Search results for: risk prediction model

Authors: Ahmed H. Abdullah, Pavel Kroupa

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Globular clusters (GC) are important objects for tracing the early evolution of a galaxy. We study the correlation between the cluster population and the global properties of the host galaxy. We found that the correlation between cluster population (NGC) and the baryonic mass (Mb) of the host galaxy are best described as 10 −5.6038Mb. In order to understand the origin of the U -shape relation between the GC specific frequency (SN) and Mb (caused by the high value of SN for dwarfs galaxies and giant ellipticals and a minimum SN for intermediate mass galaxies≈ 1010M), we derive a theoretical model for the specific frequency (SNth). The theoretical model for SNth is based on the slope of the power-law embedded cluster mass function (β) and different time scale (Δt) of the forming galaxy. Our results show a good agreement between the observation and the model at a certain β and Δt. The model seems able to reproduce higher value of SNth of β = 1.5 at the midst formation time scale.

Keywords: galaxies: dwarf, globular cluster: specific frequency, number of globular clusters, formation time scale

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Authors: Florian Pfeffel, Valentin Nickolai, Christian Louis Kühner

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Digitalization has disrupted the traditional workplace environment by allowing many employees to work from anywhere at any time. This trend of working from home was further accelerated due to the COVID-19 crisis, which forced companies to rethink their workplace models. While in many companies, this shift happened out of pure necessity; many employees were left more satisfied with their job due to the opportunity to work from home. This study focuses on employees’ job satisfaction in the service sector in dependence on the different work models, which are defined as a “work from home” model, the traditional “work in office” model, and a hybrid model. Using structural equation modeling (SEM), these three work models have been analyzed based on 13 influencing factors on job satisfaction that have been further summarized in the three groups “classic influencing factors”, “influencing factors changed by remote working”, and “new remote working influencing factors”. Based on the influencing factors on job satisfaction, a survey has been conducted with n = 684 employees in the service sector. Cronbach’s alpha of the individual constructs was shown to be suitable. Furthermore, the construct validity of the constructs was confirmed by face validity, content validity, convergent validity (AVE > 0.5: CR > 0.7), and discriminant validity. Additionally, confirmatory factor analysis (CFA) confirmed the model fit for the investigated sample (CMIN/DF: 2.567; CFI: 0.927; RMSEA: 0.048). The SEM-analysis has shown that the most significant influencing factor on job satisfaction is “identification with the work” with β = 0.540, followed by “Appreciation” (β = 0.151), “Compensation” (β = 0.124), “Work-Life-Balance” (β = 0.116), and “Communication and Exchange of Information” (β = 0.105). While the significance of each factor can vary depending on the work model, the SEM-analysis shows that the identification with the work is the most significant factor in all three work models and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that employees who work entirely remotely or have a hybrid work model are significantly more satisfied with their job, with a job satisfaction score of 5.0 respectively on a scale from 1 (very dissatisfied) to 7 (very satisfied), than employees do not have the option to work from home with a score of 4.6. This comes as a result of the lower identification with the work in the model without any remote working. Furthermore, the responses indicate that it is important to consider the individual preferences of each employee when it comes to the work model to achieve overall higher job satisfaction. Thus, it can be argued that companies can profit off of more motivation and higher productivity by considering the individual work model preferences, therefore, increasing the identification with the respective work.

Keywords: home-office, identification with work, job satisfaction, new work, remote work, structural equation modeling

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Authors: Bouneua Khamphilavanh, Toshihiko Masui

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The penetration of EV (electric vehicle) technology in Lao road transportation, in this study, was analyzed by using the AIM/CGE [Laos] model. The computable general equilibrium (CGE) model was developed by the Asia-Pacific Integrated Model (AIM) team. In line with the increase of the number of road vehicles, the energy demand in the transport sector has been gradually increased which resulted in a large amount of budget spent for importing fossil fuels during the last decade, and a high carbon dioxide emission from the transport sector, hence the aim of this research is to analyze the impact of EVs penetration on economic and CO₂ emission in short-term, middle-term, and long-term. By the year 2050, the expected gross domestic product (GDP) value, due to Laos will spend more budget for importing the EV, will be gradually lost up to one percent. The cumulative CO₂ emission from 2020 to 2050 in BAU case will be 12,000 GgCO₂eq, and those in the EV mitigation case will be 9,300 GgCO₂eq, which accounting for likely 77% cumulative CO₂ emission reduction in the road transport sector by introducing the EV technology.

