Search results for: panel data analysis

Authors: Manthan Patel, Vinit Ahlawat, Anshh Singh Claire, Pijush Samui

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This paper focuses on the analysis of quicksand condition for a dam foundation. The quicksand condition occurs in cohesion less soil when effective stress of soil becomes zero. In a dam, the saturated sediment may appear quite solid until a sudden change in pressure or shock initiates liquefaction. This causes the sand to form a suspension and lose strength hence resulting in failure of dam. A soil profile shows different properties at different points and the values obtained are uncertain thus reliability analysis is performed. The reliability is defined as probability of safety of a system in a given environment and loading condition and it is assessed as Reliability Index. The reliability analysis of dams under quicksand condition is carried by Gaussian Process Regression (GPR). Reliability index and factor of safety relating to liquefaction of soil is analysed using GPR. The results of reliability analysis by GPR is compared to that of conventional method and it is demonstrated that on applying GPR the probabilistic analysis reduces the computational time and efforts.

Keywords: factor of safety, GPR, reliability index, quicksand

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Authors: Beenish Urooj, Ubaid Ullah, Sidra Riasat

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National Testing Service-Pakistan (NTS) is an agency in Pakistan that conducts student success appraisal examinations. In this research paper, we must present a security model for the NTS organization. The security model will depict certain security countermeasures for a better defense against certain types of breaches and system malware. We will provide a security roadmap, which will help the company to execute its further goals to maintain security standards and policies. We also covered multiple aspects in securing the environment of the organization. We introduced the processes, architecture, data classification, auditing approaches, survey responses, data handling, and also training and awareness of risk for the company. The primary contribution is the Risk Survey, based on the maturity model meant to assess and examine employee training and knowledge of risks in the company's activities.

Keywords: NTS, risk assessment, threat factors, security, services

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Authors: Fan Ye

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Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: RWIS, visibility distance, low visibility, adverse weather

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Authors: Rahul Malhotra

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Rajkumar Ghosh

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The study presents an overview of the many uses and approaches for estimating out-of-sequence thrust movement in earthquake mitigation. The study investigates how knowing and forecasting thrust movement during seismic occurrences might assist to effective earthquake mitigation measures. The review begins by discussing out-of-sequence thrust movement and its importance in earthquake mitigation strategies. It explores how typical techniques of estimating thrust movement may not capture the full complexity of seismic occurrences and emphasizes the benefits of include out-of-sequence data in the analysis. A thorough review of existing research and studies on out-of-sequence thrust movement estimates for earthquake mitigation. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources such as GPS measurements, satellite imagery, and seismic recordings. The study also examines the use of out-of-sequence thrust movement estimates in earthquake mitigation measures. It investigates how precise calculation of thrust movement may help improve structural design, analyse infrastructure risk, and develop early warning systems. The potential advantages of using out-of-sequence data in these applications to improve the efficiency of earthquake mitigation techniques. The difficulties and limits of estimating out-of-sequence thrust movement for earthquake mitigation. It addresses data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and increase the accuracy and reliability of out-of-sequence thrust movement estimates, the authors recommend topics for additional study and improvement. The study is a helpful resource for seismic monitoring and earthquake risk assessment researchers, engineers, and policymakers, supporting innovations in earthquake mitigation measures based on a better knowledge of thrust movement dynamics.

Keywords: earthquake mitigation, out-of-sequence thrust, satellite imagery, seismic recordings, GPS measurements

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Authors: Handan Alan, Ulkü Baykal

