Search results for: TensorFlow probability

Authors: Fatemeh Deldarabdolmaleki, Mohamad Fakri Zaky Bin Ja'afar

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This paper presents fenestration analysis to study the balance between utilizing daylight and eliminating the disturbing parameters in a private office room with interior venetian blinds taking into account different slat angles. Mean luminance of the scene and window, luminance ratio of the workplane and window, work plane illumination and daylight glare probability(DGP) were calculated as a function of venetian blind design properties. Recently developed software, analyzing High Dynamic Range Images (HDRI captured by CCD camera), such as radiance based evalglare and hdrscope help to investigate luminance-based metrics. A total of Eight-day measurement experiment was conducted to investigate the impact of different venetian blind angles in an office environment under daylight condition in Serdang, Malaysia. Detailed result for the selected case study showed that artificial lighting is necessary during the morning session for Malaysian buildings with southwest windows regardless of the venetian blind’s slat angle. However, in some conditions of afternoon session the workplane illuminance level exceeds the maximum illuminance of 2000 lx such as 10° and 40° slat angles. Generally, a rising trend is discovered toward mean window luminance level during the day. All the conditions have less than 10% of the pixels exceeding 2000 cd/m² before 1:00 P.M. However, 40% of the selected hours have more than 10% of the scene pixels higher than 2000 cd/m² after 1:00 P.M. Surprisingly in no blind condition, there is no extreme case of window/task ratio, However, the extreme cases happen for 20°, 30°, 40° and 50° slat angles. As expected mean window luminance level is higher than 2000 cd/m² after 2:00 P.M for most cases except 60° slat angle condition. Studying the daylight glare probability, there is not any DGP value higher than 0.35 in this experiment, due to the window’s direction, location of the building and studied workplane. Specifically, this paper reviews different blind angle’s response to the suggested metrics by the previous standards, and finally conclusions and knowledge gaps are summarized and suggested next steps for research are provided. Addressing these gaps is critical for the continued progress of the energy efficiency movement.

Keywords: daylighting, office environment, energy simulation, venetian blind

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Authors: Puspa Maharani, Hendy Muagiri Margono, Izzatul Fithriyah

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Contraception is a medium used to prevent conception, aimed at couples who do not want pregnancy to occur. Unintended pregnancies have a prevalence of 48% per year of the total pregnancies that occur throughout the world. Schizophrenia sufferers have a high probability of being involved in unwanted sexual relations, but are not supported by adequate knowledge and use of contraception, so they are vulnerable to experiencing unwanted pregnancies. Unwanted pregnancy can pose significant health risks for patients with schizophrenia. There are many types of contraception that can be discussed and considered for patients with schizophrenia in order to improve the quality and well- being of their lives. Choosing the right contraceptive for patients with schizophrenia requires consideration of its use by taking into account the many factors that influence it.

Keywords: schizophrenia, contraception, pregnancy, mental health

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Authors: Cagdas Agirdas

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Background: The Affordable Care Act (ACA) requires non-grandfathered private insurance plans, starting with plan years on or after September 23rd, 2010, to provide certain preventive care services without any cost sharing in the form of deductibles, copayments or co-insurance. This requirement may affect racial and ethnic disparities in preventive care as it provides the largest copay reduction in preventive care. Objectives: We ask whether the ACA’s free preventive care benefits are associated with a reduction in racial and ethnic disparities in the utilization of four preventive services: cholesterol screenings, colonoscopies, mammograms, and pap smears. Methods: We use a data set of over 6,000 individuals from the 2009, 2010, and 2013 Medical Expenditure Panel Surveys (MEPS). We restrict our data set only to individuals who are old enough to be eligible for each preventive service. Our difference-in-differences logistic regression model classifies privately-insured Hispanics, African Americans, and Asians as the treatment groups and 2013 as the after-policy year. Our control group consists of non-Hispanic whites on Medicaid as this program already covered preventive care services for free or at a low cost before the ACA. Results: After controlling for income, education, marital status, preferred interview language, self-reported health status, employment, having a usual source of care, age and gender, we find that the ACA is associated with increases in the probability of the median, privately-insured Hispanic person to get a colonoscopy by 3.6% and a mammogram by 3.1%, compared to a non-Hispanic white person on Medicaid. Similarly, we find that the median, privately-insured African American person’s probability of receiving these two preventive services improved by 2.3% and 2.4% compared to a non-Hispanic white person on Medicaid. We do not find any significant improvements for any racial or ethnic group for cholesterol screenings or pap smears. Furthermore, our results do not indicate any significant changes for Asians compared to non-Hispanic whites in utilizing the four preventive services. These reductions in racial/ethnic disparities are robust to reconfigurations of time periods, previous diagnosis, and residential status. Conclusions: Early effects of the ACA’s provision of free preventive care are significant for Hispanics and African Americans. Further research is needed for the later years as more individuals became aware of these benefits.