Keywords: GDP, CO₂ mitigation, CGE model, EV technology, transport

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Authors: Nabeeha Hassan Abdel Jalil, Robert Barigye, Hamda Al Alawi, Afra Al Dhaheri, Fatma Graiban Al Muhairi, Maryam Al Khateri, Nouf Al Alalawi, Susan Olet, Khaja Mohteshamuddin, Ahmad Al Aiyan, Mohamed Elfatih Hamad

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A serological survey was carried out to determine the seroprevalence of Brucella spp, Coxiella burnetii, Chlamydophila abortus, and Toxoplasma gondii in sheep and goat herds in the UAE. A total of 915 blood samples [n= 222, [sheep]; n= 215, [goats]) were collected from livestock farms in the Emirates of Abu Dhabi, Dubai, Sharjah and Ras Al-Khaimah (RAK). An additional 478 samples (n= 244, [sheep]; n= 234, (goats]) were collected from the Al Ain livestock central market and tested by indirect ELISA for pathogen-specific antibodies with the Brucella antibodies being further corroborated by the Rose-Bengal agglutination test. Seropositivity for the four pathogens is variably documented in sheep and goats from the study area. Respectively, the overall livestock farm prevalence rates for Brucella spp, C. burnetii, C. abortus, and T. gondii were 2.7%, 27.9%, 8.1%, and 16.7% for sheep, and 0.0%, 31.6%, 9.3%, and 5.1% for goats. Additionally, the seroprevalence rates Brucella spp, C. burnetii, C. abortus, and T. gondii in samples from the livestock market were 7.4%, 21.7%, 16.4%, and 7.0% for sheep, and 0.9%, 32.5%, 19.2%, and 11.1% for goats respectively. Overall, sheep had 12.59 more chances than goats of testing seropositive for Brucella spp (OR, 12.59 [95% CI 2.96-53.6]) but less likely to be positive for C. burnetii-antibodies (OR, 0.73 [95% CI 0.54-0.97]). Notably, the differences in the seroprevalence rates of C. abortus and T. gondii in sheep and goats were not statistically significant (p > 0.0500). The present data indicate that all the four study pathogens are present in sheep and goat populations in the UAE where coxiellosis is apparently the most seroprevalent followed by chlamydophilosis, toxoplasmosis, and brucellosis. While sheep from the livestock market were more likely than those from farms to be Brucella-seropositive than those, the overall exposure risk of C. burnetii appears to be greater for goats than sheep. As more animals from the livestock market were more likely to be seropositive to Chlamydophila spp, it is possible that under the UAE animal production conditions, at least, coxiellosis and chlamydophilosis are more likely to increase the culling rate of domesticated small ruminants than toxoplasmosis and brucellosis. While anecdotal reports have previously insinuated that brucellosis may be a significant animal health risk in the UAE, the present data suggest C. burnetii, C. abortus and T. gondii to be more significant pathogens of sheep and goats in the country. Despite this possibility, the extent to which these pathogens may nationally be contributing to reproductive failure in sheep and goat herds is not known and needs to be investigated. Potentially, these agents may also carry a potentially zoonotic risk that needs to be investigated in risk groups like farm workers, and slaughter house personnel. An ongoing study is evaluating the seroprevalence of bovine coxiellosis in the Emirate of Abu Dhabi and the data thereof will further elucidate on the broader epidemiological dynamics of the disease in the national herd.

Keywords: Brucella spp, Chlamydophila abortus, goat, sheep, Toxoplasma gondii, UAE

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Authors: Jonathan Soliman, Thomas Cavasino, Virginie Pommelet, Lahouari Amor, Pierre Mornand, Simon Escoda, Nina Droz, Soraya Matczak, Julie Toubiana, François Angoulvant, Etienne Carbonnelle, Albert Faye, Loic de Pontual, Luu-Ly Pham

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Background: Typhoid and paratyphoid fever are systemic infections caused by Salmonella enterica serovar Typhi or paratyphi (A, B, C). Children traveling to tropical areas are at risk to contract these diseases which can be complicated. Methods: Clinical, biological and bacteriological data were collected from 78 pediatric cases reported between 2000 and 2017 in six Parisian hospitals. Children aged 0 to 18 years old, with a diagnosis of typhoid or paratyphoid fever confirmed by bacteriological exams, were included. Epidemiologic, clinical, biological features and presence of multidrug-resistant (MDR) bacteria or intermediate susceptibility to ciprofloxacin (nalidixic acid resistant) were examined by univariate analysis and by logistic regression analysis to identify risk factors of severe typhoid in children. Results: 84,6% of the children were imported cases of typhoid fever (n=66/78) and 15,4% were autochthonous cases (n=12/78). 89,7% were caused by S.typhi (n=70/78) and 12,8% by S.paratyphi (n=10/78) including 2 co-infections. 19,2% were intrafamilial cases (n=15/78). Median age at diagnosis was 6,4 years-old [6 months-17,9 years]. 28,2% of the cases were complicated forms (n=22/78): digestive (n=8; 10,3%), neurological (n=7; 9%), pulmonary complications (n=4; 5,1%) and hemophagocytic syndrome (n=4; 5,1%). Only 5% of the children had prior immunization with typhoid non-conjugated vaccine (n=4/78). 28% of the cases (n=22/78) were caused by resistant bacteria. Thrombocytopenia and diagnosis delay was significantly associated with severe infection (p= 0.029 and p=0,01). Complicated forms were more common with MDR (p=0,1) and not statistically associated with a young age or sex in this study. Conclusions: Typhoid and paratyphoid fever are not rare in children back from tropical areas. This multicentric pediatric study seems to show that thrombocytopenia, diagnosis delay, and multidrug resistant bacteria are associated with severe typhoid fever and complicated forms in children.

