Real-time Basis Research Articles

Do the Macroeconomic variables influence Exchange Traded Funds (ETFs)?

Exchange Traded Funds (ETFs) are essentially Index Funds that are listed and traded on exchanges like stocks. Until the development of ETFs, this was not possible before. Globally, ETFs have opened a whole new panorama of investment opportunities to Retail as well as Institutional Money Managers. They enable investors to gain broad exposure to entire stock markets in different Countries and specific sectors with relative ease, on a real-time basis and at a lower cost than many other forms of investing. An ETF is a basket of stocks that reflects the composition of an Index, like S&P CNX Nifty or BSE Sensex. The ETFs trading value is based on the net asset value of the underlying stocks that it represents. Think of it as a Mutual Fund that you can buy and sell in real-time at a price that change throughout the day. Compared to mutual funds, ETFs are relatively new. The first U.S. ETFs were created by State Street Global Advisors with the launch of the S&P 500 depositary receipts, also known as SPDRs ("spiders"). Although the first ETFs tended to track broad market indexes, more recent ETFs have been developed to track sectors, fixed income, global investments, commodities and currencies. There exists correlation between ETF and macroeconomic variables and also change in independent variables causes change in dependent variable. However, the degree of variability differs. These variables form significant part and the level of prediction is also good. Therefore, the model is good fit for the data. An investor needs to guise into these while taking investment decision.

Application of Big Data Analytics to Determine Traffic Congestion

This study focused on the application of big data analytics to estimate roadway congestion. The implementation of big data allows a convenient approach of data collection without any field or observer biasness. It also relieves the restriction of a limited sample. Highway Capacity Manual 2000 suggested considering travel speed as the most appropriate parameter for describing traffic congestion. Therefore, this research employs a speed performance index with the concept of big data analytics to describe traffic congestion of road. This research estimates speed performance index for different sections of road. The data was gathered for three main roads of Karachi i.e. Rashid Minhas Road, Shahra e Faisal Road and Main Korangi Road. The major achievement of this study project is to propose a novel approach to calculate the speed of a vehicle without field measurements. The data is collected through Smartphone with the aid of an application available online namely My Track. The data that was utilized for this project mainly comprises of GPS Exchange Format (GPX) routes that are converted into Extensible Markup Language (XML) to run the developed script. The script was able to determine the status of congestion on a variety of highways with the help of speed performance index. The same script can be used by administrators and other transportation service providers for estimating the congestion on a real-time basis. The methods and procedures from this study would aid in the transportation planning process, especially for route selection of individuals as well as services.

Innovative Tech-Savvy Education: Designing a Smart Assessment System

The purpose of this research is to create a smart assessment system linked to a project-based learning model through a website. It is anticipated that this system will raise the proficiency and productivity of learning evaluation through the implementation of digital technology. The approach applied in this research includes the identification of needs, and the designing of the system, followed by software development and testing. According to the findings of the research, it is evident that the system developed for smart assessment can offer several complex and combined assessment facilities within the system, which would help the educators for easier examination of the student’s performance on a real-time basis. In addition to that, this system also enhances the common work of the students with the educators on completing the project assigned by the latter. Hence, the incorporation of website-based online assessments tries to extend the validity of the PBL implementation approach as well as contribute positively towards embracing positive enhancement in education.

Exploring Summarization Performance: A Comparison of Pointer Generator, Pegasus, and GPT-3 Models

The world is rapidly advancing technologically and the way we communicate is changing with it.We are now able to send messages through text, voice, or video chat, which means that the amount of content being generated every day is growing fast. The problem with all this information is that it can be overwhelming for people to stay on top of everything they need to,especially those who have to read a lot of texts and get the gist of what they need to know. Manual text summarizing is a time-consuming and inherently tedious activity. There is a need for a system that could provide crisp, precise information on a real time basis . This would help the decision makers to comprehend the information quickly and take decisions at a faster rate. This also helps to read important information without having to spend too much time on it. By doing so, you save your time and energy, as well as reduce stress levels. This research paper identifies the need for automating the text summarization . It further elaborates on its key concepts and gives a comparison of the various text summarization models. Moving into a proposed model in itself was a challenge. This research paper dwells on the proposed methodology and performs various evaluation metrics. ImpactStatement - The process of text summarization involves using natural language processing to condense information into a shorter, more concise version. The goal is to condense the original document while preserving its essential information This paper conducts a comparison of different text summarization methods, including extractive and abstractive techniques. It also categorizes summarization systems and examines the use of statistical and linguistic approaches for summarization.