Keywords: preventive care, Affordable Care Act, cost sharing, racial disparities

Procedia PDF Downloads 151

Authors: Temesgen Sidamo, Prakruti S. Rao, Eleni Akllilu, Workineh Shibeshi, Yumi Park, Yong-Soon Cho, Jae-Gook Shin, Scott K. Heysell, Stellah G. Mpagama, Ephrem Engidawork

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The fluoroquinolones (FQs) are used off-label for the treatment of multidrug-resistant tuberculosis (MDR-TB), and for evaluation in shortening the duration of drug-susceptible TB in recently prioritized regimens. Within the class, levofloxacin (LFX) and moxifloxacin (MXF) play a substantial role in ensuring success in treatment outcomes. However, sub-therapeutic plasma concentrations of either LFX or MXF may drive unfavorable treatment outcomes. To the best of our knowledge, the pharmacokinetics of LFX and MXF in Ethiopian patients with MDR-TB have not yet been investigated. Therefore, the aim of this study was to develop a population pharmacokinetic (PopPK) model of levofloxacin (LFX) and moxifloxacin (MXF) and assess the percent probability of target attainment (PTA) as defined by the ratio of the area under the plasma concentration-time curve over 24-h (AUC0-24) and the in vitro minimum inhibitory concentration (MIC) (AUC0-24/MIC) in Ethiopian MDR-TB patients. Steady-state plasma was collected from 39 MDR-TB patients enrolled in the programmatic treatment course and the drug concentrations were determined using optimized liquid chromatography-tandem mass spectrometry. In addition, the in vitro MIC of the patients' pretreatment clinical isolates was determined. PopPK and simulations were run at various doses, and PK parameters were estimated. The effect of covariates on the PK parameters and the PTA for maximum mycobacterial kill and resistance prevention was also investigated. LFX and MXF both fit in a one-compartment model with adjustments. The apparent volume of distribution (V) and clearance (CL) of LFX were influenced by serum creatinine (Scr), whereas the absorption constant (Ka) and V of MXF were influenced by Scr and BMI, respectively. The PTA for LFX maximal mycobacterial kill at the critical MIC of 0.5 mg/L was 29%, 62%, and 95% with the simulated 750 mg, 1000 mg, and 1500 mg doses, respectively, whereas the PTA for resistance prevention at 1500 mg was only 4.8%, with none of the lower doses achieving this target. At the critical MIC of 0.25 mg/L, there was no difference in the PTA (94.4%) for maximum bacterial kill among the simulated doses of MXF (600 mg, 800 mg, and 1000 mg), but the PTA for resistance prevention improved proportionately with dose. Standard LFX and MXF doses may not provide adequate drug exposure. LFX PopPK is more predictable for maximum mycobacterial kill, whereas MXF's resistance prevention target increases with dose. Scr and BMI are likely to be important covariates in dose optimization or therapeutic drug monitoring (TDM) studies in Ethiopian patients.

Keywords: population PK, PTA, moxifloxacin, levofloxacin, MDR-TB patients, ethiopia

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Authors: Carlos Arias-Alcaide, Cristina Soguero-Ruiz, Paloma Santos-Álvarez, Adrián García-Romero, Inmaculada Mora-Jiménez

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Analysing clinical data with computers in such a way that have an impact on the practitioners’ workflow is a challenge nowadays. This paper provides a first approach for monitoring the health status of COVID-19 patients through the use of some biomarkers (blood tests) and the simplest Naïve Bayes classifier. Data of two Spanish hospitals were considered, showing the potential of our approach to estimate reasonable posterior probabilities even some days before the event.