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Personal characteristics help people understand and recognize both themselves and other people. They are also known to have direct effects on managerial behaviors. Managers’ personalities indicate how they think, perceive reality and relate to others, and affect their decision-making and problem-solving methods. This descriptive study aims to determine the personal characteristics of nurse managers and the personal and professional factors that affect them since sufficient data does not exist on personal characteristics despite the focus on the leadership and managerial characteristics in nursing. The study population consisted of nurses working in administrative positions at hospitals affiliated with the public hospitals union, research and practice hospitals affiliated with universities and private hospitals in cities in the Marmara Region. The study sample consisted of nurse managers working in the hospitals that permitted conducting the study (excluding private branch hospitals). The data were collected after obtaining the approval of the Clinical Research Ethics Committee of Çanakkale Onsekiz Mart University (Approval date: 1.7.2015, Decision No: 2015-01) and written official permissions from the administrations of the hospitals included in the study. The data analysis was carried out using means and standard deviations (SD) as descriptive statistics, one-way analysis of variance for inter-group comparisons and the independent samples t-test for paired group comparisons. A significance threshold of p < 0.05 was used to evaluate the findings. The data were collected using the Five Factor Personality Inventory. The study included 900 nurse managers, who obtained the highest mean score on the conscientiousness dimension (X=4.22 ±0.35). This dimension was followed by their mean scores on the agreeableness (X=4.06±0.40), intelligence (X=4.05±0.37), extroversion (X=3.50±0.43), and emotional instability (X=2.07±0.53) dimensions. Statistically significant differences were found between the independent variables of age, gender, marital status, education level, work institution, professional experience, institutional experience, managerial experience, administrative position, work unit and managerial education when compared using the five factor personality inventory (p < 0.05). In conclusion, the nurse managers described themselves having high conscientiousness. Statistically significant differences were found between the five factor personality inventory mean scores and their personal and professional characteristics.

Keywords: nurse manager, personality, personal characteristics, professional characteristics

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Authors: Smita Nepal

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Despite being on the global education reform agenda for over two decades, interpretations and practices of inclusive education vary widely across the world. In Nepal, similar to many other developing countries, inclusive education is still an emerging concept and limited research is available to date in relation to how inclusive education is conceptualized and implemented here. Moreover, very little is known about how teachers who are at the frontline of providing inclusive education understand this concept and how well they are prepared to teach inclusively. This study addresses this research gap by investigating an overarching research question, ‘How prepared are Nepalese teachers to practice inclusive pedagogy?’ Different societies and cultures may have different interpretations of the concepts of diversity and inclusion. Acknowledging that such contextual differences influence how these issues are addressed, such as preparing teachers for providing inclusive education, this study has investigated the research questions using a sociocultural conceptual framework. A sequential mixed-method research design involved quantitative data from 203 survey responses collected in the first phase, followed by qualitative data in the second phase collected through semi-structured interviews with teachers. Descriptive analysis of the quantitative data and reflexive thematic analysis of the qualitative data revealed a narrow understanding of inclusive education in the participating Nepalese teachers with limited preparedness for implementing inclusive pedagogy. Their interpretation of inclusion substantially included the need for non-discrimination and the provision of equal opportunities. This interpretation was found to be influenced by the social context where a lack of a deep understanding of human diversity was reported, leading to discriminatory attitudes and practices. In addition, common norms established in society that experiencing privileges or disadvantages was normal for diverse groups of people appeared to have led to limited efforts to enhance teachers’ understanding of and preparedness for inclusive education. This study has significant implications, not only in the Nepalese context but globally, for reform in policies and practices and for strengthening the teacher education and professional development system to promote inclusion in education. In addition, the significance of this research lies in highlighting the importance of further context-specific research in this area to ensure inclusive education in a real sense by valuing socio-cultural differences.

Keywords: inclusive education, inclusive pedagogy, sociocultural context, teacher preparation

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Authors: Sahand Golmohammadi, Sana Hosseini Shirazi

