Search results for: cognitive models

Authors: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou

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In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks

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Authors: Khayra Zerrouki, Noureddine Djebli, Esra Eroglu, Afife Mat, Ozhan Gul

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Neurodegenerative diseases of the human brain comprise a variety of disorders that affect an increasing percentage of the population. Alzheimer’s disease (AD) is a complex, multifactorial, heterogeneous mental illness, which is characterized by an age-dependent loss of memory and an impairment of multiple cognitive functions, but this 10 last years it concerns the population most and most young. Hypericum perforatum has traditionally been used as an external anti-inflammatory and healing remedy for the treatment of swellings, wounds and burns, diseases of the alimentary tract and psychological disorders. It is currently of great interest due to new and important therapeutic applications. In this study, the chemical composition of methanolic extract of Hypericum perforatum (HPM) was analysed by using high performance liquid chromatography – diode array detector (HPLC-DAD). The in vitro antioxidant activity of HPM was evaluated by using several antioxidant tests. HSM exhibits inhibitory capacity against posphatidylcholine liposome peroxidation, induced with iron and ascorbic acid, scavenge DPPH and superoxide radicals and act as reductants. The cytotoxic activity of HSM was also determined by using MTT cell viability assay on HeLa and NRK-52E cell lines. The in vivo activity studies in Swiss mice were determined by using behavioral, memory tests and histological study. According to tests results HPM that may be relevant to the treatment of cognitive disorders. The results of chemical analysis showed a hight level of hyperforin and quercitin that had an important antioxidant activity proved in vitro with the DPPH, anti LPO and SOD; this antioxidant activity was confirmed in vivo after the non-toxic results by means of improvement in behavioral and memory than the reducing shrunken in pyramidal cells of mice brains.

Keywords: AlCl3, alzheimer, mice, neuroprotective, neurotoxicity, phytotherapy

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Authors: Seyed-Ali Sadegh-Zadeh, Mahboobe Bahrami, Sahar Ahmadi, Seyed-Yaser Mousavi, Hamed Atashbar, Amir M. Hajiyavand

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This paper presents an investigation into the concept of neural reshaping, which is crucial for achieving strong artificial intelligence through the development of AI algorithms with very high plasticity. By examining the plasticity of both human and artificial neural networks, the study uncovers groundbreaking insights into how these systems adapt to new experiences and situations, ultimately highlighting the potential for creating advanced AI systems that closely mimic human intelligence. The uniqueness of this paper lies in its comprehensive analysis of the neural reshaping process in both human and artificial intelligence systems. This comparative approach enables a deeper understanding of the fundamental principles of neural plasticity, thus shedding light on the limitations and untapped potential of both human and AI learning capabilities. By emphasizing the importance of neural reshaping in the quest for strong AI, the study underscores the need for developing AI algorithms with exceptional adaptability and plasticity. The paper's findings have significant implications for the future of AI research and development. By identifying the core principles of neural reshaping, this research can guide the design of next-generation AI technologies that can enhance human and artificial intelligence alike. These advancements will be instrumental in creating a new era of AI systems with unparalleled capabilities, paving the way for improved decision-making, problem-solving, and overall cognitive performance. In conclusion, this paper makes a substantial contribution by investigating the concept of neural reshaping and its importance for achieving strong AI. Through its in-depth exploration of neural plasticity in both human and artificial neural networks, the study unveils vital insights that can inform the development of innovative AI technologies with high adaptability and potential for enhancing human and AI capabilities alike.

Keywords: neural plasticity, brain adaptation, artificial intelligence, learning, cognitive reshaping

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Authors: Lloyd B. Ranises

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The Philippine labor force has been affected by the pandemic recently. The situation was added by the high inflation rate, which makes matter worse. Since the Philippines has a new government after the 2022 national election, the labor condition under the previous government has been passed on to the new one. To understand the labor challenges the present government faces, this study revisits the labor conditions and responses of the previous government from 2016 to 2022. Thus, this study reviews the labor force of the Philippines within the time frame. It explores the challenges in the labor market and examines government policy. This study uses secondary sources in tracing the labor conditions and government actions that addressed them. The Literatures are consolidated to see its relevance to the new government’s labor policy. This study found that the labor force had a sluggish growth earlier until 2018 and thrived on but was affected by the pandemic. By 2020, the National Capital Region’s labor force dropped, although, after which, it begins to thrive again, showing recovery. However, its composition is much more complex. Cognitive skill is high in demand that requires tertiary education. But the production of goods and services is low in the scientific workforce in addition to the mismatch between position and profession. Moreover, Philippine labor has poor female participation. In addition to these complexities, the agricultural rural areas have high underemployment, which implies surplus labor of low skill. Overseas employment, on the other, is significant to the decrease in domestic production. The major responses of the previous government, by far, have been focused on the minimum wage increase and the social services and health insurance, which are appropriate to the post-pandemic needs. Yet still, some issues are unattended. This study concludes that the previous government’s policy needs to be fleshed out substantially. It necessitates that the new administration shall consider encompassing all aspects of the Philippine labor force to sustain and strengthen the economy of the country.

