. These compounds represent risk for both wild and human life since they are not completely removed from wastewater by conventional wastewater treatment plants. In the environment, they can be harm even in low concentration (µ or ng/L), causing bacterial resistance, endocrine disruption, cancer, among other harmful effects. One of the most common taken medicine to treat cardiocirculatory diseases is the Atenolol (ATL), a β-Blocker, which is toxic to aquatic life. In this way, it is necessary to implement a methodology, which is capable to promote the degradation of the ATL, to avoid the environmental detriment. A very promising technology is the advanced electrochemical oxidation (AEO), which mechanisms are based on the electrogeneration of reactive radicals (mediated oxidation) and/or on the direct substance discharge by electron transfer from contaminant to electrode surface (direct oxidation). The hydroxyl (HO•) and sulfate (SO₄•⁻) radicals can be generated, depending on the reactional medium. Besides that, at some condition, the peroxydisulfate (S₂O₈²⁻) ion is also generated from the SO₄• reaction in pairs. Both radicals, ion, and the direct contaminant discharge can break down the molecule, resulting in the degradation and/or mineralization. However, ATL molecule and byproducts can still remain in the treated solution. On this wise, some efforts can be done to implement the AEO process, being one of them the use of a cationic membrane to separate the cathodic (reduction) from the anodic (oxidation) reactor compartment. The aim of this study is investigate the influence of the implementation of a cationic membrane (Nafion®-117) to separate both cathodic and anodic, AEO reactor compartments. The studied reactor was a filter-press, with bath recirculation mode, flow 60 L/h. The anode was an Nb/BDD2500 and the cathode a stainless steel, both bidimensional, geometric surface area 100 cm². The solution feeding the anodic compartment was prepared with ATL 100 mg/L using Na₂SO₄ 4 g/L as support electrolyte. In the cathodic compartment, it was used a solution containing Na₂SO₄ 71 g/L. Between both solutions was placed the membrane. The applied currents densities (iₐₚₚ) of 5, 20 and 40 mA/cm² were studied over 240 minutes treatment time. Besides that, the ATL decay was analyzed by ultraviolet spectroscopy (UV/Vis). The mineralization was determined performing total organic carbon (TOC) in TOC-L CPH Shimadzu. In the cases without membrane, the iₐₚₚ 5, 20 and 40 mA/cm² resulted in 55, 87 and 98 % ATL degradation at the end of treatment time, respectively. However, with membrane, the degradation, for the same iₐₚₚ, was 90, 100 and 100 %, spending 240, 120, 40 min for the maximum degradation, respectively. The mineralization, without membrane, for the same studied iₐₚₚ, was 40, 55 and 72 %, respectively at 240 min, but with membrane, all tested iₐₚₚ reached 80 % of mineralization, differing only in the time spent, 240, 150 and 120 min, for the maximum mineralization, respectively. The membrane increased the ATL oxidation, probably due to avoid oxidant ions (S₂O₈²⁻) reduction on the cathode surface.

Keywords: contaminants of emerging concern, advanced electrochemical oxidation, atenolol, cationic membrane, double compartment reactor

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Authors: Dejene Gizaw Kidane

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This study examines the impact of foreign direct investment on economic growth of Ethiopia using yearly time-series data for 1974 through 2013. Economic growth is proxies by real per capita gross domestic product and foreign direct investment proxies by the inflow of foreign direct investment. Other control variables such as gross domestic saving, trade, government consumption and inflation has been incorporated. In order to fully account for feedbacks, a vector autoregressive model is utilized. The results show that there is a stable, long-run relationship between foreign direct investment and economic growth. The variance decomposition results show that the main sources of Ethiopia economic growth variations are due largely own shocks. The pairwise Granger causality results show that there is a unidirectional causality that runs from FDI to economic growth of Ethiopia. Hence, the researcher therefore recommends that, FDI facilitate economic growth, so the government has to exert much effort in order to attract more FDI into the country.