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Because the status and mechanical parameters of discontinuities in the rock mass are included in the calculations, various methods of rock engineering classification are often used as a starting point for the design of different types of structures. The Q-system is one of the most frequently used methods for stability analysis and determination of support systems of underground structures in rock, including tunnel. In this method, six main parameters of the rock mass, namely, the rock quality designation (RQD), joint set number (Jn), joint roughness number (Jr), joint alteration number (Ja), joint water parameter (Jw) and stress reduction factor (SRF) are required. In this regard, in order to achieve a reasonable and optimal design, identifying the effective parameters for the stability of the mentioned structures is one of the most important goals and the most necessary actions in rock engineering. Therefore, it is necessary to study the relationships between the parameters of a system and how they interact with each other and, ultimately, the whole system. In this research, it has attempted to determine the most effective parameters (key parameters) from the six parameters of rock mass in the Q-system using the rock engineering system (RES) method to improve the relationships between the parameters in the calculation of the Q value. The RES system is, in fact, a method by which one can determine the degree of cause and effect of a system's parameters by making an interaction matrix. In this research, the geomechanical data collected from the water conveyor tunnel of Azad Dam were used to make the interaction matrix of the Q-system. For this purpose, instead of using the conventional methods that are always accompanied by defects such as uncertainty, the Q-system interaction matrix is coded using a technique that is actually a statistical analysis of the data and determining the correlation coefficient between them. So, the effect of each parameter on the system is evaluated with greater certainty. The results of this study show that the formed interaction matrix provides a reasonable estimate of the effective parameters in the Q-system. Among the six parameters of the Q-system, the SRF and Jr parameters have the maximum and minimum impact on the system, respectively, and also the RQD and Jw parameters have the maximum and minimum impact on the system, respectively. Therefore, by developing this method, we can obtain a more accurate relation to the rock mass classification by weighting the required parameters in the Q-system.

Keywords: Q-system, rock engineering system, statistical analysis, rock mass, tunnel

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Authors: Eva Štěpánková

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The decision-making process is theoretically clearly defined. Generally, it includes the problem identification and analysis, data gathering, goals and criteria setting, alternatives development and optimal alternative choice and its implementation. In practice however, various modifications of the theoretical decision-making process can occur. The managers can consider some of the phases to be too complicated or unfeasible and thus they do not carry them out and conversely some of the steps can be overestimated. The aim of the paper is to reveal and characterize the perception of the individual phases of decision-making process by the managers. The research is concerned with managers in the military environment–commanders. Quantitative survey is focused cross-sectionally in the individual levels of management of the Ministry of Defence of the Czech Republic. On the total number of 135 respondents the analysis focuses on which of the decision-making process phases are problematic or not carried out in practice and which are again perceived to be the easiest. Then it is examined the reasons of the findings.

Keywords: decision making, decision making process, decision problems, concrete organization

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Authors: Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević, Lidija R. Jevrić, Stela Jokić

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The subject of this paper is to correlate antibacterial behavior of benzimidazole derivatives with their molecular characteristics using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on the inhibitory activity of benzimidazole derivatives against Staphylococcus aureus. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as a calibration models for prediction of the inhibitory activity. The quality of the models was validated by leave one out (LOO) technique and by using external data set. High agreement between experimental and predicted inhibitory acivities indicated the good quality of the derived models. These results are part of the CMST COST Action No. CM1306 "Understanding Movement and Mechanism in Molecular Machines".

Keywords: Antibacterial, benzimidazoles, chemometric, QSAR.

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Authors: Trilochana Dash, Chandan Kumar Sahoo

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In today's environment of intensive industrialization, the role of business in societal modernization is critical. The concept of corporate social responsibility (CSR) arose due to rising societal awareness of company conduct. Corporations that practice CSR devote a portion of their profits to society’s sustainable development (SD). The concept of CSR and SD has increased the impact of industries on society. In this study, bibliometric analysis was conducted using the “R” programming language to determine the comprehensiveness of CSR and SD. From 2003 to 2022, bibliometric data was collected from two databases: Scopus and Web of Science (WOS). According to the findings, CSR and SD research has risen exponentially in the past two decades, and “Corporate Social Responsibility and Environment Management” emerged as the most influential journal in this field. The findings also show that relatively very few researchers collaborate in CSR and SD research in the last twenty years. It is widely acknowledged that most CSR and SD research is conducted in developed countries and developing countries undergoing fast industrialization. Thematic evolution and cluster analysis clearly show that the notion of CSR and SD among scholars has been quite popular over the last two decades. Finally, limitations and future directions are discussed.