Keywords: cognitive skills, minimum wage, national capital region, underemployment

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Authors: Akriti Chandra, Gourav Dwivedi, Seema Sharma, Shivani

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This study investigates the adoption of green business (GB) models within a circular economy framework (CEBM) for Micro Small and Medium Enterprise (MSME), given the rising importance of sustainable practices. The research begins by exploring the shift from linear business models towards resource-efficient, sustainable models, emphasizing the benefits of the circular economy. The study's literature review identifies 60 influential factors impacting the shift to green businesses, grouped as internal and external drivers. However, there is a research gap in examining these factors' interrelationships and operationalizing them within MSMEs. To address this gap, the study employs Total Interpretive Structural Modelling (TISM) to establish a hierarchical structure of factors influencing GB and circular economy business model (CEBM) adoption. Findings reveal that factors like green innovation and market competitiveness are particularly impactful. Using Systems Theory, which views organizations as complex adaptive systems, the study contextualizes these drivers within MSMEs, proposing a framework for a sustainable business model adoption. The study concludes with significant implications for policymakers, suggesting that the identified factors and their hierarchical relationships can guide policy formulation for a broader transition to green business practices. This work also invites further research, recommending larger, quantitative studies to empirically validate these factors and explore practical challenges in implementing CEBMs.

Keywords: green business (GB), circular economy business model (CEBM), micro small and medium enterprise (MSME), total interpretive structural modelling (TISM), systems theory

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Authors: Farid Gaci, Zoubir Nemouchi

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A numerical study of laminar and turbulent fluid flows in 90° bend of square section was carried out. Three-dimensional meshes, based on hexahedral cells, were generated. The QUICK scheme was employed to discretize the convective term in the transport equations. The SIMPLE algorithm was adopted to treat the velocity-pressure coupling. The flow structure obtained showed interesting features such as recirculation zones and counter-rotating pairs of vortices. The performance of three different turbulence models was evaluated: the standard k- ω model, the SST k-ω model and the Reynolds Stress Model (RSM). Overall, it was found that, the multi-equation model performed better than the two equation models. In fact, the existence of four pairs of counter rotating cells, in the straight duct upstream of the bend, were predicted by the RSM closure but not by the standard eddy viscosity model nor the SST k-ω model. The analysis of the results led to a better understanding of the induced three dimensional secondary flows and the behavior of the local pressure coefficient and the friction coefficient.

Keywords: curved duct, counter-rotating cells, secondary flow, laminar, turbulent

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Authors: Sung-Min Kim, Joon-Hong Park, Hyuk Choi

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This study is focused on the comparative analysis and improvement to grasp the flow characteristic of the anti-splash device located under the P/V valve and new concept design models using the CFD. The P/V valve located upper deck to solve the pressure rising and vacuum condition of inner tank of the liquid cargo ships occurred oil outflow accident by transverse and longitudinal sloshing force. Anti-splash device is fitted to improve and prevent this problem in the shipbuilding industry, but the oil outflow accidents are still reported by ship owners. Thus, 4 types of new design model are presented by this study, and then comparative analysis is conducted with new models and existing model. Mostly the key criterion of this problem is flux in the outlet of the anti-splash device. Therefore, the flow and velocity are grasped by transient analysis, and then it decided optimum model and design parameters to develop model. Later, it is needed to develop an anti-splash device by flow test to get certification and verification using experiment equipments.

Keywords: anti-splash device, P/V valve, sloshing, CFD

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Authors: Iris Xiaohong Quan

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The global digital health market reached 175 billion U.S. dollars in 2019, and is expected to grow at about 25% CAGR to over 650 billion USD by 2025. Different terms such as digital health, e-health, mHealth, telehealth have been used in the field, which can sometimes cause confusion. The term digital health was originally introduced to refer specifically to the use of interactive media, tools, platforms, applications, and solutions that are connected to the Internet to address health concerns of providers as well as consumers. While mHealth emphasizes the use of mobile phones in healthcare, telehealth means using technology to remotely deliver clinical health services to patients. According to FDA, “the broad scope of digital health includes categories such as mobile health (mHealth), health information technology (IT), wearable devices, telehealth and telemedicine, and personalized medicine.” Some researchers believe that digital health is nothing else but the cultural transformation healthcare has been going through in the 21st century because of digital health technologies that provide data to both patients and medical professionals. As digital health is burgeoning, but research in the area is still inadequate, our paper aims to clear the definition confusion and provide an overall picture of digital health companies. We further investigate how business models are designed and differentiated in the emerging digital health sector. Both quantitative and qualitative methods are adopted in the research. For the quantitative analysis, our research data came from two databases Crunchbase and CBInsights, which are well-recognized information sources for researchers, entrepreneurs, managers, and investors. We searched a few keywords in the Crunchbase database based on companies’ self-description: digital health, e-health, and telehealth. A search of “digital health” returned 941 unique results, “e-health” returned 167 companies, while “telehealth” 427. We also searched the CBInsights database for similar information. After merging and removing duplicate ones and cleaning up the database, we came up with a list of 1464 companies as digital health companies. A qualitative method will be used to complement the quantitative analysis. We will do an in-depth case analysis of three successful unicorn digital health companies to understand how business models evolve and discuss the challenges faced in this sector. Our research returned some interesting findings. For instance, we found that 86% of the digital health startups were founded in the recent decade since 2010. 75% of the digital health companies have less than 50 employees, and almost 50% with less than 10 employees. This shows that digital health companies are relatively young and small in scale. On the business model analysis, while traditional healthcare businesses emphasize the so-called “3P”—patient, physicians, and payer, digital health companies extend to “5p” by adding patents, which is the result of technology requirements (such as the development of artificial intelligence models), and platform, which is an effective value creation approach to bring the stakeholders together. Our case analysis will detail the 5p framework and contribute to the extant knowledge on business models in the healthcare industry.