Keywords: antimicrobial resistance, children, Salmonella enterica typhi and paratyphi, severe typhoid

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Authors: Andrey Matyukhin

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Russian official statistics have been showing a steady decline in residential real estate prices for several consecutive years. Price risk in real estate markets is thus affecting various groups of economic agents, namely, individuals, construction companies and financial institutions. Potential use of property derivatives might help mitigate adverse consequences of negative price dynamics. Unless a sustainable price indicator is developed, settlement of such instruments imposes constraints on counterparties involved while imposing restrictions on real estate market development. The study addresses geographical and classification heterogeneity in real estate prices by means of variance analysis in various groups of real estate properties. In conclusion, we determine optimal sample structure of representative real estate assets with sufficient level of price homogeneity. The composite price indicator based on the sample would have a higher level of robustness and reliability and hence improving liquidity in the market for property derivatives through underlying standardization. Unlike the majority of existing real estate price indices, calculated on country-wide basis, the optimal indices for Russian market shall be constructed on the city-level.

Keywords: price homogeneity, property derivatives, real estate price index, real estate price risk

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Authors: Soledad Natalia M. Dalisay, Mylene De Guzman

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In archipelagic countries like the Philippines, many communities thrive along coastal areas. The sea is the community members’ main source of livelihood and the site of many cultural activities. For these communities, the sea is their life and livelihood. Nevertheless, the sea also poses a hazard during the rainy season when typhoons frequent their communities. Coastal communities often encounter threats from storm surges and flooding that are common when there are typhoons. During such periods, disaster evacuation programs are implemented. However, in many instances, evacuation has been the bane of local government officials implementing such programs in their communities as resistance from community members is often encountered. Such resistance is often viewed by program implementers as due to the fact that people were hard headed and ignorant of the potential impacts of living in hazard prone areas. This paper argues that it is not for these reasons that people refused to evacuate. Drawing from data collected from fieldwork done in three sites in Central Philippines affected by super typhoon Haiyan, this study aimed to provide a contextualized understanding of peoples’ refusal to heed disaster evacuation warnings. This study utilized the multi-sited ethnography approach with in-depth episodic interviews, focus group discussions, participatory risk mapping and key informant interviews in gathering data on peoples’ experiences and insights specifically on evacuation during typhoon Haiyan. This study showed that people have priorities and considerations vital in their social lives that they are protecting in their refusal to leave their homes for pre-emptive evacuation. It is not that they are not aware of the risks when the face the hazard. It is more that they had faith in the local knowledge and strategies that they have developed since the time of their ancestors as a result of living and engaging with hazards in their areas for as long as they could remember. The study also revealed that risk in encounters with hazards was gendered. Furthermore, previous engagement with local government officials and the manner in which the pre-emptive evacuation programs were implemented had cast doubt on the value of such programs in saving their lives. Life in the designated evacuation areas can be as dangerous if not more compared with living in their coastal homes. There seems to be the impression that in the evacuation program of the government, people were being moved from hazard zones to death zones. Thus, this paper ends with several recommendations that may contribute to building more responsive evacuation programs that aim to build people’s resilience while taking into consideration the local moral world in communities in identified hazard zones.

Keywords: coastal communities, disaster evacuation, disaster risk perception, social and cultural responses to hazards

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Authors: Muhammad Kamran Hanif Khan, Fahad Mushtaq

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Background: In both developed and underdeveloped countries, coronary artery disease (CAD) is a serious cause of death and disability. Cardiovascular disease (CVD) is becoming more prevalent in emerging countries like Pakistan due to the spread and acceptance of Western lifestyles. Material and Methods: An observational cross-sectional investigation was conducted, and data collection relied on a random cluster sampling method. The sample size for this cross-sectional study was calculated using the following factors: estimated true proportion of 17.5%, desired precision of 2%, and confidence interval of 95%. The data for this study was collected from a sample of 1387 adults. Results: The average age of those living in rural areas is 55.24 years, compared to 52.60 years for those living in urban areas. The mean fasting blood glucose of the urban participants is 105.28 mg/dL, which is higher than the mean fasting blood glucose of the rural participants, which is 102.06 mg/dL. The mean total cholesterol of the urban participants is 192.20 mg/dL, which is slightly higher than the mean total cholesterol of the rural participants, which is 191.97 mg/dL. CAD prevalence is greater in urban areas than in rural areas. ECG abnormalities prevalence is 16.1% in females compared to 12.5% in men. Conclusion: The prevalence of CAD is more common in urban areas than in rural ones for all of the measures of CAD used in the study.