Keywords: real per capita GDP, FDI, co-integration, VECM, Granger causality

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

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Authors: Shengjun Yan, Xuan Wang

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Water level changes in a shallow lake always introduce dramatic land pattern changes. To achieve sustainable ecosystem service, it is necessary to evaluate habitat quality dynamic and its spatio-temporal variation resulted from water level changes, which can provide a scientific basis for protection of biodiversity and planning of wetland ecological system. Landsat data in the spring was chosen to obtain landscape data at different times based on the high, moderate and low water level of Baiyangdian Shallow Lake. We used the InVEST to evaluate the habitat quality, habitat degradation, and habitat scarcity. The result showed that: 1) the water level of shallow lake changes from high to low lead to an obvious landscape pattern changes and habitat degradation, 2) the most change area occurred in northwestward and southwest of Baiyangdian Shallow Lake, which there was a 21 percent of suitable habitat and 42 percent of moderately suitable habitat lost. Our findings show that the changes of water level in the shallow lake would have a strong relationship with the habitat quality.

Keywords: habitat quality, habitat degradation, water level changes, shallow lake

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Authors: Anshu Gupta, Charu Gupta

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Background: Adolescence is the transitional period between puberty and adulthood. It is marked by immense turmoil in emotional and behavioral spheres. Adolescents are at risk of an array of behavioral and emotional problems, resulting in social, academic and vocational function impairments. Conflicts in the family and inability of the parents to cope with the changing demands of an adolescent have a negative impact on the overall development of the child. This augers ill for the individual’s future, resulting in depression, delinquency and suicides among other problems. Aim: The aim of the study was to compare the prevalence of behavioral and emotional problems in school going adolescents aged 13 to 15 years residing in Ludhiana city. Method: A total of 1380 school children in the age group of 13 to 15 years were assessed by the adolescent health screening questionnaire (FAPS) and Youth Self-Report (2001) questionnaire. Statistical significance was ascertained by t-test, chi-square test (x²) and ANOVA, as appropriate. Results: A considerably high prevalence of behavioral and emotional problems was found in school going adolescents (26.5%), more in girls (31.7%) than in boys (24.4%). In case of boys, the maximum problem was in the 13 year age group, i.e., 28.2%, followed by a significant decline by the age of 14 years, i.e., 24.2% and 15 years, i.e., 19.6%. In case of girls also, the maximum problem was in the 13 year age group, i.e., 32.4% followed by a marginal decline in the 14 years i.e., 31.8% and 15 year age group, i.e., 30.2%. Demographic factors were non contributory. Internalizing syndrome (22.4%) was the most common problem followed by the neither internalizing nor externalizing (17.6%) group. In internalizing group, most (26.5%) of the students were observed to be anxious/ depressed. Social problem was observed to be the most frequent (10.6%) among neither internalizing nor externalizing group. Aggressive behavior was the commonest (8.4%) among externalizing group. Internalizing problems, mainly anxiety and depression, were commoner in females (30.6%) than males (24.6%). More boys (16%) than girls (13.4%) were reported to suffer from externalizing disorders. A critical review of the data showed that most of the adolescents had poor knowledge about reproductive health. Almost 36% reported that the source of their information on sexual and reproductive health being friends and the electronic media. There was a high percentage of adolescents who reported being worried about sexual abuse (20.2%) with majority of them being girls (93.6%) reflecting poorly on the social setup in the country. About 41% of adolescents reported being concerned about body weight and most of them being girls (92.4%). Up to 14.5% reported having thoughts of using alcohol or drugs perhaps due to the easy availability of substances of abuse in this part of the country. 12.8% (mostly girls) reported suicidal thoughts. Summary/conclusion: There is a high prevalence of emotional and behavioral problems among school-going adolescents. Resolution of these problems during adolescence is essential for attaining a healthy adulthood. The need of the hour is to spread awareness among caregivers and formulation of effective management strategies including school mental health programme.

Keywords: adolescence, behavioral, emotional, internalizing problem

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Authors: Mizam Doğan

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Adapazarı railway car factory, the only railway car factory of Turkey, was constructed in 1950. It was a steel design and it had filled beam sections and truss beam systems. Columns were steel profiles and box sections. The factory was damaged heavily on Izmit Earthquake and closed. In this earthquake 90% of damaged structures are reinforced concrete, the others are %7 prefabricated and 3% steel construction. As can be seen in statistical data, damaged industrial buildings in this earthquake were generally reinforced concrete and prefabricated structures. Adapazari railway car factory is the greatest steel structure damaged in the earthquake. This factory has 95% of the total damaged steel structure area. In this paper; earthquake damages on beams and columns of the factory are studied by considering TS648 'Turkish Standard Building Code for Steel Structures' and also damaged connection elements as welds, rivets and bolts are examined. A model similar to the damaged system is made and high-stress zones are searched. These examinations, conclusions, suggestions are explained by damage photos and details.