Keywords: digital health, business models, entrepreneurship opportunities, healthcare

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Authors: Liane Marcia Freitas Silva, Fernando Augusto Silva Marins, Maria Silene Alexandre Leite

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The adoption of a network structure, like in the supply chains, favors the increase of dependence between companies and, by consequence, their vulnerability. Environment disasters, sociopolitical and economical events, and the dynamics of supply chains elevate the uncertainty of their operation, favoring the occurrence of events that can generate break up in the operations and other undesired consequences. Thus, supply chains are exposed to various risks that can influence the profitability of companies involved, and there are several previous studies that have proposed risk classification models in order to categorize the risks and to manage them. The objective of this paper is to analyze and discuss thirty of these risk classification models by means a theoretical survey. The research method adopted for analyzing and discussion includes three phases: The identification of the types of risks proposed in each one of the thirty models, the grouping of them considering equivalent concepts associated to their definitions, and, the analysis of these risks groups, evaluating their similarities and differences. After these analyses, it was possible to conclude that, in fact, there is more than thirty risks types identified in the literature of Supply Chains, but some of them are identical despite of be used distinct terms to characterize them, because different criteria for risk classification are adopted by researchers. In short, it is observed that some types of risks are identified as risk source for supply chains, such as, demand risk, environmental risk and safety risk. On the other hand, other types of risks are identified by the consequences that they can generate for the supply chains, such as, the reputation risk, the asset depreciation risk and the competitive risk. These results are consequence of the disagreements between researchers on risk classification, mainly about what is risk event and about what is the consequence of risk occurrence. An additional study is in developing in order to clarify how the risks can be generated, and which are the characteristics of the components in a Supply Chain that leads to occurrence of risk.

Keywords: sisks classification, survey, supply chain management, theoretical discussion

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Authors: Keyvan Ramin

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The geometric nonlinearity of Off-Diagonal Bracing System (ODBS) could be a complementary system to covering and extending the nonlinearity of reinforced concrete material. Finite element modeling is performed for flexural frame, x-braced frame and the ODBS braced frame system at the initial phase. Then the different models are investigated along various analyses. According to the experimental results of flexural and x-braced frame, the verification is done. Analytical assessments are performed in according to three-dimensional finite element modeling. Non-linear static analysis is considered to obtain performance level and seismic behavior, and then the response modification factors calculated from each model’s pushover curve. In the next phase, the evaluation of cracks observed in the finite element models, especially for RC members of all three systems is performed. The finite element assessment is performed on engendered cracks in ODBS braced frame for various time steps. The nonlinear dynamic time history analysis accomplished in different stories models for three records of Elcentro, Naghan, and Tabas earthquake accelerograms. Dynamic analysis is performed after scaling accelerogram on each type of flexural frame, x-braced frame and ODBS braced frame one by one. The base-point on RC frame is considered to investigate proportional displacement under each record. Hysteresis curves are assessed along continuing this study. The equivalent viscous damping for ODBS system is estimated in according to references. Results in each section show the ODBS system has an acceptable seismic behavior and their conclusions have been converged when the ODBS system is utilized in reinforced concrete frame.

Keywords: FEM, seismic behaviour, pushover analysis, geometric nonlinearity, time history analysis, equivalent viscous damping, passive control, crack investigation, hysteresis curve

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Authors: Delphine Morales, Florian Lombart, Agathe Truchot, Pauline Maire, Pascale Vigneron, Antoine Galmiche, Catherine Lok, Muriel Vayssade