Keywords: corporate social responsibility, sustainable development, bibliometric analysis, “R” programming language, visualization, holistic picture

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Authors: Md Monirul Islam, Shahaduz Zaman, Mosarraf H. Sarker

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Context: Medical waste management (MWM) is a critical sector in Bangladesh due to its impact on human health and the environment. There is a need to assess the current policies and identify the role of policy actors in the policy formulation and implementation process. Research Aim: The study aimed to evaluate the role of policy actors in the medical waste management policy-making process in Bangladesh, identify policy gaps, and provide actionable recommendations for improvement. Methodology: The study adopted a qualitative research method and conducted key informant interviews. The data collected were analyzed using the thematic coding approach through Atlas.ti software. Findings: The study found that policies are formulated at higher administrative levels and implemented in a top-down approach. Higher-level institutions predominantly contribute to policy development, while lower-level institutions focus on implementation. However, due to negligence, ignorance, and lack of coordination, medical waste management receives insufficient attention from the actors. The study recommends the need for immediate strategies, a comprehensive action plan, regular policy updates, and inter-ministerial meetings to enhance medical waste management practices and interventions. Theoretical Importance: The research contributes to evaluating the role of policy actors in medical waste management policymaking and implementation in Bangladesh. It identifies policy gaps and provides actionable recommendations for improvement. Data Collection: The study used key informant interviews as the data collection method. Thirty-six participants were interviewed, including influential policymakers and representatives of various administrative spheres. Analysis Procedures: The data collected was analyzed using the inductive thematic analysis approach. Question Addressed: The study aimed to assess the role of policy actors in medical waste management policymaking and implementation in Bangladesh. Conclusion: In conclusion, the study provides insights into the current medical waste management policy in Bangladesh, the role of policy actors in policy formulation and implementation, and the need for improved strategies and policy updates. The findings of this study can guide future policy-making efforts to enhance medical waste management practices and interventions in Bangladesh.

Keywords: key informant, medical waste management, policy maker, qualitative study

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Authors: D. F. Zeynalov, T. V. Kartseva, O. V. Sorokin

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Currently, approaches to assess cardiointervalography are based on the calculation of data variance intervals RR. However, they do not allow the evaluation of features related to a period of the cardiac cycle, so how electromechanical phenomena during cardiac subphase are characterized by differently directed changes. Therefore, we have proposed a method of subphase analysis of the cardiac cycle, developed in the department of hominal physiology Novosibirsk State Medical University to identify the features of the dispersion subphase of the cardiac cycle. In the present paper we have examined the 5-minute intervals cardiointervalography (CIG) to isolate RR-, QT-, ST-ranges in healthy children and children with acute respiratory diseases (ARD) in comparison. It is known that primary school-aged children suffer at ARD 5-7 times per year. Consequently, it is one of the most relevant problems in pediatrics. It is known that the spectral indices and indices of temporal analysis of heart rate variability are highly sensitive to the degree of intoxication during immunological process. We believe that the use of subphase analysis of heart rate will allow more thoroughly evaluate responsiveness of the child organism during the course of ARD. The study involved 60 primary school-aged children (30 boys and 30 girls). In order to assess heart rhythm regulation, the record CIG was used on the "VNS-Micro" device of Neurosoft Company (Ivanovo) for 5 minutes in the supine position and 5 minutes during active orthostatic test. Subphase analysis of variance QT-interval and ST-segment was performed on the "KardioBOS" software Biokvant Company (Novosibirsk). In assessing the CIG in the supine position and in during orthostasis of children with acute respiratory diseases only RR-intervals are observed typical trend of general biological reactions through pressosensitive compensation mechanisms to lower blood pressure, but compared with healthy children the severity of the changes is different, of sick children are more pronounced indicators of heart rate regulation. But analysis CIG RR-intervals and analysis subphase ST-segment have yielded conflicting trends, which may be explained by the different nature of the intra- and extracardiac influences on regulatory mechanisms that implement the various phases of the cardiac cycle.

Keywords: acute respiratory diseases, cardiointervalography, subphase analysis, cardiac cycle

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Authors: Angkrit Phitchayangkoon, Ar-Aishah Dadeh