Keywords: column-beam connection, seismic analysis, seismic load, steel structure

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Authors: Nur Aisyah Jalalludin, Arwindra Rizqiawan, Goro Fujita

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In this study, we aim to demonstrate a microgrid system experimental simulation for an easy understanding of a large-scale microgrid system. This model is required for industrial training and learning environments. However, in order to create an exact representation of a microgrid system, the laboratory-scale system must fulfill the requirements of a grid-connected inverter, in which power values are assigned to the system to cope with the intermittent output from renewable energy sources. Aside from that, during changes in load capacity, the grid-connected system must be able to supply power from the utility grid side and microgrid side in a balanced manner. Therefore, droop control is installed in the inverter’s control board to maintain equal power sharing in both sides. This power control in a stand-alone condition and droop control in a grid-connected condition must be implemented in order to maintain a stabilized system. Based on the experimental results, power control and droop control can both be applied in the system by comparing the experimental and reference values.

Keywords: droop control, droop characteristic, grid-connected inverter, microgrid, power control

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Authors: Getaneh Berie Tarekegn

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Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. With reconfigurable intelligent surfaces (RIS), this problem can be effectively addressed. To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communication system. According to simulation results, LSTM can improve the channel estimation performance of RIS-assisted UAV-enabled wireless communication.

Keywords: channel estimation, reconfigurable intelligent surfaces, long short-term memory, unmanned aerial vehicles

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Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: Abderrahmane Mousstain, Ez-Zohra Belkadi

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Many authors support more participatory and inclusive place branding practices that empower stakeholders’ participation. According to this participatory point of view, the effectiveness of place branding strategies cannot be achieved without citizen involvement. However, the role of all residents as key participants in the city branding process has not been widely discussed. The aim of this paper was to determine how citizens’ involvement impacts their perceptions of the city's image, using a multivariate model. To test our hypotheses hypothetical-deductive reasoning by the quantitative method was chosen. Our investigation is based on data collected through a survey among 200 citizens of Casablanca. Results show that the more citizens are involved, the more they tend to evaluate the image of the brand positively. Additionally, the degree of involvement seems to impact satisfaction and a sense of belonging. As well, the more citizen develops a sense of belonging to the city, the more favorable his or her perception of the brand image is. Ultimately, the role of citizens shouldn’t be limited to reception. They are also Co-creators of the brand, who ensure the correlation of the brand with authentic place roots.

Keywords: citybranding, sense of belonging, satisfaction, impact, brand’s image

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Authors: Jai Myung Yang, Na Young Kim, Sung Ho Shin

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The adverse reactions of current live smallpox vaccines and potential use of smallpox as a bioterror weapon have heightened the development of new effective vaccine for this infectious disease. In the present study, DNA vaccine vector was produced which was optimized for expression of the vaccinia virus L1 antigen in the mouse model. A plasmid IgM-tL1R, which contains codon-optimized L1R gene, was constructed and fused with an IgM signal sequence under the regulation of a SV40 enhancer. The expression and secretion of recombinant L1 protein was confirmed in vitro 293 T cell. Mice were administered the DNA vaccine by electroporation and challenged with vaccinia virus. We observed that immunization with IgM-tL1R induced potent neutralizing antibody responses and provided complete protection against lethal vaccinia virus challenge. Isotyping studies reveal that immunoglobulin G2 (IgG2) antibody predominated after the immunization, indicative of a T helper type 1 response. Our results suggest that an optimized DNA vaccine, IgM-tL1R, can be effective in stimulating anti-vaccinia virus immune response and provide protection against lethal orthopoxvirus challenge.