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Targeted therapy molecules are used as a first-line treatment for metastatic melanoma with B-Raf mutation. Nevertheless, these molecules can cause side effects to patients and are efficient on 50 to 60 % of them. Indeed, melanoma cell sensitivity to targeted therapy molecules is dependent on tumor microenvironment (cell-cell and cell-extracellular matrix interactions). To better unravel factors modulating cell sensitivity to B-Raf inhibitor, we have developed and compared several melanoma models: from metastatic melanoma cells cultured as monolayer (2D) to a co-culture in a 3D dermal equivalent. Cell response was studied in different melanoma cell lines such as SK-MEL-28 (mutant B-Raf (V600E), sensitive to Vemurafenib), SK-MEL-3 (mutant B-Raf (V600E), resistant to Vemurafenib) and a primary culture of dermal human fibroblasts (HDFn). Assays have initially been performed in a monolayer cell culture (2D), then a second time on a 3D dermal equivalent (dermal human fibroblasts embedded in a collagen gel). All cell lines were treated with Vemurafenib (a B-Raf inhibitor) for 48 hours at various concentrations. Cell sensitivity to treatment was assessed under various aspects: Cell proliferation (cell counting, EdU incorporation, MTS assay), MAPK signaling pathway analysis (Western-Blotting), Apoptosis (TUNEL), Cytokine release (IL-6, IL-1α, HGF, TGF-β, TNF-α) upon Vemurafenib treatment (ELISA) and histology for 3D models. In 2D configuration, the inhibitory effect of Vemurafenib on cell proliferation was confirmed on SK-MEL-28 cells (IC50=0.5 µM), and not on the SK-MEL-3 cell line. No apoptotic signal was detected in SK-MEL-28-treated cells, suggesting a cytostatic effect of the Vemurafenib rather than a cytotoxic one. The inhibition of SK-MEL-28 cell proliferation upon treatment was correlated with a strong expression decrease of phosphorylated proteins involved in the MAPK pathway (ERK, MEK, and AKT/PKB). Vemurafenib (from 5 µM to 10 µM) also slowed down HDFn proliferation, whatever cell culture configuration (monolayer or 3D dermal equivalent). SK-MEL-28 cells cultured in the dermal equivalent were still sensitive to high Vemurafenib concentrations. To better characterize all cell population impacts (melanoma cells, dermal fibroblasts) on Vemurafenib efficacy, cytokine release is being studied in 2D and 3D models. We have successfully developed and validated a relevant 3D model, mimicking cutaneous metastatic melanoma and tumor microenvironment. This 3D melanoma model will become more complex by adding a third cell population, keratinocytes, allowing us to characterize the epidermis influence on the melanoma cell sensitivity to Vemurafenib. In the long run, the establishment of more relevant 3D melanoma models with patients’ cells might be useful for personalized therapy development. The authors would like to thank the Picardie region and the European Regional Development Fund (ERDF) 2014/2020 for the funding of this work and Oise committee of "La ligue contre le cancer".

Keywords: 3D human skin model, melanoma, tissue engineering, vemurafenib efficiency

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Authors: Shatha Khuzaee

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The study develops multimodal stylistic approach to analyse a number of BBC online news articles reporting some key events from the so called ‘Arab Uprisings’. Critical stylistics (CS) and visual grammar (VG) provide insightful arguments to the ways ideology is projected through different verbal and visual modes, yet they are mode specific because they examine how each mode projects its meaning separately and do not attempt to clarify what happens intersemiotically when the two modes co-occur. Therefore, it is the task undertaken in this research to propose multimodal stylistic approach that addresses the issue of ideology construction when the two modes co-occur. Informed by functional grammar and social semiotics, the analysis attempts to integrate three linguistic models developed in critical stylistics, namely, transitivity choices, prioritizing and hypothesizing along with their visual equivalents adopted from visual grammar to investigate the way ideology is constructed, in multimodal text, when text/image participate and interrelate in the process of meaning making on the textual level of analysis. The analysis provides comprehensive theoretical and analytical elaborations on the different points of integration between CS linguistic models and VG equivalents which operate on the textual level of analysis to better account for ideology construction in news as non-literary multimodal texts. It is argued that the analysis well thought out a plan that would remark the first step towards the integration between the well-established linguistic models of critical stylistics and that of visual analysis to analyse multimodal texts on the textual level. Both approaches are compatible to produce multimodal stylistic approach because they intend to analyse text and image depending on whatever textual evidence is available. This supports the analysis maintain the rigor and replicability needed for a stylistic analysis like the one undertaken in this study.

Keywords: multimodality, stylistics, visual grammar, social semiotics, functional grammar

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Authors: Akaash Tawade, Manan Khattar, Lucas Spangher, Costas J. Spanos

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Many grids are increasing the share of renewable energy in their generation mix, which is causing the energy generation to become less controllable. Buildings, which consume nearly 33% of all energy, are a key target for demand response: i.e., mechanisms for demand to meet supply. Understanding the behavior of office workers is a start towards developing demand response for one sector of building technology. The literature notes that dynamic computational modeling can be predictive of individual action, especially given that occupant behavior is traditionally abstracted from demand forecasting. Recent work founded on Social Cognitive Theory (SCT) has provided a promising conceptual basis for modeling behavior, personal states, and environment using control theoretic principles. Here, an adapted linear dynamical system of latent states and exogenous inputs is proposed to simulate energy demand amongst office workers engaged in a social energy shifting game. The energy shifting competition is implemented in an office in Singapore that is connected to a minigrid of buildings with a consistent 'price signal.' This signal is translated into a 'points signal' by a reinforcement learning (RL) algorithm to influence participant energy use. The dynamic model functions at the intersection of the points signals, baseline energy consumption trends, and SCT behavioral inputs to simulate future outcomes. This study endeavors to analyze how the dynamic model trains an RL agent and, subsequently, the degree of accuracy to which load deferability can be simulated. The results offer a generalizable behavioral model for energy competitions that provides the framework for further research on transfer learning for RL, and more broadly— transactive control.