Keywords: CVD prevalence, CVD risk factors, rural area, urban area

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Authors: Hongyun Li, Zhibin Jiang

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The travel of passengers in the urban rail transit network is influenced by changes in network structure and operational status, and the response of individual travel preferences to these changes also varies. Firstly, the influence of the suspension of urban rail transit line sections on passenger travel along the line is analyzed. Secondly, passenger travel trajectories containing multi-dimensional semantics are described based on network UD data. Next, passenger panel data based on spatio-temporal sequences is constructed to achieve frequent passenger clustering. Then, the Graph Convolutional Network (GCN) is used to model and identify the changes in travel modes of different types of frequent passengers. Finally, taking Shanghai Metro Line 11 as an example, the travel behavior patterns of frequent passengers after the Huaqiao section shutdown during the COVID-19 epidemic are analyzed. The results showed that after the section shutdown, most passengers would transfer to the nearest Anting station for boarding, while some passengers would transfer to other stations for boarding or cancel their travels directly. Among the passengers who transferred to Anting station for boarding, most of passengers maintained the original normalized travel mode, a small number of passengers waited for a few days before transferring to Anting station for boarding, and only a few number of passengers stopped traveling at Anting station or transferred to other stations after a few days of boarding on Anting station. The results can provide a basis for understanding urban rail transit passenger travel patterns and improving the accuracy of passenger flow prediction in abnormal operation scenarios.

Keywords: urban rail transit, section shutdown, frequent passenger, travel behavior pattern

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Javier Fernandez, Mayte Medina, Rafael Fernandez de Canete, Nuria Alcain, Juan Carlos Ramos-Diaz

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At present, the experimentation of anesthetic drugs on patients requires a regulation protocol, and the response of each patient to several doses of entry drug must be well known. Therefore, the development of pharmacological dose control systems is a promising field of research in anesthesiology. In this paper, it has been developed a non-linear compartmental the pharmacokinetic-pharmacodynamical model which describes the anesthesia depth effect in a sufficiently reliable way over a set of patients with the depth effect quantified by the Bi-Spectral Index. Afterwards, an Artificial Neural Network (ANN) predictive controller has been designed based on the depth of anesthesia model so as to keep the patient in the optimum condition while he undergoes surgical treatment. For the purpose of quantifying the efficiency of the neural predictive controller, a classical proportional-integral-derivative controller has also been developed to compare both strategies. Results show the superior performance of predictive neural controller during BiSpectral Index reference tracking.

Keywords: anesthesia, bi-spectral index, neural network control, pharmacokinetic-pharmacodynamical model

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Authors: Pui-Lai To, Chechen Liao, Tzu-Ling Lin

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Virtual communities have created a range of new social spaces in which to meet and interact with one another. Both as a stand-alone model or as a supplement to sustain competitive advantage for normal business models, building virtual communities has been hailed as one of the major strategic innovations of the new economy. However for a virtual community to evolve, the biggest challenge is how to make members actively give information or provide advice. Even in busy virtual communities, usually, only a small fraction of members post information actively. In order to investigate the determinants of information giving willingness of those contributors who usually actively provide their opinions, we proposed a model to understand the reasons for contribution in communities. The study will definitely serve as a basis for the future growth of information giving in virtual communities.

Keywords: information giving, social identity, trust, virtual community

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Authors: Taiki Baba, Tomoaki Hashimoto

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The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization.

Keywords: model predictive control, stochastic systems, probabilistic constraints, random dither quantization

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Authors: Maryam Maleki, Esther Mead, Mohammad Arani, Nitin Agarwal

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This study is conducted to examine how opposing viewpoints related to COVID-19 were diffused on Twitter. To accomplish this, six datasets using two epidemiological models, SIR (Susceptible, Infected, Recovered) and SEIZ (Susceptible, Exposed, Infected, Skeptics), were analyzed. The six datasets were chosen because they represent opposing viewpoints on the COVID-19 pandemic. Three of the datasets contain anti-subject hashtags, while the other three contain pro-subject hashtags. The time frame for all datasets is three years, starting from January 2020 to December 2022. The findings revealed that while both models were effective in evaluating the propagation trends of these polarizing viewpoints, the SEIZ model was more accurate with a relatively lower error rate (6.7%) compared to the SIR model (17.3%). Additionally, the relative error for both models was lower for anti-subject hashtags compared to pro-subject hashtags. By leveraging epidemiological models, insights into the propagation trends of polarizing viewpoints on Twitter were gained. This study paves the way for the development of methods to prevent the spread of ideas that lack scientific evidence while promoting the dissemination of scientifically backed ideas.