Keywords: DNA vaccine, electroporation, L1R, vaccinia virus

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Utkan Caliskan

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Two types of numerical codes based on finite volume method are developed in order to solve compressible Euler equations to simulate the flow through forward facing step channel. Both algorithms have AUSM+- up (Advection Upstream Splitting Method) scheme for flux splitting and two-stage Runge-Kutta scheme for time stepping. In this study, the flux calculations differentiate between the algorithm based on OpenFOAM mesh format which is called 'face-based' algorithm and the basic algorithm which is called 'element-based' algorithm. The face-based algorithm avoids redundant flux computations and also is more flexible with hybrid grids. Moreover, some of OpenFOAM’s preprocessing utilities can be used on the mesh. Parallelization of the face based algorithm for which atomic operations are needed due to the shared memory model, is also presented. For several mesh sizes, 2.13x speed up is obtained with face-based approach over the element-based approach.

Keywords: cell centered finite volume method, compressible Euler equations, OpenFOAM mesh format, OpenMP

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Authors: Lena Lapidot, Amir Amnon, Rita Nosenko, Veitsman Ella, Cohen-Ezra Oranit, Davidov Yana, Segev Shlomo, Koren Omry, Safran Michal, Ben-Ari Ziv

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Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer related mortality worldwide. Liver Cirrhosis is the main predisposing risk factor for the development of HCC. The factor(s) influencing disease progression from Cirrhosis to HCC remain unknown. Gut microbiota has recently emerged as a major player in different liver diseases, however its association with HCC is still a mystery. Moreover, there might be an important association between the gut microbiota, nutrition, life style and the progression of Cirrhosis and HCC. The aim of our study was to characterize the gut microbial signature in association with life style and nutrition of patients with Cirrhosis, HCC-Cirrhosis and healthy controls. Design: Stool samples were collected from 95 individuals (30 patients with HCC, 38 patients with Cirrhosis and 27 age, gender and BMI-matched healthy volunteers). All participants answered lifestyle and Food Frequency Questionnaires. 16S rRNA sequencing of fecal DNA was performed (MiSeq Illumina). Results: There was a significant decrease in alpha diversity in patients with Cirrhosis (qvalue=0.033) and in patients with HCC-Cirrhosis (qvalue=0.032) compared to healthy controls. The microbiota of patients with HCC-cirrhosis compared to patients with Cirrhosis, was characterized by a significant overrepresentation of Clostridium (pvalue=0.024) and CF231 (pvalue=0.010) and lower expression of Alphaproteobacteria (pvalue=0.039) and Verrucomicrobia (pvalue=0.036) in several taxonomic levels: Verrucomicrobiae, Verrucomicrobiales, Verrucomicrobiaceae and the genus Akkermansia (pvalue=0.039). Furthermore, we performed an analysis of predicted metabolic pathways (Kegg level 2) that resulted in a significant decrease in the diversity of metabolic pathways in patients with HCC-Cirrhosis (qvalue=0.015) compared to controls, one of which was amino acid metabolism. Furthermore, investigating the life style and nutrition habits of patients with HCC-Cirrhosis, we found significant correlations between intake of artificial sweeteners and Verrucomicrobia (qvalue=0.12), High sugar intake and Synergistetes (qvalue=0.021) and High BMI and the pathogen Campylobacter (qvalue=0.066). Furthermore, overweight in patients with HCC-Cirrhosis modified bacterial diversity (qvalue=0.023) and composition (qvalue=0.033). Conclusions: To the best of the our knowledge, we present the first report of the gut microbial composition in patients with HCC-Cirrhosis, compared with Cirrhotic patients and healthy controls. We have demonstrated in our study that there are significant differences in the gut microbiome of patients with HCC-cirrhosis compared to Cirrhotic patients and healthy controls. Our findings are even more pronounced because the significantly increased bacteria Clostridium and CF231 in HCC-Cirrhosis weren't influenced by diet and lifestyle, implying this change is due to the development of HCC. Further studies are needed to confirm these findings and assess causality.