Keywords: energy demand forecasting, social cognitive behavioral modeling, social game, transfer learning

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Xiaoci Li, Yonghua Huang, Hui Lin

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Optimization of the concentration of components in mixed refrigerants leads to potential improvement of either thermodynamic cycle performance or safety performance of heat pumps and refrigerators. R32+R1234yf and R125+R290 are two promising binary mixed refrigerants for the application of heat pumps working in the cold areas. The p-ρ-T data of these mixtures are one of the fundamental and necessary properties for design and evaluation of the performance of the heat pumps. Although the property data of mixtures can be predicted by the mixing models based on the pure substances incorporated in programs such as the NIST database Refprop, direct property measurement will still be helpful to reveal the true state behaviors and verify the models. Densities of the mixtures of R32+R1234yf an d R125+R290 are measured by an Anton Paar U shape oscillating tube digital densimeter DMA-4500 in the range of temperatures from 0°C to 100 °C and pressures up to 10 MPa. The accuracy of the measurement reaches 0.00005 g/cm³. The experimental data are compared with the predictions by Refprop in the corresponding range of pressure and temperature.

Keywords: mixed refrigerant, density measurement, densimeter, thermodynamic property

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Authors: Yi Shan Cheng, Ya Hui Yeh, Hsiao Wen Hsu

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Delirium is an acute state of confusion, which is mainly the result of the interaction of many factors, including: age>65 years, comorbidity, cognitive function and visual/auditory impairment, dehydration, pain, sleep disorder, pipeline retention, general anesthesia and major surgery… etc. Researches show the prevalence of delirium in hospitalized elderly patients over 50%. If it doesn't improve in time, may cause cognitive decline or impairment, not only prolong the length of hospital stay but also increase mortality. Some studies have shown that multiple nonpharmacological interventions are the most effective and common strategies, which are reorientation, early mobility, promoting sleep and nutritional support (including water intake), could improve or prevent delirium in the hospitalized elderly. In Taiwan, only one research to compare the delirium incidence of the older patients who have received orthopedic surgery between multi-nonpharmacological interventions and general routine care. Therefore, the purpose of this study is to address the prevention or improvement of delirium incidence density in medical hospitalized elderly, provide clinical nurses as a reference for clinical implementation, and develop follow-up related research. This study is a quasi-experimental design using purposive sampling. Samples are from two wards: the geriatric ward and the general medicine ward at a medical center in central Taiwan. The sample size estimated at least 100, and then the data will be collected through a self-administered structured questionnaire, including: demographic and professional evaluation items. Case recruiting from 5/13/2023. The research results will be analyzed by SPSS for Windows 22.0 software, including descriptive statistics and inferential statistics: logistic regression、Generalized Estimating Equation(GEE)、multivariate analysis of variance(MANOVA).

Keywords: multiple nonpharmacological interventions, hospitalized elderly, delirium incidence, delirium

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Authors: Marlene Rosa, Susana Lopes

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There are few studies that explore the potential of board games as a performance measure, despite it can be an interesting strategy in the context of frailty populations. In fact, board games are immersive strategies than can inhibit the pressure of being evaluated. This study aimed to test the ability of gamed-base strategies to assess executive function in elderly population. Sixteen old participants were included: 10 with affected executive functions (G1 – 85.30±6.00 yrs old; 10 male); 6 with executive functions with non-clinical important modifications (G2 - 76.30±5.19 yrs old; 6 male). Executive tests were assessed using the Frontal Assessment Battery (FAB), which is a quick-applicable cognitive screening test (score<12 means impairment). The board game used in this study was the TATI Hand Game, specifically for training rhythmic coordination of the upper limbs with multiple cognitive stimuli. This game features 1 table grid, 1 set of Single Game cards (to play with one hand); Double Game cards (to play simultaneously with two hands); 1 dice to plan Single Game mode; cards to plan the Double Game mode; 1 bell; 2 cups. Each participant played 3 single game cards, and the following data were collected: (i) variability in time during board game challenges (SD); (ii) number of errors; (iii) execution speed (sec). G1 demonstrated: high variability in execution time during board game challenges (G1 – 13.0s vs G2- 0.5s); a higher number of errors (1.40 vs 0.67); higher execution velocity (607.80s vs 281.83s). These results demonstrated the potential of implementing board games as a functional assessment strategy in geriatric care. Future studies might include larger samples and statistical methodologies to find cut-off values for impairment in executive functions during performance in TATI game.

Keywords: board game, aging, executive function, evaluation

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Authors: Hai L. Tran

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When covering events and issues, the news media often employ both personal accounts as well as facts and figures. However, the process of using numbers and narratives in the newsroom is mostly operated through trial and error. There is a demonstrated need for the news industry to better understand the specific effects of storytelling and data-driven reporting on the audience as well as explanatory factors driving such effects. In the academic world, anecdotal evidence and statistical evidence have been studied in a mutually exclusive manner. Existing research tends to treat pertinent effects as though the use of one form precludes the other and as if a tradeoff is required. Meanwhile, narratives and statistical facts are often combined in various communication contexts, especially in news presentations. There is value in reconceptualizing and theorizing about both relative and collective impacts of numbers and narratives as well as the mechanism underlying such effects. The current undertaking seeks to link theory to practice by providing a complete picture of how and why people are influenced by information conveyed through quantitative and qualitative accounts. Specifically, the cognitive-experiential theory is invoked to argue that humans employ two distinct systems to process information. The rational system requires the processing of logical evidence effortful analytical cognitions, which are affect-free. Meanwhile, the experiential system is intuitive, rapid, automatic, and holistic, thereby demanding minimum cognitive resources and relating to the experience of affect. In certain situations, one system might dominate the other, but rational and experiential modes of processing operations in parallel and at the same time. As such, anecdotes and quantified facts impact audience response differently and a combination of data and narratives is more effective than either form of evidence. In addition, the present study identifies several media variables and human factors driving the effects of statistics and anecdotes. An integrative model is proposed to explain how message characteristics (modality, vividness, salience, congruency, position) and individual differences (involvement, numeracy skills, cognitive resources, cultural orientation) impact selective exposure, which in turn activates pertinent modes of processing, and thereby induces corresponding responses. The present study represents a step toward bridging theoretical frameworks from various disciplines to better understand the specific effects and the conditions under which the use of anecdotal evidence and/or statistical evidence enhances or undermines information processing. In addition to theoretical contributions, this research helps inform news professionals about the benefits and pitfalls of incorporating quantitative and qualitative accounts in reporting. It proposes a typology of possible scenarios and appropriate strategies for journalists to use when presenting news with anecdotes and numbers.