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Background: A key aim of triage is to identify the patients with high risk of cardiac arrest because they require intensive monitoring, resuscitation facilities, and early intervention. We aimed to identify the predicting factors such as initial vital signs, serum pH, serum lactate level, initial capillary blood glucose, and Modified Early Warning Score (MEWS) which affect the occurrence of cardiac arrest in an emergency department (ED). Methods: We conducted a retrospective data review of ED patients in an emergency department (ED) from 1 August 2014 to 31 July 2016. Significant variables in univariate analysis were used to create a multivariate analysis. Differentiation of predicting factors between cardiac arrest patient and non-cardiac arrest patients for occurrence of cardiac arrest in an emergency department (ED) was the primary outcome. Results: The data of 527 non-trauma patients with Emergency Severity Index (ESI) 1-3 were collected. The factors found to have a significant association (P < 0.05) in the non-cardiac arrest group versus the cardiac arrest group at the ED were systolic BP (mean [IQR] 135 [114,158] vs 120 [90,140] mmHg), oxygen saturation (mean [IQR] 97 [89,98] vs 82.5 [78,95]%), GCS (mean [IQR] 15 [15,15] vs 11.5 [8.815]), normal sinus rhythm (mean 59.8 vs 30%), sinus tachycardia (mean 46.7 vs 21.7%), pH (mean [IQR] 7.4 [7.3,7.4] vs 7.2 [7,7.3]), serum lactate (mean [IQR] 2 [1.1,4.2] vs 7 [5,10.8]), and MEWS score (mean [IQR] 3 [2,5] vs 5 [3,6]). A multivariate analysis was then performed. After adjusting for multiple factors, ESI level 2 patients were more likely to have cardiac arrest in the ER compared with ESI 1 (odds ratio [OR], 1.66; P < 0.001). Furthermore, ESI 2 patients were more likely than ESI 1 patients to have cardiovascular disease (OR, 1.89; P = 0.01), heart rate < 55 (OR, 6.83; P = 0.18), SBP < 90 (OR, 3.41; P = 0.006), SpO2 < 94 (OR, 4.76; P = 0.012), sinus tachycardia (OR, 4.32; P = 0.002), lactate > 4 (OR, 10.66; P = < 0.001), and MEWS > 4 (OR, 4.86; P = 0.028). These factors remained predictive of cardiac arrest at the ED. Conclusion: The factors related to cardiac arrest in the ED are ESI 1 patients, ESI 2 patients, patients diagnosed with cardiovascular disease, SpO2 < 94, lactate > 4, and a MEWS > 4. These factors can be used as markers in the event of simultaneous arrival of many patients and can help as a pre-state for patients who have a tendency to develop cardiac arrest. The hemodynamic status and vital signs of these patients should be closely monitored. Early detection of potentially critical conditions to prevent critical medical intervention is mandatory.

Keywords: cardiac arrest, predicting factor, emergency department, emergency patient

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Authors: Kerem Ok, Serafettin Senturk, Serdar Aktas, Cem Cevikbas

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Mobile technology is very popular nowadays and it provides a digital world where users can experience many value-added services. Service Providers are also eager to offer diverse value-added services to users such as digital identity, mobile financial services and so on. In this context, the security of data storage in smartphones and the security of communication between the smartphone and service provider are critical for the success of these services. In order to provide the required security functions, the SIM card is one acceptable alternative. Since SIM cards include a Secure Element, they are able to store sensitive data, create cryptographically secure keys, encrypt and decrypt data. In this paper, we design and implement a SIM and a smartphone framework that uses a SIM card for secure key generation, key storage, data encryption, data decryption and digital signing for mobile financial services. Our frameworks show that the SIM card can be used as a controlled Secure Element to provide required security functions for popular e-services such as mobile financial services.

Keywords: SIM card, mobile financial services, cryptography, secure data storage

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Dana Craciun, Daniela Dascalu, Adriana Isvoran