Keywords: Cirrhosis, Hepatocellular carcinoma, life style, liver disease, microbiome, nutrition

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Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

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This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

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Authors: V. Vicente E. Mujica, Gustavo Gonzalez

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The global coverage of broadband multimedia and internet-based services in terrestrial-satellite networks demand particular interests for satellite providers in order to enhance services with low latencies and high signal quality to diverse users. In particular, the delay of on-board processing is an inherent source of latency in a satellite communication that sometimes is discarded for the end-to-end delay of the satellite link. The frame work for this paper includes modelling of an on-orbit satellite payload using an agent model that can reproduce the properties of processing delays. In essence, a comparison of different spatial interpolation methods is carried out to evaluate physical data obtained by an GEO satellite in order to define a discretization function for determining that delay. Furthermore, the performance of the proposed agent and the development of a delay discretization function are together validated by simulating an hybrid satellite and terrestrial network. Simulation results show high accuracy according to the characteristics of initial data points of processing delay for Ku bands.

Keywords: terrestrial-satellite networks, latency, on-orbit satellite payload, simulation

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Authors: Olusola Bamisile, Ferdinard Dika, Mustafa Dagbasi, Serkan Abbasoglu

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The aim of this study was to model an air conditioning system that brings about effective cooling and reduce fossil fuel consumption with solar energy as an alternative source of energy. The objective of the study is to design a system with high COP, low usage of electricity and to integrate solar energy into AC systems. A hybrid solar assisted air conditioning system is designed to produce 30kW cooling capacity and R744 (CO₂) is used as a refrigerant. The effect of discharge pressure on the performance of the system is studied. The subcool temperature, evaporating temperature (5°C) and suction gas return temperature (12°C) are kept constant for the four different discharge pressures considered. The cooling gas temperature is set at 25°C, and the discharge pressure includes 80, 85, 90 and 95 bars. Copeland Scroll software is used for the simulation. A pressure-enthalpy graph is also used to deduce each enthalpy point while numerical methods were used in making other calculations. From the result of the study, it is observed that a higher COP is achieved with the use of solar assisted systems. As much as 46% of electricity requirements will be save using solar input at compressor stage.

Keywords: air conditioning, solar energy, performance, energy saving

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Authors: Lizbeth Puerta, Enselmina Marín

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The purpose of this research is to present a survey to be applied to professors of public universities, to identify the factors that benefit or hinder the university-industry relation. Hence, this research studies some elements that integrate the variables: Knowledge management, technology management, and technology transfer; to define the existence of a relation between these variables and the industry necessities of innovation. This study is exploratory, descriptive and non-experimental. The research question is: What is the impact of the knowledge management, the technology management, and the technology transfer, made by administrative support areas of the public universities, in the industries innovation? Thus, literature review was made to identify some elements that should be considered to design a survey that allows to obtain valid information to the study variables. After this, the survey was developed, and the Content Validity Analysis was made through the Lawshe Model. The analysis indicated that the Content Validity Index (CVI) was 0.80. Hence, it was determined that this survey presents acceptable psychometric properties to be used as an evaluation tool.

Keywords: innovation, knowledge management, technology management, technology transfer

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Shashwat Mookherjee, Shruti Dutta

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This study explores linguistic distinctions among American, Indian, and Irish English dialects and assesses various Language Models (LLMs) in their ability to generate British English translations from these dialects. Using cosine similarity analysis, the study measures the linguistic proximity between original British English translations and those produced by LLMs for each dialect. The findings reveal that Indian and Irish English translations maintain notably high similarity scores, suggesting strong linguistic alignment with British English. In contrast, American English exhibits slightly lower similarity, reflecting its distinct linguistic traits. Additionally, the choice of LLM significantly impacts translation quality, with Llama-2-70b consistently demonstrating superior performance. The study underscores the importance of selecting the right model for dialect translation, emphasizing the role of linguistic expertise and contextual understanding in achieving accurate translations.

Keywords: cross-dialect translation, language models, linguistic similarity, multilingual NLP

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Authors: Thiago E. Goto, Carla C. Lopes, Helena B. Nader, Anielle C. A. Silva, Noelio O. Dantas, José R. Siqueira Jr., Luciano Caseli