Keywords: data, narrative, number, anecdote, storytelling, news

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Timea Harmath-Tánczos

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Drug-resistant epilepsy (DRE) is defined as the persistence of seizures despite at least two syndrome-adapted antiseizure drugs (ASD) used at efficacious daily doses. About a third of patients with epilepsy suffer from drug resistance. Cognitive assessment has a crucial role in the diagnosis and clinical management of epilepsy. Previous studies have addressed the clinical targets and indications for measuring neuropsychological functions; best to our knowledge, no studies have examined it in a Hungarian therapy-resistant population. To fill this gap, we investigated the Hungarian diagnostic protocol between 18 and 65 years of age. This study aimed to describe and analyze neuropsychological functions in patients with drug-resistant epilepsy and identify factors associated with neuropsychology deficits. We perform a prospective case-control study comparing neuropsychological performances in 50 adult patients and 50 healthy individuals between March 2023 and July 2023. Neuropsychological functions were examined in both patients and controls using a full set of specific tests (general performance level, motor functions, attention, executive facts., verbal and visual memory, language, and visual-spatial functions). Potential risk factors for neuropsychological deficit were assessed in the patient group using a multivariate analysis. The two groups did not differ in age, sex, dominant hand and level of education. Compared with the control group, patients with drug-resistant epilepsy showed worse performance on motor functions and visuospatial memory, sustained attention, inhibition and verbal memory. Neuropsychological deficits could therefore be systematically detected in patients with drug-resistant epilepsy in order to provide neuropsychological therapy and improve quality of life. The analysis of the classical and complex indices of the special neuropsychological tasks presented in the presentation can help in the investigation of normal and disrupted memory and executive functions in the DRE.

Keywords: drug-resistant epilepsy, Hungarian diagnostic protocol, memory, executive functions, cognitive neuropsychology

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Authors: Saeedullah J. Mandokhail, Shamsher Sadiq, Meer H. Khan

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This paper presents the comparison of excess pore water pressure ratio (ru) predicted by using accumulated stress based pore pressure model and plasticity model. One dimensional effective stress site response analyses were performed on a 30 m deep sand column (consists of a liquefiable layer in between non-liquefiable layers) using accumulated stress based pore pressure model in Deepsoil and PDMY2 (PressureDependentMultiYield02) model in Opensees. Three Input motions with different peak ground acceleration (PGA) levels of 0.357 g, 0.124 g, and 0.11 g were used in this study. The developed excess pore pressure ratio predicted by the above two models were compared and analyzed along the depth. The time history of the ru at mid of the liquefiable layer and non-liquefiable layer were also compared. The comparisons show that the two models predict mostly similar ru values. The predicted ru is also consistent with the PGA level of the input motions.

Keywords: effective stress, excess pore pressure ratio, pore pressure model, site response analysis

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Zhifeng Kong

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Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory.

Keywords: over-parameterization, rectified linear units ReLU, convergence, gradient descent, neural networks

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Authors: Zeinab Yazdanfar, Dilan Robert, Daniel Lester, S. Setunge

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Scour has been identified as the most common threat to bridge stability worldwide. Traditionally, scour around bridge piers is calculated using the empirical approaches that have considerable limitations and are difficult to generalize. The multi-physic nature of scouring which involves turbulent flow, soil mechanics and solid-fluid interactions cannot be captured by simple empirical equations developed based on limited laboratory data. These limitations can be overcome by direct numerical modeling of coupled hydro-mechanical scour process that provides a robust prediction of bridge scour and valuable insights into the scour process. Several numerical models have been proposed in the literature for bridge scour estimation including Eulerian flow models and coupled Euler-Lagrange models incorporating an empirical sediment transport description. However, the contact forces between particles and the flow-particle interaction haven’t been taken into consideration. Incorporating collisional and frictional forces between soil particles as well as the effect of flow-driven forces on particles will facilitate accurate modeling of the complex nature of scour. In this study, a coupled Computational Fluid Dynamics and Discrete Element Model (CFD-DEM) has been developed to simulate the scour process that directly models the hydro-mechanical interactions between the sediment particles and the flowing water. This approach obviates the need for an empirical description as the fundamental fluid-particle, and particle-particle interactions are fully resolved. The sediment bed is simulated as a dense pack of particles and the frictional and collisional forces between particles are calculated, whilst the turbulent fluid flow is modeled using a Reynolds Averaged Navier Stocks (RANS) approach. The CFD-DEM model is validated against experimental data in order to assess the reliability of the CFD-DEM model. The modeling results reveal the criticality of particle impact on the assessment of scour depth which, to the authors’ best knowledge, hasn’t been considered in previous studies. The results of this study open new perspectives to the scour depth and time assessment which is the key to manage the failure risk of bridge infrastructures.