We are stronger when we are connected: Queer counterpublics and the Korean Queer Culture Festival

This paper examines the emergence of queer counterpublics in South Korea through a case study on the opening ceremony of the Korean Queer Culture Festival (KQCF) in 2015. Due to an outbreak of the Middle East Respiratory Syndrome (MERS), the organizers live-streamed the event via YouTube instead of inviting the participants to the Seoul City Plaza, where they set up the stage. During the opening ceremony, social media platforms momentarily became alternative spaces for queers to interact with each other on a real-time basis. In analyzing a series of concerted actions, such as lining up in front of a police station for a permit, setting up a stage, writing a statement, shooting a video for broadcast, watching the event on YouTube, posting messages and pictures on social media, and exchanging messages with friends, I discuss how queer counterpublics emerge, contesting the heteronormative and heteropatriarchal public sphere in South Korea.

Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health. This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting. The calculations used the AAindex matrices in parallel to the Hamming distance. The calculations were carried out with different types and values of antigenic escape thresholds, on four data sets. The results were compared using common binary classification metrics. Significant differentiation is shown depending on the data sets used. The best results were demonstrated by all three models for the forecast autumn season of 2022, which were preliminary trained on the February season of the same year (Auroc 0.934; 0.958; 0.956, respectively). The lowest results were obtained for the entire forecast year 2023, they were set up on data from two seasons of 2022 (Aucroc 0.614; 0.658; 0.775). The dependence of the results on the types of thresholds used and their values turned out to be insignificant. The additional use of AAindex matrices did not significantly improve the results of the models without introducing significant deterioration. More complex models show better results. When developing cross-immunity models, testing on a variety of data sets is important to make strong claims about their prognostic robustness.

Impact of Bedside Echocardiography in the Management of Critically Ill Pediatric Patients in the Pediatric Intensive Care Unit: A Cross-Sectional Study.

Background Managing critically ill pediatric patients is a challenging responsibility that necessitates effective prioritization and time management. It is important not only to assess the condition of the patient on a continuous and real-time basis but also to assess in a way that will provide vital clues that may help in diagnosis and treatment. Our study aims to investigate the association between echocardiography and clinical systemic examination, to find the association between cardiac dysfunction and pediatric outcomes, and to identify the indications and necessity of echocardiography assessments and therapeutic interventions for patients in the pediatric intensive care unit. Methods This cross-sectional study was conducted in the pediatric intensive care unit (PICU) of the pediatrics department at Datta Meghe Institute of Higher Education and Research, Wardha, India. All critically ill pediatric patients admitted to the PICU underwent echocardiography. The study primarily focused on the indications for echocardiography, the association between systolic and diastolic dysfunction and patient outcomes, and the therapeutic interventions implemented based on the echocardiographic findings. Results The study analyzed 139 subjects aged from one month to 204 months, with the majority in the one- to five-year age group, followed by the 10-15-year age group. Cardiac anomalies were identified in 39 out of 139 cases, and cardiac murmurs were present in 27 cases with an almost equal gender distribution in the infantile age group. Dyspnea, edema, and hepatomegaly were the most common indications for echocardiography. Lasix was the most commonly used antifailure drug used in heart diseases. Sixteen individuals had systolic dysfunction, and eight had diastolic dysfunction, with a mortality rate of 62%. There was a significant association between systolic and diastolic dysfunction and mortality. Conclusion Echocardiography is a valuable asset within the PICU, providing critical insights into cardiac function and hemodynamics. By guiding clinical decision-making, it plays a pivotal role in optimizing care strategies, ultimately leading to improved outcomes for pediatric patients admitted with cardiac conditions. Overall, the study emphasizes the complex nature of pediatric cardiac conditions and the necessity for individualized treatment approaches based on distinct diagnoses and clinical indications.

Vibration Characteristics and Mesoscopic State Evolution of Ballasted Track During Dynamic Stabilizing Operation