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Phthalates are a class of plastic additives that are used to enhance the physical properties of plastics and as solvents in paintings and some of them proved to be of particular concern for the human health. There are insufficient data concerning the health risks of phthalates and further research on evaluating their effects in humans is needed. As humans are not volunteers for such experiments, computational analysis may be used to predict the biological effects of phthalates in humans. Within this study we have used some computational approaches (SwissADME, admetSAR, FAFDrugs) for predicting the absorption, distribution, metabolization, excretion and toxicity (ADME-Tox) profiles and pharmacokinetics for the most common used phthalates. These computational tools are based on quantitative structure-activity relationship modeling approach. The predictions are further compared to the known effects of each considered phthalate in humans and correlations between computational results and experimental data are discussed. Our data revealed that phthalates are a class of compounds reflecting high toxicity both when ingested and when inhaled, but by inhalation their toxicity is even greater. The predicted harmful effects of phthalates are: toxicity and irritations of the respiratory and gastrointestinal tracts, dyspnea, skin and eye irritations and disruption of the functions of liver and of the reproductive system. Many of investigated phthalates are predicted to be able to inhibit some of the cytochromes involved in the metabolism of numerous drugs and consequently to affect the efficiency of administrated treatments for many diseases and to intensify the adverse drugs reactions. The obtained predictions are in good agreement with clinical data concerning the observed effects of some phthalates in cases of acute exposures. Our study emphasizes the possible health effects of numerous phthalates and underlines the applicability of computational methods for predicting the biological effects of xenobiotics.

Keywords: phthalates, ADME-Tox, pharmacokinetics, biological effects

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Authors: Soumita Banerjee

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The State of West Bengal is mostly watered by innumerable rivers, and they are different in nature in both the northern and the southern part of the state. The southern part of West Bengal is mainly drained with the river Bhagirathi-Hooghly, and its major distributaries and tributaries have divided this major river basin into many subparts like the Ichamati-Bidyadhari, Pagla-Bansloi, Mayurakshi-Babla, Ajay, Damodar, Kangsabati Sub-basin to name a few. These rivers basically drain the Districts of Bankura, Burdwan, Hooghly, Nadia and Purulia, Birbhum, Midnapore, Murshidabad, North 24-Parganas, Kolkata, Howrah and South 24-Parganas. West Bengal has a huge number of flood-prone blocks in the southern part of the state of West Bengal, the responsible factors for flood situation are the shape and size of the catchment area, its steep gradient starting from plateau to flat terrain, the river bank erosion and its siltation, tidal condition especially in the lower Ganga Basin and very low maintenance of the embankments which are mostly used as communication links. Along with these factors, DVC (Damodar Valley Corporation) plays an important role in the generation (with the release of water) and controlling the flood situation. This year the whole Gangetic West Bengal is being flooded due to high intensity and long duration rainfall, and the release of water from the Durgapur Barrage As most of the rivers are interstate in nature at times floods also take place with release of water from the dams of the neighbouring states like Jharkhand. Other than Embankments, there is no such structural measures for combatting flood in West Bengal. This paper tries to analyse the reasons behind the flood situation this year especially with the help of climatic data collected from the Indian Metrological Department, flood related data from the Irrigation and Waterways Department, West Bengal and GPM (General Precipitation Measurement) data for rainfall analysis. Based on the threshold value derived from the calculation of the past available flood data, it is possible to predict the flood events which may occur in the near future and with the help of social media it can be spread out within a very short span of time to aware the mass. On a larger or a governmental scale, heightening the settlements situated on the either banks of the river can yield a better result than building up embankments.

Keywords: dam failure, embankments, flood, rainfall

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Authors: Dilson Subedi

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Current transformers are an integral part of power system because it provides a proportional safe amount of current for protection and measurement applications. However, due to upgradation of electromechanical relays to numerical relays and electromechanical energy meters to digital meters, the connected burden, which defines some of the CT characteristics, has drastically reduced. This has led to the system experiencing high currents damaging the connected relays and meters. Since the protection and metering equipment's are designed to withstand only certain amount of current with respect to time, these high currents pose a risk to man and equipment. Therefore, during such instances, the CT saturation characteristics have a huge influence on the safety of both man and equipment and on the reliability of the protection and metering system. This paper shows the effectiveness of a bank of parallel connected resistors, as a burden compensation technique, in compensating the burden of under-burdened CT’s. The response of the CT in the case of failure of one or more resistors at different levels of overcurrent will be captured using the LabVIEWTM data acquisition hardware (DAQ). The analysis is done on the real-time data gathered using LabVIEWTM. Variation of current transformer saturation characteristics with changes in burden will be discussed.