Keywords: Bayesian model, blood biomarkers, classification, health tracing, machine learning, posterior probability

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Authors: Hafiz Gulfam Ahmad, Chuangdong Li, Zeeshan Ahmad

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In this paper, we proposed a parallel IDS and honeypot based approach to detect and analyze the unknown and known attack taxonomy for improving the IDS performance and protecting the network from intruders. The main theme of our approach is to record and analyze the intruder activities by using both the low and high interaction honeypots. Our architecture aims to achieve the required goals by combing signature based IDS, honeypots and generate the new signatures. The paper describes the basic component, design and implementation of this approach and also demonstrates the effectiveness of this approach reducing the probability of network attacks.

Keywords: network security, intrusion detection, honeypot, snort, nmap

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Authors: Kurt Maxwell Kusterer, Sylvain Mignot, Annick Vignes

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To our best knowledge YouTube is the largest video hosting platform in the world. A high number of creators, viewers, subscribers and commentators act in this specific eco-system which generates huge sums of money. Views, subscribers, and comments help to increase the popularity of content creators. The most popular creators are sponsored by brands and participate in marketing campaigns. For a few of them, this becomes a financially rewarding profession. This is made possible through the YouTube Partner Program, which shares revenue among creators based on their popularity. We believe that the role of comments in increasing the popularity is to be emphasized. In what follows, YouTube is considered as a bilateral network between the videos and the commentators. Analyzing a detailed data set focused on French YouTubers, we consider each comment as a link between a commentator and a video. Our research question asks what are the predominant features of a video which give it the highest probability to be commented on. Following on from this question, how can we use these features to predict the action of the agent in commenting one video instead of another, considering the characteristics of the commentators, videos, topics, channels, and recommendations. We expect to see that the videos of more popular channels generate higher viewer engagement and thus are more frequently commented. The interest lies in discovering features which have not classically been considered as markers for popularity on the platform. A quick view of our data set shows that 96% of the commentators comment only once on a certain video. Thus, we study a non-weighted bipartite network between commentators and videos built on the sub-sample of 96% of unique comments. A link exists between two nodes when a commentator makes a comment on a video. We run an Exponential Random Graph Model (ERGM) approach to evaluate which characteristics influence the probability of commenting a video. The creation of a link will be explained in terms of common video features, such as duration, quality, number of likes, number of views, etc. Our data is relevant for the period of 2020-2021 and focuses on the French YouTube environment. From this set of 391 588 videos, we extract the channels which can be monetized according to YouTube regulations (channels with at least 1000 subscribers and more than 4000 hours of viewing time during the last twelve months).In the end, we have a data set of 128 462 videos which consist of 4093 channels. Based on these videos, we have a data set of 1 032 771 unique commentators, with a mean of 2 comments per a commentator, a minimum of 1 comment each, and a maximum of 584 comments.

Keywords: YouTube, social networks, economics, consumer behaviour

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Authors: Alfred Hasanaj , Ardit Gjeta, Miranda Kullolli

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The approach in analyzing defects on different pipe lines is conducted through Failure Assessment Diagram (FAD). These methods of analyses have further extended in recent years. This approach is used to identify and stress out a solution for the defects which randomly occur with gas pipes such are corrosion defects, gauge defects, and combination of defects where gauge and dents are included. Few of the defects are to be analyzed in this paper where our main focus will be the fracture of cast Iron pipes, elastic-plastic failure and plastic collapse of X52 steel pipes for gas transport. We need to conduct a calculation of probability of the defects in order to predict and avoid such costly defects.

Keywords: defects, failure assessment diagrams, steel pipes, safety factor

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Authors: Saurabh Agarwal

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This paper attempts to formalise the effect of demographic variables like marital status, gender, occupation and age on the source of investment advice which, in turn, affect the herd behaviour of investors and probability of investment in near future. Further, postulations have been made for most preferred investment option and purpose of saving and source of investment. Impact of theoretical analysis on choice among investment alternatives has also been investigated. The analysis contributes to understanding the different investment choices made by households in India. The insights offered in the paper indirectly contribute in uncovering the various unexplained asset pricing puzzles.