Dynamic stabilization of large machines is necessary procedures before the commencement of operations railway lines that are newly built. However, limited or no studies have been carried out to reveal the operational mechanism from the vibration transfer characteristics of ballasted track subjected to excitation by stabilizing machines. The focus of this study is to solve this shortcoming. First, a typical new railway line was selected to test the dynamic response of the ballasted track during stabilizing operation, and then a stabilizing machine-ballasted track model was established with the help of the DEM-MBD coupling method. And the biaxial vibration accelerometers were inserted at various locations in the ballast bed to monitor the evolution of the vibration level on real-time basis. The influence of the stabilizing operation process on the vibration transmission and attenuation characteristics of sleeper and ballast bed was explored using wavelet analysis and Fast Fourier Transform method. The vibration absorption capacity and energy dissipation characteristics of the ballasted track under various stabilizing frequencies were analyzed. At the same time, the contact state and movement posture of ballast particles during the stabilizing operation were explored. Results showed that the vertical vibration attenuation rate of the slope toe of the ballast bed after the first stabilizing machine pass is 92.32%, while the vertical vibration attenuation rate of the second stabilizing machine is reduced by 3.14%. The second stabilizing machine can cause a large lateral movement of the ballast at the bottom of the sleeper. Under the excitation of 25[Formula: see text]Hz stabilizing frequency, it was observed that, the vibration absorption capacity of the ballast bed at the bottom of the sleeper is high, the vibration level at the ballast bed slope is low, and the stability is better. This is the optimal frequency for the stable operation of the new railway. This study can provide theoretical support for the establishment of the internal correlation mechanism between the vibration characteristics of stabilizing machines and the mechanical state of the ballast bed.

Influence of Smart Fertilizer on Yield and Economics of Cowpea

The extent of agricultural land under cultivation is diminishing continuously as a result of improper over use of fertilizer leads to environmental contamination and productivity of land is also become diminished. Nonetheless, in order to fulfill the rising population's nutritional needs as well as those of the developing industry, agricultural production must be enhanced. Smart fertilizers like Nano fertilizers are the new technologies to enhance the nutrient use efficiency them by improving crop yield in sustainable manner. Nano-fertilizers are the Nano-particles-based fertilizers, where supply of the nutrients is made precisely for maximum plant growth, have higher use efficiency, exploiting plant unavailable nutrients in the rhizosphere and can be delivered on real time basis into the rhizosphere or by foliar spray. Pulses are gaining more important position in Indian agriculture. But the average productivity of pulses is pulses in India (764 kg ha-1) is far below the average productivity of the world (848 kg ha-1). Basally applied nutrients are lost over time, thus applying important fertilizer to cowpeas during their critical growth is a effective strategy to enhance their yield. Especially smart fertilizers like Nano Di-Ammonium Phosphate (DAP) and Zinc - Ethylene Diamine Tetra Acetic Acid (Zn EDTA) are very helpful in that. Therefore, the field experiment was done in the School of Agriculture and Animal Science, Gandhi gram Rural Institute, Gandhi gram, Tamil Nadu during January 2024 to April 2024 to assess the impact of Nano DAP and Zn EDTA on growth and yield of cowpea. There were seven treatments with three replications that were laid out in RBD. Among the different treatments, RDF 50% P, 100% NK + Seed treatment and two foliar spray 0.2% of Nano DAP + Foliar spray of 3% Zn EDTA (T7) was achieved a supremely improved growth parameters and yield attributes and yield. Experimental results clearly revealed that RDF 50% P, 100% NK + Seed treatment and two foliar spray 0.2% of Nano DAP + Foliar spray of 3% Zn EDTA(T7) has recorded the highest grain yield of 1720 kg ha-1 and BCR of 3.25.

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.

Demand side management strategy for smart building using multi-objective hybrid optimization technique

This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.

Synergistic Effects of Exogenous Nutrient Ions on the Real-Time Cadmium Extraction by an Accumulator

Bidens tripartita L. is a cadmium (Cd) accumulator. However, the real-time influx or efflux of Cd2+ around its root apex has not yet been performed. The object of this experiment was to compare the roles of added ions in solution on dynamic Cd extraction by B. tripartita root tip. Quartz sand was used to grow the seedling of B. tripartite. The Cd concentrations of all samples were determined by using ICP-OES after digestion. The Cd2+ influx around the root apex was measured in vivo, i.e., using non-invasive micro-test technology (NMT). The results showed that the Cd2+ influx was found to be decreased by 35.9%, 43.7%, 20.6%, and 57.5% under 10 μM Cd combined with high content Ca2+, Mg2+, Fe3+, or K+ (16 mM, 8 mM, 0.5 mM, 18 mM, respectively), compared to that under 10 μM Cd stress. But Cd treatments with low content ions with 0.05 mM Fe3+ or 0.5 mM S increased the Cd2+ influx in roots by 20.5% and 34.6%, respectively. It was also found that Cd treatment with high concentrations of Ca2+ or K+ increased the shoot biomass of B. tripartita seedlings. Chl a and b contents were significantly decreased in the Cd treatments with low concentrations of Fe3+ or S compared to those under Cd stress alone, and the dehydrogenase activity of the roots decreased in the treatment of Cd with 0.05 mM Fe3+ or 0.5 mM S. Our results indicate that the addition of 0.05 mM Fe3+ or 0.5 mM S promoted Cd2+ influx and Cd uptake by B. tripartita. Unlike traditional measurement, the Cd2+ movements of three-dimensional space around the B. tripartita root tip had been performed by NMT. It was suggested that the effects of S and Fe3+ on the remediation potential of B. tripartita need to be further researched in the future. The results of this study provided a real-time and micro-dynamic theoretical basis for phytoremediation mechanisms.