Keywords: mathematical modeling, epidemiological model, seiz model, sir model, covid-19, twitter, social network analysis, social contagion

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Authors: Justin Zhengjie Tan, Yang Zhao

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Adiabatic Rapid Passage, a popular method of achieving population inversion, is studied in a Rabi dimer model in the presence of noise which acts as a dissipative environment. The integration of the multi-Davydov D2 Ansatz into the time-dependent variational framework enables us to model the intricate quantum system accurately. By influencing the system with a driving field strength resonant with the energy spacing, the probability of adiabatic rapid passage, which is modelled after the Landau Zener model, can be derived along with several other observables, such as the photon population. The effects of a dissipative environment can be reproduced by coupling the system to a common phonon mode. By manipulating the strength and frequency of the driving field, along with the coupling strength of the phonon mode to the qubits, we are able to control the qubits and photon dynamics and subsequently increase the probability of Adiabatic Rapid Passage happening.

Keywords: quantum electrodynamics, adiabatic rapid passage, Landau-Zener transitions, dissipative environment

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Authors: E. D. Paul, O. F. Paul, J. E. Toryila, A. J. Salifu, C. E. Gimba

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Adsorption of Cd2+ ions from aqueous solution by Chitosan, a natural polymer, obtained from a mixture of the exoskeletons of Littorina littorea (Periwinkle) and Achatinoidea (Snail) was studied at varying adsorbent dose, contact time, metal ion concentrations, temperature and pH using batch adsorption method. The equilibrium adsorption isotherms were determined between 298 K and 345 K. The adsorption data were adjusted to Langmuir, Freundlich and the pseudo second order kinetic models. It was found that the Langmuir isotherm model most fitted the experimental data, with a maximum monolayer adsorption of 35.1 mgkg⁻¹ at 308 K. The entropy and enthalpy of adsorption were -0.1121 kJmol⁻¹K⁻¹ and -11.43 kJmol⁻¹ respectively. The Freundlich adsorption model, gave Kf and n values consistent with good adsorption. The pseudo-second order reaction model gave a straight line plot with rate constant of 1.291x 10⁻³ kgmg⁻¹ min⁻¹. The qe value was 21.98 mgkg⁻¹, indicating that the adsorption of Cadmium ion by the chitosan composite followed the pseudo-second order kinetic model.

Keywords: adsorption, chitosan, littorina littorea, achatinoidea, natural polymer

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

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Authors: Hsuan Yu Lai, Hsuan Hsuan Chang

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The consumer decision process steps through problems by weighing evidence, examining alternatives, and choosing a decision path. Currently, the COVID 19 made the tourism industry encounter a huge challenge and suffer the biggest amount of economic loss. It would be very important to reexamine the decision-making process model, especially after the pandemic, and consider the differences among different generations. The tourism industry has been significantly impacted by the global outbreak of COVID-19, but as the pandemic subsides, the sector is recovering. This study addresses the scarcity of research on travel decision-making patterns among generations in Taiwan. Specifically targeting individuals who frequently traveled abroad before the pandemic, the study explores differences in decision-making at different stages post-outbreak. So this study investigates differences in travel decision-making among individuals from different generations during/after the COVID-19 pandemic and examines the moderating effects of social media usage and individuals' perception of health risks. The study hypotheses are “there are significant differences in the decision-making process including travel motivation, information searching preferences, and criteria for decision-making” and that social-media usage and health-risk perception would moderate the results of the previous study hypothesis. The X, Y, and Z generations are defined and categorized based on a literature review. The survey collected data including their social-economic background, travel behaviors, motivations, considerations for destinations, travel information searching preferences, and decision-making criteria before/after the pandemic based on the reviews of previous studies. Data from 656 online questionnaires were collected between January to May 2023 and from Taiwanese travel consumers who used to travel at least one time abroad before Covid-19. SPSS is used to analyze the data with One-Way ANOVA and Two-Way ANOVA. The analysis includes demand perception, information gathering, alternative comparison, purchase behavior, and post-travel experience sharing. Social media influence and perception of health risks are examined as moderating factors. The findings show that before the pandemic, the Y Generation preferred natural environments, while the X Generation favored historical and cultural sites compared to the Z Generation. However, after the outbreak, the Z Generation displayed a significant preference for entertainment activities. This study contributes to understanding changes in travel decision-making patterns following COVID-19 and the influence of social media and health risks. The findings have practical implications for the tourism industry.