Keywords: accuracy limiting factor, burden, burden compensation, current transformer

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Authors: H. A. Erdogan

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Historical masonry arch bridges built centuries ago have fulfilled their function until recent decades. However, from the beginning of 20th century, these bridges have remained inadequate as a result of increasing speed, size and capacity of the means of transport. Although new bridges have been built in many places, masonry bridges located within the city limits still need to be used. When the size and transportation loads of modern vehicles are taken into account, it is apparent that historical masonry arch bridges would be exposed to greater loads than their initial design loads. Because of that, many precautions taken either remain insufficient or damage these bridges. In this study, the history of Debbaglar Bridge, one of the historic bridges located in the city center of Aksaray/Turkey is presented and its existing condition is evaluated. Structural analysis of the bridge under present conditions and loads is explained. Moreover, the retrofit and restoration application prepared considering the analysis data is described.

Keywords: adaptive re-use, Aksaray debbaglar bridge, masonry bridge, reconstruction

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Muhammad Tariq, Hammad Tahir, Fawwad Mahmood Butt

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Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model.

Keywords: forecasting, time series, auto regression, ARCH, ARMA

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Authors: Caroline Labib

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This paper reports on a qualitative study exploring the approaches teachers used to integrate computers or smart tablets into their program planning. Their aim was to integrate ICT into children’s play, thereby supporting children’s learning and development. Data was collected in preschool settings in Melbourne in 2016. Interviews with teachers, observations of teacher interactions with children and copies of teachers’ planning and observation documents informed the study. The paper looks closely at findings from two early childhood settings and focuses on exploring the differing approaches two EC teachers have adopted when integrating iPad or computers into their settings. Data analysis revealed three key approaches which have been labelled: free digital play, guided digital play and teacher-led digital use. Importantly, teacher decisions were influenced by the interplay between the opportunities that the ICT tools offered, the teachers’ prior knowledge and experience about ICT and children’s learning needs and contexts. This paper is a snapshot of two early childhood settings, and further research will encompass data from six more early childhood settings in Victoria with the aim of exploring a wide range of motivating factors for early childhood teachers trying to integrate ICT into their programs.

Keywords: early childhood education (ECE), digital play, information and communication technologies (ICT), play, and teachers' interaction approaches

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Authors: Ma. Rosario Concepcion O. Ang, Luis Caezar Ian K. Panganiban, Charmyne B. Mamador, Oliver Dan G. De Luna, Michael D. Bausas, Joselito P. Cruz

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PhilSHORE is a multi-site, multi-device and multi-criteria decision support tool designed to support the development of tidal current energy in the Philippines. Its platform is based on Geographic Information Systems (GIS) which allows for the collection, storage, processing, analyses and display of geospatial data. Combining GIS tools with open source web development applications, PhilSHORE becomes a webGIS-based marine spatial planning tool. To date, PhilSHORE displays output maps and graphs of power and energy density, site suitability and site-device analysis. It enables stakeholders and the public easy access to the results of tidal current energy resource assessments and site suitability analyses. Results of the initial development shows PhilSHORE is a promising decision support tool for ORE project developments.

Keywords: gis, site suitability analysis, tidal current energy resource assessment, webgis

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Authors: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

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The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

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Authors: Natasha Gabriella, Josef-Peter Schöggl, Alfred Posch

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Being a mature industry, pulp and paper industry (PPI) possess strength points coming from its existing infrastructure, technology know-how, and abundant availability of biomass. However, the declining trend of the wood-based products sales sends a clear signal to the industry to transform its business model in order to increase its profitability. With the emerging global attention on bio-based economy and circular economy, coupled with the low price of fossil feedstock, the PPI starts to integrate biorefinery as a value-added business model to keep the industry’s competitiveness. Nonetheless, biorefinery as an innovation exposes the PPI with some barriers, of which the uncertainty of the promising product becomes one of the major hurdles. This study aims to assess factors that affect the diffusion and development of forest biorefinery in the PPI, including drivers, barriers, advantages, disadvantages, as well as the most promising bio-based products of forest biorefinery. The study examines the identified factors according to the layer of business environment, being the macro-environment, industry, and strategic group level. Besides, an overview of future state of the identified factors is elaborated as to map necessary improvements for implementing forest biorefinery. A two-phase Delphi method is used to collect the empirical data for the study, comprising of an online-based survey and interviews. Delphi method is an effective communication tools to elicit ideas from a group of experts to further reach a consensus of forecasting future trends. Collaborating a total of 50 experts in the panel, the study reveals that influential factors are found in every layers of business of the PPI. The politic dimension is apparent to have a significant influence for tackling the economy barrier while reinforcing the environmental and social benefits in the macro-environment. In the industry level, the biomass availability appears to be a strength point of the PPI while the knowledge gap on technology and market seem to be barriers. Consequently, cooperation with academia and the chemical industry has to be improved. Human resources issue is indicated as one important premise behind the preceding barrier, along with the indication of the PPI’s resistance towards biorefinery implementation as an innovation. Further, cellulose-based products are acknowledged for near-term product development whereas lignin-based products are emphasized to gain importance in the long-term future.