Impact of Fintech on Investment Banking

Fintech, which stands for financial technology, has emerged as a disruptive force in various industries, and its impact on investment banking has been particularly profound. The rapid advancement of technology, coupled with the growing demand for more efficient and accessible financial services, has paved the way for innovative fintech applications within the investment banking sector. Fintech platforms offer advanced data analytics, artificial intelligence (AI), and machine learning tools that enable investors to process vast amounts of information and make data-driven investment decisions. This has not only increased the speed and accuracy of investment research but has also levelled the playing field, allowing smaller investors and even individual retail investors to access high-quality research previously reserved for institutional players. Another area where fintech applications are transforming investment banking is in the realm of online trading. Fintech platforms provide seamless and user-friendly interfaces that allow investors to execute trades on a real-time basis. The integration of AI algorithms and robo-advisory services within these platforms has expanded the reach of automated investment advice, enabling investors to receive personalized recommendations based on their investment goals, risk appetite, and financial constraints. fintech platforms have introduced alternative methods of capital formation, such as crowdfunding and peer-to-peer lending. This has not only reduced costs but has also increased access to capital for small and medium-sized enterprises (SMEs) and startups, fostering innovation and entrepreneurial activity. As technology continues to advance and regulatory frameworks adapt, investment banking is likely to witness even more profound changes, driving innovation and shaping the future of the industry.

Augmented Physics-Informed Neural Networks and Seahorse Optimization Algorithm for Music Scene Simulation and Emotion Regulation in Virtual Reality Technology

Virtual Reality (VR) technology has shown promise in simulating music scenes for emotion regulation. The challenge is to enhance the realism and interactivity of VR-based music experiences to create a more immersive and emotionally impactful environment for users. This manuscript proposes an augmented physics-informed neural network and seahorse optimization algorithm for music scene simulation and emotion regulation in virtual reality technology (APINN-SHO-MSS-ERVRT). Initially, the data is collected via head-mounted display (HMD) in real time basis. Afterward, the data is fed to data-adaptive Gaussian average filtering (DAGAF) based pre-processing process. In the pre-processing segment, it improves virtual presence. The pre-processing output is given to augmented physics-informed neural network (APINN)and seahorse optimization algorithm (SHO) for effectively classifying the facial expression for fear and null. Therefore, SHO is proposed to enhance weight parameter of augmented physics-informed neural network classifier, which precisely classifies the facial emotion classifier. The proposed method is implemented in MATLAB and evaluated their performance with existing methods. The performance metrics, like accuracy, precision, sensitivity, specificity, computational time and ROC is analysed to the performance of the proposed method. The proposed APINN-SHO-MSS-ERVRT methods of accuracy are provide 90% higher accuracy for fear and 97% higher accuracy for null emotion is analysed when compared with existing methods GERR-NN-AMSVR, ER-VR-EEG and MTS-VRS-CDI. Proposed APINN-SHO-MSS-ERVRT method attains 0.87%, 0.88% and 0.89%higher ROC analysed to the existing methods respectively.

Simultaneous optimal control of directional missed discovery rates in data stream diagnosis

High-dimensional data streams are ubiquitous in modern manufacturing, due to their ability to provide valuable information about the industrial system’s performance on a real-time basis. If a shift occurs in a production process, fault diagnosis based on the data streams is of critical importance for identifying the root cause. Existing methods have largely focused on controlling the total missed discovery rate without distinguishing missed signals for positive versus negative components of the shift vector. In practice, however, losses incurred from the two directional shifts can differ substantially, so it is desirable to constrain the proportions of missed signals for positive and negative components at two distinctive levels. In this article, we propose a fault classification procedure that controls the two proportions separately. By formulating the problem as Lagrangian multiplier optimization, we show that the proposed procedure is optimal in the sense that it minimizes the expected number of false discoveries. We also suggest an iterative adjustment algorithm that converges to the optimal Lagrangian parameters. The asymptotic optimality for the data-driven version of our procedure is also established. Theoretical justification and numerical comparison with state-of-the-art methods show that the proposed procedure works well in applications.