Keywords: consumer decision-making, generation study, health risk perception, post-pandemic era, social media

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Authors: Amin Padash, Gholam Reza Nabi Bid Hendi, Hassan Hoveidi

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Purpose: The aim of this research is developing a model for green management based on Health, Safety and Environmental Management System. An HSE-MS can be a powerful tool for organizations to both improve their environmental, health and safety performance, and enhance their business efficiency to green management. Model: The model is developed in this study can be used for industries as guidelines for implementing green management issue by considering Health, Safety and Environmental Management System. Case Study: The Pars Special Economic / Energy Zone Organization on behalf of Iran’s Petroleum Ministry and National Iranian Oil Company (NIOC) manages and develops the South and North oil and gas fields in the region. Methodology: This research according to objective is applied and based on implementing is descriptive and also prescription. We used technique MCDM (Multiple Criteria Decision-Making) for determining the priorities of the factors. Based on process approach the model consists of the following steps and components: first factors involved in green issues are determined. Based on them a framework is considered. Then with using MCDM (Multiple Criteria Decision-Making) algorithms (TOPSIS) the priority of basic variables are determined. The authors believe that the proposed model and results of this research can aid industries managers to implement green subjects according to Health, Safety and Environmental Management System in a more efficient and effective manner. Finding and conclusion: Basic factors involved in green issues and their weights can be the main finding. Model and relation between factors are the other finding of this research. The case is considered Petrochemical Company for promoting the system of ecological industry thinking.

Keywords: Fuzzy-AHP method , green management, health, safety and environmental management system, MCDM technique, TOPSIS

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Authors: Nurulhuda A. Manaf, Nor Najihah Zainal Abidin, Nur Amalina Jamaludin

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Coordinating service interactions are a vital part of developing distributed applications that are built up as networks of autonomous participants, e.g., software components, web services, online resources, involve a collaboration between a diverse number of participant services on different providers. The complexity in coordinating service interactions reflects how important the techniques and approaches require for designing and coordinating the interaction between participant services to ensure the overall goal of a collaboration between participant services is achieved. The objective of this research is to develop capability of steering a complex service interaction towards a desired outcome. Therefore, an efficient technique for modelling, generating, and verifying the coordination of service interactions is developed. The developed model describes service interactions using service choreographies approach and focusing on a declarative approach, advocating an Object Management Group (OMG) standard, Semantics of Business Vocabulary and Rules (SBVR). This model, namely, SBVR model for service choreographies focuses on a declarative deontic rule expressing both obligation and prohibition, which can be more useful in working with coordinating service interactions. The generated SBVR model is then be formulated and be transformed into Alloy model using Alloy Analyzer for verifying the generated SBVR model. The transformation of SBVR into Alloy allows to automatically generate the corresponding coordination of service interactions (service choreography), hence producing an immediate instance of execution that satisfies the constraints of the specification and verifies whether a specific request can be realised in the given choreography in the generated choreography.

Keywords: service choreography, service coordination, behavioural modelling, complex interactions, declarative specification, verification, model transformation, semantics of business vocabulary and rules, SBVR

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Authors: Natasha Goh, Sebastian Brown

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Purpose: This case report describes an unusual presentation of uveal melanoma. Method: Case notes, imaging, and histopathological specimen were reviewed for this case report. Result: The patient is a 62-year-old lady of Chinese heritage who had been receiving follow-up at the eye clinic of a tertiary hospital. She had a longstanding history of poor vision in her right eye after sustaining trauma to the eye at age 3. She was found to have a carotid-cavernous sinus fistula in the right eye in 2009 and underwent stenting in China. Unfortunately, this was unsuccessful and resulted in a painful blind eye. She had represented with headaches, worsening eye pain, and ptosis in Sydney in 2016. Her CT angiogram showed a calcified vascular structure in the orbit and globe, and she was offered a digital subtraction angiography by the neurosurgical team, which she ultimately declined. She had since been followed up at the eye clinic for the pthisical eye. Due to chronic ocular pain and recurrent conjunctivitis, the decision was made for an evisceration in 2021. The specimen was sent for routine histopathological examination and returned positive for uveal melanoma. The patient was subsequently referred to a melanoma center for further follow-up, which comprised serial imaging and radiotherapy treatment. Conclusion: Clinicians should bear in mind that uveal melanomas may present in a longstanding phthisical eye and in patients with no or little apparent risk factors.

Keywords: uveal melanoma, pthisical eye, carotid cavernous fistula, uveal melanoma risk factors

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Authors: Dhaladhuli Pranavi, Amirtham Rajagopal

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There are various failure modes in ceramic matrix composites; notable ones are fiber breakage, matrix cracking and fiber matrix debonding. Crack nucleation and propagation in microstructure of such composites requires an understanding of interaction of crack with the multiple inclusion heterogeneous system and interfaces. In order to assess structural integrity, the material parameters especially of the interface that governs the crack growth should be determined. In the present work, a nonlocal phase field approach is proposed to model the crack interface interaction in such composites. Nonlocal approaches help in understanding the complex mechanisms of delamination growth and mitigation and operates at a material length scale. The performance of the proposed formulation is illustrated through representative numerical examples. The model proposed is implemented in the framework of the finite element method. Several parametric studies on interface crack interaction are conducted. The proposed model is easy and simple to implement and works very well in modeling fracture in composite systems.