Keywords: portfolio choice, investment decisions, investor’s behaviour, Indian securities market

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Authors: Imen Bouazzi

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The IEEE-802.15.4 standard utilizes the CSMA-CA mechanism to control nodes access to the shared wireless communication medium. It is becoming the popular choice for various applications of surveillance and control used in wireless sensor network (WSN). The benefit of this standard is evaluated regarding of the packet loss probability who depends on the configuration of IEEE 802.15.4 MAC parameters and the traffic load. Our exigency is to evaluate the effects of various configurable MAC parameters on the performance of beaconless IEEE 802.15.4 networks under different traffic loads, static values of IEEE 802.15.4 MAC parameters (macMinBE, macMaxCSMABackoffs, and macMaxFrame Retries) will be evaluated. To performance analysis, we use ns-2[2] network simulator.

Keywords: WSN, packet loss, CSMA/CA, IEEE-802.15.4

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Authors: Vinicius R. Domingues, Luan C. S. M. Ozelim

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The statistical study has become indispensable for various fields of knowledge. Not any different, in Geotechnics the study of probabilistic and statistical methods has gained power considering its use in characterizing the uncertainties inherent in soil properties. One of the situations where engineers are constantly faced is the definition of a probability distribution that represents significantly the sampled data. To be able to discard bad distributions, goodness-of-fit tests are necessary. In this paper, three non-parametric goodness-of-fit tests are applied to a data set computationally generated to test the goodness-of-fit of them to a series of known distributions. It is shown that the use of normal distribution does not always provide satisfactory results regarding physical and behavioral representation of the modeled parameters.

Keywords: Kolmogorov-Smirnov test, Anderson-Darling test, Cramer-Von-Mises test, nonparametric adherence tests

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Authors: Minjung Park, Sangmi Chai, Myoung Jun Lee

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Since there are growing concerns on online privacy, enterprises could involve various personal privacy infringements cases resulting legal causations. For companies that are involving online business, it is important for them to pay extra attentions to protect users’ privacy. If firms can aware consequences from possible online privacy invasion cases, they can more actively prevent future online privacy infringements. This study attempts to predict the probability of ruling types caused by various invasion cases under U.S Personal Privacy Act. More specifically, this research explores online privacy invasion cases which was sentenced guilty to identify types of criminal punishments such as penalty, imprisonment, probation as well as compensation in civil cases. Based on the 853 U.S judicial cases ranged from January, 2000 to May, 2016, which related on data privacy, this research examines the relationship between personal information infringements cases and adjudications. Upon analysis results of 41,724 words extracted from 853 regal cases, this study examined online users’ privacy invasion cases to predict the probability of conviction for a firm as an offender in both of criminal and civil law. This research specifically examines that a cause of privacy infringements and a judgment type, whether it leads a civil or criminal liability, from U.S court. This study applies network text analysis (NTA) for data analysis, which is regarded as a useful method to discover embedded social trends within texts. According to our research results, certain online privacy infringement cases caused by online spamming and adware have a high possibility that firms are liable in the case. Our research results provide meaningful insights to academia as well as industry. First, our study is providing a new insight by applying Big Data analytics to legal cases so that it can predict the cause of invasions and legal consequences. Since there are few researches applying big data analytics in the domain of law, specifically in online privacy, this study suggests new area that future studies can explore. Secondly, this study reflects social influences, such as a development of privacy invasion technologies and changes of users’ level of awareness of online privacy on judicial cases analysis by adopting NTA method. Our research results indicate that firms need to improve technical and managerial systems to protect users’ online privacy to avoid negative legal consequences.

Keywords: network text analysis, online privacy invasions, personal information infringements, predicting judgements

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Authors: Ali Y Al-Attraqchi, P. Rajeev, M. Javad Hashemi, Riadh Al-Mahaidi

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This paper presents a probabilistic incremental dynamic analysis (IDA) of a full reinforced concrete building subjected to column loss scenario for the assessment of progressive collapse. The IDA is chosen to explicitly account for uncertainties in loads and system capacity. Fragility curves are developed to predict the probability of progressive collapse given the loss of one or more columns. At a broader scale, it will also provide critical information needed to support the development of a new generation of design codes that attempt to explicitly quantify structural robustness.

Keywords: fire, nonlinear incremental dynamic analysis, progressive collapse, structural engineering

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Authors: Safia Hocine, Zina Benouaret, Djamil A¨ıssani

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In this paper, we study the performance of the strong stability method of the univariate classical risk model. We interest to the stability bounds established using two approaches. The first based on the strong stability method developed for a general Markov chains. The second approach based on the regenerative processes theory . By adopting an algorithmic procedure, we study the performance of the stability method in the case of exponential distribution claim amounts. After presenting numerically and graphically the stability bounds, an interpretation and comparison of the results have been done.