텍스트 마이닝 기법을 적용한 헤어미용 키워드 도출 분석

With the rapid growth and development of communication technology, it has been possible to develop diverse digital devices. At the same time, more people have become aware of the importance of online spaces. In fact, diverse data are stored online in realtime, having a significant influence on people’s lives. In particular, ‘texts’ categorized as unstructured data have been influential in diverse fields including personal life. Therefore, text research techniques, methodologies and institutes have appeared. With the popularity of K-beauty around the world, related data in cosmetology industry have also been accumulated on a realtime basis. Under these circumstances, there is a necessity to discuss industrial development through analysis. This study attempted to extract hairstyling-related keywords and analyze top frequency texts, using text mining among diverse big data analysis techniques. The goal is to understand how such texts are connected in awareness of keywords and figure out the current situation of hairstyling industry. It is anticipated that the study results would be available as basic data for big data research. It is also expected that there would be further studies on big data for the development of Korean beauty industry.

Bilevel planning of electric vehicle charging station and battery swapping station considering real-time uncertainty

The placement of electric vehicle charging stations (EVCS) and battery swapping stations (BSS) is crucial to meet demand while minimizing their impact on the power system network. This article addresses the placement and scheduling of EVCS and BSS within a radial distribution network (RDN) overlaid with a road network. Electric Vehicle Routing Problem (EVRP) and Traffic Assignment Problem (TAP) is introduced to reduce EV energy consumption during trips to EVCS and BSS. A Bi-layer optimization approach is used, with the outer layer minimizing energy loss by allocating EVCS and BSS optimally, and the inner layer optimizing scheduling considering associated costs. Uncertainties related to EV is considered on a real-time trip basis and modelled using 2m-Point estimation method. The problem is solved using Differential Evolution (DE) and Prairie Dog Optimization (PDO) techniques. PDO results in a 9.6% reduction in total energy losses and a 22.5% decrease in total associated costs by optimally placing EVCS at 69, 25,114 and BSS at 19, 84,113 of the RDN compared to DE. Inclusion of 2-m PEM uncertainty increases total energy loss by 8.76% but decreases total associated costs by 6.97% compared to real-time uncertainty cases, enhancing the realism of the results.

Real-time investigation of dust collection effects on solar PV panel efficiency

The amount of the light distraction on the PV is made by the accumulation of particles of dust which in turn decreases efficient performance as well as leads to a reduction of money flow for the investors. More studies and tests were carried out inside the laboratories that cannot find a proper solution to mitigate the same. This study can enable the proper cleaning schedules of the PV panels as this work is being carried out on a real-time basis on the rooftops. The measurement of required parameters like irradiation, output power from the panels, and the amount of dust particles accumulated was done on an hourly, monthly, and yearly basis. It is found that nearly 8% of the performance could be dropped annually. For making a sustained operation of the PV panels it is required to have a cleaning process for 45 days intervals, especially for small-scale systems.

Understanding Complex Traffic Dynamics with the Nondimensionalisation Technique

Hydrodynamic traffic models are crucial to optimizing transportation efficiency and urban planning. They usually comprise a set of coupled partial differential equations featuring an arbitrary number of terms that aim to describe the different nuances of traffic flow. Consequently, traffic models quickly become complicated to solve and difficult to interpret. In this article, we present a general traffic model that includes a relaxation term and an inflow of vehicles term and utilize the mathematical technique of nondimensionalisation to obtain universal solutions to the model. Thus, we are able to show extreme sensitivity to initial conditions and parameter changes, a classical signature of deterministic chaos. Moreover, we obtain simple relations among the different variables governing traffic, thus managing to efficiently describe the onset of traffic jams. We validate our model by comparing different scenarios and highlighting the model’s applicability regimes in traffic equations. We show that extreme speed values, or heavy traffic inflow, lead to divergences in the model, showing its limitations but also demonstrating how the problem of traffic jams can be alleviated. Our results pave the way to simulating and predicting traffic accurately on a real-time basis.

Analysis of Accidents in Petroleum and Chemical Industries

This study determined the critical risk factors that influence the risk of major accidents in petroleum and chemical industries. The retrospective study assessed five iconic accidents using the tripod beta analysis methodology. These failures were aggregated into critical risk factors for these major accidents. The findings of this study revealed empirically that there are broadly five critical risk factors that influence the risk of major accidents: (i) design flaws, (ii) site process safety management, (iii) barrier management / control, (iv) operating procedures, and (v) process safety culture. These findings highlight that major accident risks can accumulate “dangerous” from the impairment of safety-critical barriers and that the operators of petroleum and chemical facilities may be blind-sided to the cumulative risk of the impairments. The findings also highlight the need for a proactive risk assessment tool with the capability to check the health of safety-critical barriers on a real time basis, in an operating plant. The use of Tripod Beta as a tool in accident causation analysis is recommended