Keywords: composite, interface, nonlocal, phase field

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Authors: Kassahun Birhanu Tadesse, Megersa Olumana Dinka

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Reliable future river flow information is basic for planning and management of any river systems. For data scarce river system having only a river flow records like the Waterval River, a univariate time series models are appropriate for river flow forecasting. In this study, a univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied for forecasting Waterval River flow using GRETL statistical software. Mean monthly river flows from 1960 to 2016 were used for modeling. Different unit root tests and Mann-Kendall trend analysis were performed to test the stationarity of the observed flow time series. The time series was differenced to remove the seasonality. Using the correlogram of seasonally differenced time series, different SARIMA models were identified, their parameters were estimated, and diagnostic check-up of model forecasts was performed using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AIc) and Hannan-Quinn (HQc) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 was selected as the best model for Waterval River flow forecasting. Therefore, this model can be used to generate future river information for water resources development and management in Waterval River system. SARIMA model can also be used for forecasting other similar univariate time series with seasonality characteristics.

Keywords: heteroscedasticity, stationarity test, trend analysis, validation, white noise

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Authors: Areej Isam Barham

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Developing students’ number sense is an essential element in the learning of mathematics. Number sense is one of the foundational ideas in mathematics where students need to understand numbers, representing them in different ways, and realize the relationships among numbers. Number sense also reflects students’ understanding of the meaning of operations, how they related to one another, how to compute fluently and make reasonable estimates. Developing students’ number sense in the mathematics classroom requires good preparation for mathematics teachers, those who will direct their students towards the real understanding of numbers and its implementation in the learning of mathematics. This study describes the development of elementary prospective mathematics teachers’ number sense through a mathematics teaching methods course at Qatar University. The study examined the effect of using the Lesh model in enhancing mathematics prospective teachers’ number sense. Thirty-nine elementary prospective mathematics teachers involved in the current study. The study followed an experimental research approach, and quantitative research methods were used to answer the research questions. Pre-post number sense test was constructed and implemented before and after teaching by using the Lesh model. Data were analyzed using Statistical Packages for Social Sciences (SPSS). Descriptive data analysis and t-test were used to examine the impact of using the Lesh model in enhancing prospective teachers’ number sense. Finding of the study indicated poor number sense and limited numeracy skills before implementing the use of the Lesh model, which highly demonstrate the importance of the study. The results of the study also revealed a positive impact on the use of the Lesh model in enhancing prospective teachers’ number sense with statistically significant differences. The discussion of the study addresses different features and issues related to the participants’ number sense. In light of the study, the research presents recommendations and suggestions for the future development of mathematics prospective teachers’ number sense.

Keywords: number sense, Lesh model, prospective mathematics teachers, development of number sense

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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: Rekab Djabri Hamza, Daoud Salah

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We report first-principles calculations of structural and magnetic properties of TmAs compound in zinc blende(B3) and CsCl(B2), structures employing the density functional theory (DFT) within the local density approximation (LDA). We use the full potential linear muffin-tin orbitals (FP-LMTO) as implemented in the LMTART-MINDLAB code (Calculation). Results are given for lattice parameters (a), bulk modulus (B), and its first derivatives(B’) in the different structures NaCl (B1) and CsCl (B2). The most important result in this work is the prediction of the possibility of transition; from cubic rocksalt (NaCl)→ CsCl (B2) (32.96GPa) for TmAs. These results use the LDA approximation.

Keywords: LDA, phase transition, properties, DFT

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Authors: Sila Akhan, Muge Toygar, Murat Sayan, Simge Fidan

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Chronic viral hepatitis B, C, and D can cause hepatocellular carcinoma (HCC), cirrhosis and death. The proper treatment reduce the risk of development of HCC importantly, but not to zero point. Materials and Methods: We analysed retrospectively our chronic viral hepatitis B, C and D patients who attended to our Infectious Diseases policlinic between 2004-2018. From 589 biopsy-proven chronic hepatitis patients 3 have hepatocellular carcinoma on our follow up. First case is 74 years old patient. His HCV infection diagnosis was made 8 years ago. First treatment was pegylated interferon plus ribavirin only 28 weeks, because of HCV RNA breakthrough under treatment. In 2013 he was retreated with telaprevir, pegylated interferon plus ribavirin 24 weeks. But at the end of the therapy HCV RNA was found 1.290.000 IU/mL. He has abdominal ultrasonography (US) controls and alpha-fetoprotein (AFP) at 6 months intervals. All seemed normal until 2015 then he has an abdominal magnetic resonance imaging (MRI) and found HCC by chance. His treatment began in Oncology Clinic after verified with biopsy of HCC. And then sofosbuvir/ledipasvir was given to him for HCV 24 weeks. Sustained virologic response (SVR) was obtained. He is on cure for HCV infection and under control of Oncology for HCC. Second patient is 36 years old man. He knows his HBV infection since 2008. HBsAg and HBeAg positive; HDV RNA negative. Liver biopsy revealed grade:4, stage 3-4 according modified Knodell scoring system. In 2010 tenofovir treatment was began. His abdominal US and AFP were normal. His controls took place at 6 months intervals and HBV DNA negative, US, and AFP were normal until 2016 continuously. AFP found 37 above the normal range and then HCC was found in MRI. Third patient is 57 years old man. As hepatitis B infection was first diagnosed; he has cirrhosis and was began tenofovir as treatment. In short time he has HCC despite normal AFP values. Conclusion: In Mediterranian countries including Turkey naturally occurring pre-S/S variants are more than 75% of all chronic hepatitis B patients. This variants may contribute to the development of progressive liver damage and hepatocarcinogenesis. HCV-induced development of HCC is a gradual process and is affected by the duration of disease and viral genotype. All the chronic viral hepatitis patients should be followed up in 6 months intervals not only with US and AFP for HCC. Despite they have proper treatment there is always the risk development of HCC. Chronic hepatitis patients cannot be dropped from follow up even treated well.