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Quantum dots (QD) are semiconductor nanocrystals that can be employed in biological research as a tool for fluorescence imagings, having the potential to expand in vivo and in vitro analysis as cancerous cell biomarkers. Particularly, cadmium selenide (CdSe) magic-sized quantum dots (MSQDs) exhibit stable luminescence that is feasible for biological applications, especially for imaging of tumor cells. For these facts, it is interesting to know the mechanisms of action of how such QDs mark biological cells. For that, simplified models are a suitable strategy. Among these models, Langmuir films of lipids formed at the air-water interface seem to be adequate since they can mimic half a membrane. They are monomolecular films formed at liquid-gas interfaces that can spontaneously form when organic solutions of amphiphilic compounds are spread on the liquid-gas interface. After solvent evaporation, the monomolecular film is formed, and a variety of techniques, including tensiometric, spectroscopic and optic can be applied. When the monolayer is formed by membrane lipids at the air-water interface, a model for half a membrane can be inferred where the aqueous subphase serve as a model for external or internal compartment of the cell. These films can be transferred to solid supports forming the so-called Langmuir-Blodgett (LB) films, and an ampler variety of techniques can be additionally used to characterize the film, allowing for the formation of devices and sensors. With these ideas in mind, the objective of this work was to investigate the specific interactions of CdSe MSQDs with tumorigenic and non-tumorigenic cells using Langmuir monolayers and LB films of lipids and specific cell extracts as membrane models for diagnosis of cancerous cells. Surface pressure-area isotherms and polarization modulation reflection-absorption spectroscopy (PM-IRRAS) showed an intrinsic interaction between the quantum dots, inserted in the aqueous subphase, and Langmuir monolayers, constructed either of selected lipids or of non-tumorigenic and tumorigenic cells extracts. The quantum dots expanded the monolayers and changed the PM-IRRAS spectra for the lipid monolayers. The mixed films were then compressed to high surface pressures and transferred from the floating monolayer to solid supports by using the LB technique. Images of the films were then obtained with atomic force microscopy (AFM) and confocal microscopy, which provided information about the morphology of the films. Similarities and differences between films with different composition representing cell membranes, with or without CdSe MSQDs, was analyzed. The results indicated that the interaction of quantum dots with the bioinspired films is modulated by the lipid composition. The properties of the normal cell monolayer were not significantly altered, whereas for the tumorigenic cell monolayer models, the films presented significant alteration. The images therefore exhibited a stronger effect of CdSe MSQDs on the models representing cancerous cells. As important implication of these findings, one may envisage for new bioinspired surfaces based on molecular recognition for biomedical applications.

Keywords: biomembrane, langmuir monolayers, quantum dots, surfaces

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Authors: Farah Islam, Md. Monirul Islam, Mosammat Salma Akter, Goutam Kumar Kundu

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The continuous variability of climate and the risk associated with it have a significant impact on the fisheries leading to a global concern for about half a billion fishery-based livelihoods. Though in the context of Bangladesh mounting evidence on the impacts of climate change on fishery-based livelihoods or their socioeconomic conditions are present, the country’s inland fisheries sector remains in a negligible corner as compared to the coastal areas which are spotted on the highlight due to its higher vulnerability to climatic hazards. The available research on inland fisheries, particularly river fisheries, has focussed mainly on fish production, pollution, fishing gear, fish biodiversity and livelihoods of the fishers. This study assesses the impacts of climate variability and changes on the Jamuna (a transboundary river called Brahmaputra in India) River fishing communities and their coping and adaptation strategies. This study has used primary data collected from Kalitola Ghat and Debdanga fishing communities of the Jamuna River during May, August and December 2015 using semi-structured interviews, oral history interviews, key informant interviews, focus group discussions and impact matrix as well as secondary data. This study has found that both communities are exposed to storms, floods and land erosions which impact on fishery-based livelihood assets, strategies, and outcomes. The impact matrix shows that human and physical capitals are more affected by climate hazards which in turn affect financial capital. Both communities have been responding to these exposures through multiple coping and adaptation strategies. The coping strategies include making dam with soil, putting jute sac on the yard, taking shelter on boat or embankment, making raised platform or ‘Kheua’ and involving with temporary jobs. While, adaptation strategies include permanent migration, change of livelihood activities and strategies, changing fishing practices and making robust houses. The study shows that migration is the most common adaptation strategy for the fishers which resulted in mostly positive outcomes for the migrants. However, this migration has impacted negatively on the livelihoods of existing fishers in the communities. In sum, the Jamuna river fishing communities have been impacted by several climatic hazards and they have traditionally coped with or adapted to the impacts which are not sufficient to maintain sustainable livelihoods and fisheries. In coming decades, this situation may become worse as predicted by latest scientific research and an enhanced level of response would be needed.