Keywords: bridge scour, discrete element method, CFD-DEM model, multi-phase model

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Authors: Tamir Michal

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The subject of Bedouin dispersion has accompanied the State of Israel from the day of its establishment. From a legal point of view, this subject has offered a launchpad for creative judicial decisions. Thus, for example, the first court decision in Israel to recognize affirmative action (Avitan), dealt with a petition submitted by a Jew appealing the refusal of the State to recognize the Petitioner’s entitlement to the long-term lease of a plot designated for Bedouins. The Supreme Court dismissed the petition, holding that there existed a public interest in assisting Bedouin to establish permanent urban settlements, an interest which justifies giving them preference by selling them plots at subsidized prices. In another case (The Forum for Coexistence in the Negev) the Supreme Court extended equitable relief for the purpose of constructing a bridge, even though the construction infringed the Law, in order to allow the children of dispersed Bedouin to reach school. Against this background, the recent verdict, delivered during the Protective Edge military campaign, which dismissed a petition aimed at forcing the State to spread out Protective Structures in Bedouin villages in the Negev against the risk of being hit from missiles launched from Gaza (Abu Afash) is disappointing. Even if, in arguendo, no selective discrimination was involved in the State’s decision not to provide such protection, the decision, and its affirmation by the Court, is problematic when examined through the prism of the Theory of Recognition. The article analyses the issue by tools of theory of Recognition, according to which people develop their identities through mutual relations of recognition in different fields. In the social context, the path to recognition is cognitive respect, which is provided by means of legal rights. By seeing other participants in Society as bearers of rights and obligations, the individual develops an understanding of his legal condition as reflected in the attitude to others. Consequently, even if the Court’s decision may be justified on strict legal grounds, the fact that Jewish settlements were protected during the military operation, whereas Bedouin villages were not, is a setback in the struggle to make the Bedouin citizens with equal rights in Israeli society. As the Court held, ‘Beyond their protective function, the Migunit [Protective Structures] may make a moral and psychological contribution that should not be undervalued’. This contribution is one that the Bedouin did not receive in the Abu Afash verdict. The basic thesis is that the Court’s verdict analyzed above clearly demonstrates that the reliance on classical liberal instruments (e.g., equality) cannot secure full appreciation of all aspects of Bedouin life, and hence it can in fact prejudice them. Therefore, elements of the recognition theory should be added, in order to find the channel for cognitive dignity, thereby advancing the Bedouins’ ability to perceive themselves as equal human beings in the Israeli society.

Keywords: bedouin dispersion, cognitive respect, recognition theory, sustainable development

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Authors: Hepinda Fajari Nuharini, Sugiarti A. Musabiq

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Background: Various and continuous pressures that exceed individual resources can cause first-year undergraduate college students to experience psychological distress. Psychological distress can occur when individuals use rumination as cognitive coping strategies. Rumination is one of the cognitive coping strategies that can be used by individuals to respond to psychological distress that causes individuals to think about the causes and consequences of events that have occurred. Rumination had two subtypes, such as brooding and reflective. Therefore, the purpose of this study was determining the role of brooding and reflective as subtypes of rumination toward psychological distress in University of Indonesia first-year undergraduate students. Methods: Participants of this study were 403 University of Indonesia first-year undergraduate students aged between 18 and 21 years old. Psychological distress measured using self reporting questionnaire (SRQ-20) and brooding and reflective as subtypes of rumination measured using Ruminative Response Scale - Short Version (RRS - Short Version). Results: Binary logistic regression analyses showed that 22.8% of the variation in psychological distress could be explained by the brooding and reflective as subtypes of rumination, while 77.2% of the variation in psychological distress could be explained by other factors (Nagelkerke R² = 0,228). The results of the binary logistic regression analysis also showed rumination subtype brooding is a significant predictor of psychological distress (b = 0,306; p < 0.05), whereas rumination subtype reflective is not a significant predictor of psychological distress (b = 0,073; p > 0.05). Conclusion: The findings of this study showed a positive relationship between brooding and psychological distress indicates that a higher level of brooding will predict higher psychological distress. Meanwhile, a negative relationship between reflective and psychological distress indicates a higher level of reflective will predict lower psychological distress in University of Indonesia first-year undergraduate students. Added Values: The psychological distress among first-year undergraduate students would then have an impact on student academic performance. Therefore, the results of this study can be used as a reference for making preventive action to reduce the percentage and impact of psychological distress among first-year undergraduate students.