Keywords: HCC, HCV, HBV, DAA

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Authors: Mili Dutta, Lokender Prashad

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Introduction: Population ageing is a phenomenon that can be observed around the globe. The health-related quality of life (HRQOL) is a measurement of health status of an individual, and it describes the effect of physical and mental health disorders on the well-being of a person. The present study is aimed to describe the influence of socio-economic characteristics of elderly on their health-related quality of life in India. Methods: EQ-5D instrument and population-based EQ-5D index score has been measured to access the HRQOL among elderly. Present study utilized the Study on Global Ageing and Adult Health (SAGE) data which was conducted in 2007 in India. Multiple Logistic Regression model and Multivariate Linear Regression model has been employed. Result: In the present study, it was found that the female are more likely to have problems in mobility (OR=1.41, 95% Cl: 1.14 to 1.74), self-care (OR=1.26, 95% Cl: 1.01 to 1.56) and pain or discomfort (OR=1.50, 95% Cl: 1.16 to 1.94). Elderly residing in rural area are more likely to have problems in pain/discomfort (OR=1.28, 95% Cl: 1.01 to 1.62). More older and non-working elderly are more likely whereas higher educated and highest wealth quintile elderly are less likely to have problems in all the dimensions of EQ-5D viz. mobility, self-care, usual activity, pain/discomfort and anxiety/depression. The present study has also shown that oldest old people, residing in rural area and currently not working elderly are more likely to report low EQ-5D index score whereas elderly with high education level and high wealth quintile are more likely to report high EQ-5D index score than their counterparts. Conclusion: The present study has found EQ-5D instrument as the valid measure for assessing the HRQOL of elderly in India. The study indicates socio-economic characteristics of elderly such as female, more older people, residing in rural area, non-educated, poor and currently non-working as the major risk groups of having poor HRQOL in India. Findings of the study will be helpful for the programmes and policy makers, researchers, academician and social workers who are working in the field of ageing.

Keywords: ageing, HRQOL, India, EQ-5D, SAGE, socio-economic characteristics

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Authors: S. K. Ashiquer Rahman

Abstract:

Recently, oil has become more influential in almost every economic sector as a key material. As can be seen from the news, when there are some changes in an oil price or OPEC announces a new strategy, its effect spreads to every part of the economy directly and indirectly. That’s a reason why people always observe the oil price and try to forecast the changes of it. The most important factor affecting the price is its supply which is determined by the number of wildcats drilled. Therefore, a study about the number of wellheads and other economic variables may give us some understanding of the mechanism indicated by the amount of oil supplies. In this paper, we will consider a relationship between the number of wellheads and three key factors: the price of the wellhead, domestic output, and GNP constant dollars. We also add trend variables in the models because the consumption of oil varies from time to time. Moreover, this paper will use an econometrics method to estimate parameters in the model, apply some tests to verify the result we acquire, and then conclude the model.

Keywords: price, domestic output, GNP, trend variable, wildcat activity

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Authors: Samuel Opeyemi Williams, Razali Bin Adul Hamid, M. S. Misnan, Taki Eddine Seghier, D. I. Ajayi

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Construction sites are characterized with occupational risks. Review of literature on construction accidents reveals that a lot of theories have been propounded over the years by different theorists, coupled with multifarious models developed by different proponents at different times. Accidents are unplanned events that are prominent in construction sites, involving materials, objects and people with attendant damages, loses and injuries. Models were developed to investigate the causations of accident with the aim of preventing its occurrence. Though, some of these theories were criticized, most especially, the Heinrich Domino theory, being mostly faulted for placing much blame on operatives rather than the management. The purpose of this paper is to unravel the significant construction accident causation theories and models for the benefit of understanding of the theories, and consequently enabling construction stakeholders identify the possible potential hazards on construction sites, as all stakeholders have significant roles to play in preventing accident. Accidents are preventable; hence, understanding the risk factors of accident and the causation theories paves way for its prevention. However, findings reveal that still some gaps missing in the existing models, while it is recommended that further research can be made in order to develop more models in order to maintain zero accident on construction sites.

Keywords: domino theory, construction site, site safety, accident causation model

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