Keywords: Marcov chain, regenerative process, risk model, ruin probability, strong stability

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Authors: Dariusz Jacek Jakóbczak

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Mathematics and computer science are interested in methods of 2D curve interpolation and extrapolation using the set of key points (knots). A proposed method of Hurwitz- Radon Matrices (MHR) is such a method. This novel method is based on the family of Hurwitz-Radon (HR) matrices which possess columns composed of orthogonal vectors. Two-dimensional curve is interpolated via different functions as probability distribution functions: polynomial, sinus, cosine, tangent, cotangent, logarithm, exponent, arcsin, arccos, arctan, arcctg or power function, also inverse functions. It is shown how to build the orthogonal matrix operator and how to use it in a process of curve reconstruction.

Keywords: 2D data interpolation, hurwitz-radon matrices, MHR method, probabilistic modeling, curve extrapolation

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Authors: Anna Dolinayova, Jozef Danis, Juraj Camaj

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The report deals with comparison of traffic accidents in Slovak republic in road and rail transport since year 2009 until 2014, with evaluation of external costs and consequently with the possibilities of their internalization. The results of road traffic accidents analysis are realized in line with after-effects they have caused; in line with main cause, place of origin (within or out of town) and in accordance to age of people they were killed or hard, eventually easy injured in traffic accidents. Evaluation of individual after-effects is carried in terms of probability of traffic accidents occurrence.

Keywords: external costs, traffic accident, rail transport, road transport

Procedia PDF Downloads 591

Authors: N. Burtebayev, Sh. Hamada, Zh. Kerimkulov, D. K. Alimov, A. V. Yushkov, N. Amangeldi, A. N. Bakhtibaev

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The main aim of the current work is to examine if 14N is candidate to be clusterized nuclei or not. In order to check this attendance, we have measured the angular distributions for 14N ion beam elastically scattered on 12C target nuclei at different low energies; 17.5, 21, and 24.5MeV which are close to the Coulomb barrier energy for 14N+12C nuclear system. Study of various transfer reactions could provide us with useful information about the attendance of nuclei to be in a composite form (core + valence). The experimental data were analyzed using two approaches; Phenomenological (Optical Potential) and semi-microscopic (Double Folding Potential). The agreement between the experimental data and the theoretical predictions is fairly good in the whole angular range.

Keywords: deuteron transfer, elastic scattering, optical model, double folding, density distribution

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Authors: Wi-Sun Ryu, Eun-Kee Bae

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Prior studies have shown that patients with right hemispheric stroke are likely to seek medical service compared with those with left hemispheric stroke. However, the underlying mechanism for this phenomenon is unknown. In the present study, we generated lesion probability maps in a patient with right and left middle cerebral artery infarct and statistically compared. We found that precentral gyrus-Brodmann area 44, a language area in the left hemisphere - involvement was significantly higher in patients with left hemispheric stroke. This finding suggests that a language dysfunction was more noticeable, thereby taking more patients to hospitals.

Keywords: cerebral infarct, brain MRI, statistical parametric mapping, middle cerebral infarct

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Kais Manai

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The main part of this work focuses on the study of pion/muon separation at low energy using a nuclear Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The work consists of two parts: particle reconstruction algorithm and a Neural Network that assigns to each reconstructed particle the probability to be a muon or a pion. The pion/muon separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data. The algorithm allows to achieve a 60% muon identification efficiency with a pion misidentification smaller than 3%.

Keywords: nuclear emulsion, particle identification, tracking, neural network

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Authors: Yousef Abu Nahleh, Arun Kumar, Fugen Daver, Reham Al-Hindawi

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Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.

Keywords: decision tree modeling, forecasting, humanitarian relief, emergency supply chain

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Authors: Thi Thao Nguyen, Andrew McLennan, Shino Takayama

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We study the probabilistic allocation of finitely many indivisible objects to finitely many agents. Well known allocation rules for this problem include random priority, the market mechanism proposed by Hylland and Zeckhauser [1979], and the probabilistic serial rule of Bogomolnaia and Moulin [2001]. We propose a new allocation rule, which we call the lexico-graphic (serial) rule, that is tailored for situations in which each agent's primary concern is to maximize the probability of receiving her favourite object. Three axioms, lex efficiency, lex envy freeness and fairness, are proposed and fully characterize the lexicographic serial rule. We also discuss how our axioms and the lexicographic rule are related to other allocation rules, particularly the probabilistic serial rule.