Keywords: brooding as subtypes of rumination, first-year undergraduate students, psychological distress, reflective as subtypes of rumination

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Authors: Yuan Long, Fabrizio Ceschin, David Harrison

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Environmental sustainability is under the threat of excessive single-use plastic packaging waste, and current waste management fails to address this issue. Therefore, it has led to a reidentification of the alternative, which can curb the packaging waste without reducing social needs. Reusable packaging represents a circular approach to close the loop of consumption in which packaging can stay longer in the system to satisfy social needs. However, the implementation of reusable packaging is fragmented and lacks systematic approaches. The product-service system (PSS) is widely regarded as a sustainable business model innovation for embracing circular consumption. As a result, applying PSS to reusable packaging solutions will be promising to address the packaging waste issue. This paper aims at filling the knowledge gap relating to apply PSS to reusable packaging solutions and provide a strategic design tool that could support packaging professionals to design reusable packaging solutions. The methodology of this paper is case studies and workshops to provide a design tool. The respondents are packaging professionals who are packaging consultants, NGO professionals, and entrepreneurs. 57 cases collected show that 15 archetypal models operate in the market. Subsequently, a polarity diagram is developed to embrace those 15 archetypal models, and a total number of 24 experts were invited for the workshop to evaluate the design tool. This research finally provides a strategic design tool to support packaging professionals to design reusable packaging solutions. The application of the tool is to support the understanding of the reusable packaging solutions, analyzing the markets, identifying new opportunities, and generate new business models. The implication of this research is to provide insights for academics and businesses in terms of tackling single-use packaging waste and build a foundation for further development of the reusable packaging solution tool.

Keywords: environmental sustainability, product-service system, reusable packaging, design tool

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Authors: Grmanesh Gidey Kahsay

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In modern technical engineering applications, quality is defined in two ways. The first one is that quality is the parameter that measures a product or service’s characteristics to meet and satisfy the pre-stated or fundamental needs (reliability, durability, serviceability). The second one is the quality of a product or service free of any defect or deficiencies. The American Society for Quality (ASQ) describes quality as a pursuit of optimal solutions to confirm successes and fulfillment to be accountable for the product or service's requirements and expectations. This article focuses on quality engineering tools in modern industrial applications. Quality engineering is a field of engineering that deals with the principles, techniques, models, and applications of the product or service to guarantee quality. Including the entire activities to analyze the product’s design and development, quality engineering emphasizes how to make sure that products and services are designed and developed to meet consumers’ requirements. This episode acquaints with quality tools such as quality systems, auditing, product design, and process control. The finding presents thoughts that aim to improve quality engineering proficiency and effectiveness by introducing essential quality techniques and tools in some selected industries.

Keywords: essential quality tools, quality systems and models, quality management systems, and quality assurance

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Authors: Milan N. A. Van Der Kuil, Anne M. A. Visser-Meily, Andrea W. M. Evers, Ineke J. M. Van Der Ham

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Navigation ability is essential for autonomy and mobility in daily life. In patients with acquired brain injury, navigation impairment is frequently impaired; however, in this study, we tested the effectiveness of a serious gaming training protocol as a tool for cognitive rehabilitation to reduce navigation impairment. In total, 38 patients with acquired brain injury and subjective navigation complaints completed the experiment, with a partially blind, randomized control trial design. An objective navigation test was used to construct a strengths and weaknesses profile for each patient. Subsequently, patients received personalized compensation training that matched their strengths and weaknesses by addressing an egocentric or allocentric strategy or a strategy aimed at minimizing the use of landmarks. Participants in the experimental condition received psychoeducation and a home-based rehabilitation game with a series of exercises (e.g., map reading, place finding, and turn memorization). The exercises were developed to stimulate the adoption of more beneficial strategies, according to the compensatory approach. Self-reported navigation ability (wayfinding questionnaire), participation level, and objective navigation performance were measured before and after 1 and 4 weeks after completing the six-week training program. Results indicate that the experimental group significantly improved in subjective navigation ability both 1 and 4 weeks after completion of the training, in comparison to the score before training and the scores of the control group. Similarly, goal attainment showed a significant increase after the first and fourth week after training. Objective navigation performance was not affected by the training. This navigation training protocol provides an effective solution to address navigation impairment after acquired brain injury, with clear improvements in subjective performance and goal attainment of the participants. The outcomes of the training should be re-examined after implementation in a clinical setting.

Keywords: spatial navigation, cognitive rehabilitation, serious gaming, acquired brain injury

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Authors: S. Khamtree, T. Ratanawilai, C. Nuntadusit

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Rubberwood is a crucial commercial timber in Southern Thailand. All processes in a rubberwood production depend on the knowledge and expertise of the technicians, especially the drying process. This research aims to develop an empirical model for drying kinetics in rubberwood. During the experiment, the temperature of the hot air and the average air flow velocity were kept at 80-100 °C and 1.75 m/s, respectively. The moisture content in the samples was determined less than 12% in the achievement of drying basis. The drying kinetic was simulated using an empirical solver. The experimental results illustrated that the moisture content was reduced whereas the drying temperature and time were increased. The coefficient of the moisture ratio between the empirical and the experimental model was tested with three statistical parameters, R-square (R²), Root Mean Square Error (RMSE) and Chi-square (χ²) to predict the accuracy of the parameters. The experimental moisture ratio had a good fit with the empirical model. Additionally, the results indicated that the drying of rubberwood using the Henderson and Pabis model revealed the suitable level of agreement. The result presented an excellent estimation (R² = 0.9963) for the moisture movement compared to the other models. Therefore, the empirical results were valid and can be implemented in the future experiments.

Keywords: empirical models, rubberwood, moisture ratio, hot air drying

Procedia PDF Downloads 268