Keywords: forest biorefinery, pulp and paper, bio-based product, Delphi method

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Authors: Roshanak Nouralian

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The primary focus of my Ph.D. dissertation revolves around the interconnection between language and culture, as well as the crucial role that language plays in facilitating communication and fostering integration within the host society for immigrants. This research specifically focuses on Iranian students studying at various universities in Vienna. Throughout this study, I have attempted to examine and analyze their challenges in various life situations in Austria. The broad dimensions of the research question led the research process to apply a constructivist grounded theory strategy. I have also used critical discourse analysis that is in line with constructivist GT's point of view to look closely at the borders, contradictions, and inequalities that came up in the participants' real-life experiences. Data from individual interviews and group discussions have expanded the research trajectory beyond disciplinary boundaries toward a transdisciplinary approach. The research findings indicate how the language policy of the host society leads to the establishment of power relationships and the arousal of a sense of cultural dominance among the research participants. This study investigates the problems experienced by participants in their daily interactions within the host society. Additionally, the results illustrate the development of a dependency relationship between participants and their host society despite linguistic policies that cause a sense of cultural hegemony. Conversely, the obtained data allowed me to examine the participants' language ideologies. The findings of this study show that social linguistics has the potential to go beyond the boundaries of its field. This is possible by using a variety of research strategies and analyzing people's real-life experiences to find out how language affects different parts of their daily lives. Therefore, in this conference, discussing the logic of employing a constructivist GT strategy along with critical discourse analysis (CDA) in this research, I intend to discuss the achieved results.

Keywords: cultural adapttaion, language policy, language ideology, cultural hegemony, transdisciplinary research, constructivist grounded theory, critical discourse analysis

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Nicholas Agyepong, Paul Gyan

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Inappropriate prescribing of antibiotic is a common healthcare concern globally resulted in an increased risk of adverse reactions and the emergence of antimicrobial resistance. The wrong antibiotic prescribing habits may lead to ineffective and unsafe treatment, worsening of disease condition, and thus increase in health care costs. The study was to examine the antibiotic prescribing pattern and associated risk factors at Regional Hospital in the Bono region of Ghana. A retrospective cross-sectional study was conducted to describe the current prescribing practices at the Hospital from January 2014 to December, 2021. A systematic random sampling method was used to select the participants for the study. STATA version 16 software was used for data management and analysis. Descriptive statistics and logistic regression analysis were used to analyze the data. Statistical significance set at p<0.05. Antibiotic consumption was equivalent to 11 per 1000 inhabitants consuming 1 DDD per day. Most common prescribed antibiotic was amoxicillin/clavulanic acid (14.39%) followed by erythromycin (11.44%), and ciprofloxacin (11.36%). Antibiotics prescription have been steadily increased over the past eight years (2014: n=59,280 to 2021: n=190,320). Prescribers above the age of 35 were more likely to prescribe antibiotics than those between the ages of 20 and 25 (COR=21.00; 95% CI: 1.78 – 48.10; p=0.016). Prescribers with at least 6 years of experience were also significantly more likely to prescribe antibiotics than those with at most 5 years of experience (COR=14.17; 95% CI: 2.39 – 84.07; p=0.004). Thus, the establishment of an antibiotic stewardship program in the hospitals is imperative, and further studies need to be conducted in other facilities to establish the national antibiotic prescription guideline.

Keywords: antibiotic, antimicrobial resistance, prescription, prescribers

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