Keywords: Efficiency, Envy free, Lexicographic, Probabilistic Serial Rule

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Authors: Christo Februanto Putra

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This paper presents risk identification that affects investment feasibility on toll road infrastructure in Indonesia using qualitative methods survey based on the expert practitioner in investor, contractor, and state officials. The problems on infrastructure investment in Indonesia, especially on KPBU model contract, is many risk factors in the investment plan is not calculated in detail thoroughly. Risk factor is a value used to provide an overview of the risk level assessment of an event which is a function of the probability of the occurrence and the consequences of the risks that arise. As results of the survey which is to show which risk factors impacts directly to the investment feasibility and rank them by their impacts on the investment.

Keywords: risk identification, indonesia toll road, investment feasibility

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Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Daniel R. dos Santos, Mateus S. Maldonado, Estevão J. R. Batista

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The military commanders increasingly dependent on spatial awareness, as knowing where enemy are, understanding how war battle scenarios change over time, and visualizing these trends in ways that offer insights for decision-making. Thanks to advancements in geospatial technologies and artificial intelligence algorithms, the commanders are now able to modernize military operations on a universal scale. Thus, geovisualisation has become an essential asset in the defense sector. It has become indispensable for better decisionmaking in dynamic/temporal scenarios, operation planning and management for the war field, situational awareness, effective planning, monitoring, and others. For example, a 3D visualization of war field data contributes to intelligence analysis, evaluation of postmission outcomes, and creation of predictive models to enhance decision-making and strategic planning capabilities. However, old-school visualization methods are slow, expensive, and unscalable. Despite modern technologies in generating 3D point clouds, such as LIDAR and stereo sensors, monocular depth values based on deep learning can offer a faster and more detailed view of the environment, transforming single images into visual information for valuable insights. We propose a dedicated monocular depth reconstruction approach via deep learning techniques for 3D geovisualisation of satellite images. It introduces scalability in terrain reconstruction and data visualization. First, a dataset with more than 7,000 satellite images and associated digital elevation model (DEM) is created. It is based on high resolution optical and radar imageries collected from Planet and Copernicus, on which we fuse highresolution topographic data obtained using technologies such as LiDAR and the associated geographic coordinates. Second, we developed an imagery-DEM fusion strategy that combine feature maps from two encoder-decoder networks. One network is trained with radar and optical bands, while the other is trained with DEM features to compute dense 3D depth. Finally, we constructed a benchmark with sparse depth annotations to facilitate future research. To demonstrate the proposed method's versatility, we evaluated its performance on no annotated satellite images and implemented an enclosed environment useful for Geovisualisation applications. The algorithms were developed in Python 3.0, employing open-source computing libraries, i.e., Open3D, TensorFlow, and Pythorch3D. The proposed method provides fast and accurate decision-making with GIS for localization of troops, position of the enemy, terrain and climate conditions. This analysis enhances situational consciousness, enabling commanders to fine-tune the strategies and distribute the resources proficiently.

Keywords: depth, deep learning, geovisualisation, satellite images

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Authors: Khachatur V. Nerkararyan, Sergey I. Bozhevolnyi

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We investigate relaxation dynamics of a quantum dipole emitter (QDE), e.g., a molecule or quantum dot, located near a metal nanoparticle (MNP) exhibiting a dipolar localized surface plasmon (LSP) resonance at the frequency of the QDE radiative transition. It is shown that under the condition of the QDE-MNP characteristic relaxation time being much shorter than that of the QDE in free-space but much longer than the LSP lifetime. It is also shown that energy dissipation in the QDE-MNP system is relatively weak with the probability of the photon emission being about 0.75, a number which, rather surprisingly, does not explicitly depend on the metal absorption characteristics. The degree of entanglement measured by the concurrency takes the maximum value, while the distances between the QDEs and metal ball approximately are equal.