Keywords: climatic hazards, impacts and adaptation, fisherfolk, the Jamuna River

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Authors: A. Q. Ahmed, S. Gao

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In this paper, the energy saving and human thermal comfort in a typical office room are investigated. The impact of a combined system of exhaust inlet air with light slots located at the ceiling level in a room served by displacement ventilation system is numerically modelled. Previous experimental data are used to validate the computational fluid dynamic (CFD) model. A case study of simulated office room includes two seating occupants, two computers, two data loggers and four lamps. The combined system is located at the ceiling level above the heat sources. A new method of calculation for the cooling coil load in stratified air distribution (STRAD) system is used in this study. The results show that 47.4 % energy saving of space cooling load can be achieved by combing the exhaust inlet air with light slots at the ceiling level above the heat sources.

Keywords: air conditioning, displacement ventilation, energy saving, thermal comfort

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Authors: Francesco Rizzuto, Matthew Stickland, Stephan Hannot

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The simulation of a positive displacement pump system with commercial software for Computer Fluid Dynamics (CFD), will result in an enormous computational effort due to the complexity of the pump system. This drawback restricts the use of it to a specific part of the pump in one simulation. This research focuses on developing an algorithm that provides a suitable result in agreement with experiment data, without that computational effort. The compressible equations are solved with an explicit algorithm. A comparison is presented between the FV method with Monotonic Upwind scheme for Conservative Laws (MUSCL) with slope limiter and experimental results. The source term for cavitation and friction is introduced into the algorithm with a slipping strategy and solved with a 4th order Runge-Kutta scheme (RK4). Different pumps are modeled and analyzed to evaluate the flexibility of the code. The simulation required minimal computation time and resources without compromising the accuracy of the simulation results. Therefore, this algorithm highlights the feasibility of pressure pulsation simulation as a design tool for an industrial purpose.

Keywords: cavitation, diaphragm, DVCM, finite volume, MUSCL, positive displacement pump

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Authors: Fatiha Teklal, Bachir Kacimi, Arezki Djebbar

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The study and application of composite materials are a truly interdisciplinary endeavor that has been enriched by contributions from chemistry, physics, materials science, mechanics and manufacturing engineering. The understanding of the interface (or interphase) in composites is the central point of this interdisciplinary effort. From the early development of composite materials of various nature, the optimization of the interface has been of major importance. Even more important, the ideas linking the properties of composites to the interface structure are still emerging. In our study, we need a direct characterization of the interface; the micromechanical tests we are addressing seem to meet this objective and we chose to use two complementary tests simultaneously. The microindentation test that can be applied to real composites and the drop test, preferred to the pull-out because of the theoretical possibility of studying systems with high adhesion (which is a priori the case with our systems). These two tests are complementary because of the principle of the model specimen used for both the first "compression indentation" and the second whose fiber is subjected to tensile stress called the drop test. Comparing the results obtained by the two methods can therefore be rewarding.

Keywords: Fiber, Interface, Matrix, Micromechanics, Pull-out

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Authors: Peng Liu, Maria Justo Alonso, Hans Martin Mathisen

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A theoretical model is developed to evaluate the performance and energy saving potential of an air handling unit integrated with a membrane energy exchanger in cold climates. The recovered sensible and latent heat, fan preheating use for frost prevention and heating energy consumed by heating coil after the ventilator is compared for the air handling unit combined heat and energy exchanger respectively. A concept of coefficient of performance of air handling unit is presented and applied to assess the energy use of air handling unit (AHU) in cold climates. The analytic results indicate downsizing of the preheating coil before exchanger and heating coils after exchanger are expected since the required power to preheat and condition the air is reduced compared to heat exchanger when the MEE is integrated with AHU. Simultaneously, a superior ratio of energy recovered (RER) is obtained from AHU build-in a counter-flow MEE. The AHU with sensible-only heat exchanger has noticeably low RER, around 1 at low outdoor air temperature where the maximum energy rate is desired to condition the severe cold and dry air.

Keywords: membrane energy exchanger, cold climate, energy efficient building, HVAC

Procedia PDF Downloads 314