Keywords: metal nanoparticle, localized surface plasmon, quantum dipole emitter, relaxation dynamics

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Authors: Luke McGowan, David Pickernell, Martini Battisti

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In this paper, Adversity Triggers were explored through the lens of Organisational Resilience. Adversity Triggers are contextualized by temporal factors, thus, naturally aligning to Resilience literature. Resilience has been chosen as the theoretical framework as risk management approaches are often not geared towards providing meaningful responses to high-impact, low-probability events. Adversity Triggers and Organisational Resilience both consider temporal factors which enabled investigation of each phase of recovery. A systematic literature was employed to assess previous literature and define further areas of research. The systematic literature review method was chosen to catalogue and identify gaps in current literature.

Keywords: adversity triggers, crisis, extreme events, organisational resilience, resilience

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Authors: Rashmi Uchil, A. H. Sequeira

Abstract:

Post-liberalization era in India has seen an unprecedented growth of mergers, both domestic as well as cross-border deals. Indian organizations have slowly begun appreciating this inorganic method of growth. However, all is not well as is evidenced in the lowering value creation of organizations after mergers. Several studies have identified that organizational culture is one of the key factors that affects the success of mergers. But very few studies have been attempted in this realm in India. The current study attempts to identify the factors in the organizational culture variable that may be unique to India. It also focuses on the difference in the impact of organizational culture on merger of organizations in the manufacturing and service sectors in India. The study uses a mixed research approach. An exploratory research approach is adopted to identify the variables that constitute organizational culture specifically in the Indian scenario. A few hypotheses were developed from the identified variables and tested to arrive at the Grounded Theory. The Grounded Theory approach used in the study, attempts to integrate the variables related to organizational culture. Descriptive approach is used to validate the developed grounded theory with a new empirical data set and thus test the relationship between the organizational culture variables and the success of mergers. Empirical data is captured from merged organizations situated in major cities of India. These organizations represent significant proportions of the total number of organizations which have adopted mergers. The mix of industries included software, banking, manufacturing, pharmaceutical and financial services. Mixed sampling approach was adopted for this study. The first phase of sampling was conducted using the probability method of stratified random sampling. The study further used the non-probability method of judgmental sampling. Adequate sample size was identified for the study which represents the top, middle and junior management levels of the organizations that had adopted mergers. Validity and reliability of the research instrument was ensured with appropriate tests. Statistical tools like regression analysis, correlation analysis and factor analysis were used for data analysis. The results of the study revealed a strong relationship between organizational culture and its impact on the success of mergers. The study also revealed that the results were unique to the extent that they highlighted a marked difference in the manner of internalization of change of organizational culture after merger by the organizations in the manufacturing sector. Further, the study reveals that the organizations in the service sector internalized the changes at a slower rate. The study also portrays the industries in the manufacturing sector as more proactive and can contribute to a change in the perception of the said organizations.

Keywords: manufacturing industries, mergers, organizational culture, service industries

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Authors: Bovornluck Kuosuwan, Kevin Wongleedee

Abstract:

The purposes of this research was to study the level of satisfaction of international tourists in five important areas: satisfaction on visiting tourist destinations, satisfaction on tourist images, satisfaction on value for money, satisfaction on service quality, and satisfaction when compared with their expectation. A probability random sampling of 200 inbound tourists was utilized. A questionnaire was used to collect the data and small in-depth interviews were also used to get their opinions about their positive and negative evaluations of their experience travelling in Thailand. The findings revealed that the majority of respondents had a medium level of satisfaction. When examined in detail, the level of satisfaction can be ranked from highest to lowest according to the mean average as follows: visiting tourist destinations, expectations, service quality, tourist image, and value for money.

Keywords: inbound tourists, satisfaction, Thailand, international tourists

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Authors: S. Suman, G. N. Singh

Abstract:

The present manuscript addresses the estimation procedure of population parameter using Poisson probability distribution when characteristic under study possesses a rare sensitive attribute. The generalized form of unrelated randomized response model is suggested in order to acquire the truthful responses from respondents. The resultant estimators have been proposed for two situations when the information on an unrelated rare non-sensitive characteristic is known as well as unknown. The properties of the proposed estimators are derived, and the measure of confidentiality of respondent is also suggested for respondents. Empirical studies are carried out in the support of discussed theory.

Keywords: Poisson distribution, randomized response model, rare sensitive attribute, non-sensitive attribute

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