Session 8: \u3cem\u3eMachine Learning based Behavior of Non-OPEC Global Supply in Crude Oil Price Determinism\u3c/em\u3e

Abstract While studies on global oil price variability, occasioned by OPEC crude oil supply, is well documented in energy literature; the impact assessment of non-OPEC global oil supply on price variability, on the other hand, has not received commensurate attention. Given this gap, the primary objective of this study, therefore, is to estimate the magnitude of oil price determinism that is explained by the share of non-OPEC’s global crude oil supply. Using secondary sources of data collection method, data for target variable will be collected from the US Federal Reserve, as it relates to annual crude oil price variability, while data for the two explanatory variables of OPEC oil supply, and non-OPEC oil supply will be gathered from the OPEC Annual Statistical Bulletin. The dataset so generated will covers the period from 2000 to 2022 using the supervised Machine Learning Random Forest Model in scientific writing. To assess the performance of this model, the collected data will be shuffled and split into two parts with the help of the train­-test-split function in the Scikit-learn library. While the study, among the many determinants of oil price variability, is limited to only two explanatory variables of OPEC and non-OPEC oil supply, it is the expectation of the study to isolate, and see what magnitude of price variability that is explained by non-OPEC supply behavior. The accuracy of the model preliminary shows a test result of about 0.96 with an F1_score value of 0.88. Keywords: Variability, behavior, determinism, oil price, oil suppl

Malware Materials Detection by Clustering the Sequence using Hidden Markov Model

The exponential development in (malware)malicious development in current times and the growing scenario of challenges that malware poses to network atmospheres, like as  the network and smart networks, highlight the need for further investigation in data technology and privacy forensics on computer network protection.The approach described in this scenario curve distinguishes "types" of malware groups that complex is extending, more obfuscated and more varied in nature. We suggest a hybrid strategy integrating signature recognition elements with machine learning-based approaches for classifying families of malware.The approach is performed using PHMM of the behavior features of the species of malware. This research article illustrates the method of modelling and learning developed PHMM using sequences derived from the discovery of the paramount aspects of each malware family, and the recognized orders produced during the development of Multiple Sequences Alignment (MSA). Since all file sets are not dangerous, and the aim would be to separate their malicious parts from the genuine ones along with put greater focus on them to raise the possibility of malware finding by ensuring the least case effect on the genuine parts. Founded on the "consensus series," the investigational findings indicate that even though minimal training data are usable, our proposed method outperforms other HMM-related strategies

Approaches to managing urbanization: A case study of the importance of developing secondary cities in Rwanda

The successful management of urban growth is fundamental in achieving sustainable development. In the absence of sound macro-economic policy, adequate investments in infrastructure and well-functioning institutions, rapid urbanization can inhibit growth. The Rwandan government has set out plans to mitigate mass unplanned urbanization to Kigali by focusing on creating economic centers of opportunity across Rwanda. The cities of Muhanga, Rubavu, Huye, Rusizi, Musanze and Nyagatare were selected as ‘secondary cities’ with the objective of off-loading pressure on Kigali by developing infrastructure, social and economic opportunities in these cities. This thesis incorporates a mixed-methodology design and provides a policy analysis of Rwanda’s attempts to ensure more balanced regional growth through the development of its secondary cities

Computational diagnostics of diesel spray end-of-injection combustion recession

Diesel engines are efficient, reliable, and durable, making them a popular choice for ground transportation and heavy-duty applications. While emissions controls are challenging for diesel engines, strategies such as low-temperature combustion (LTC) strategies have been proven to reduce nitrogen oxides and particulate matter emissions that are common in diesel engines. However, these strategies can result in an increased fraction of the fuel spray being unburnt, leading to unburned hydrocarbon (UHC) emissions. Previous studies have indicated that end-of-injection (EOI) processes can support ignition near the nozzle, thereby consuming the UHCs after EOI. In particular, combustion recession is an EOI process where high-temperature ignition occurs between the nozzle and flame lift-off length, consuming UHCs in the process. Current literature suggests that combustion recession is likely attributed to auto-ignition rather than flame propagation. This is inferred through the analysis of the flame structures at different boundary conditions. However, previous studies have not presented a quantitative analysis of whether combustion recession is driven by auto-ignition or flame propagation. Chemical explosive mode analysis (CEMA) is a flame diagnostic tool based on the eigenanalysis for the chemical Jacobian to identify critical combustion events and has been used in various types of combustion setups, including LTC of diesel sprays. CEMA has been successfully used to determine flame features and is also able to identify the local propagation regimes within a flame which includes autoignition, deflagration, and extinction. Therefore, the objective of this study is to further the understanding of the combustion recession of diesel sprays through computational fluid dynamics (CFD) at LTC conditions where a customized CEMA is implemented to study the EOI combustion modes. The study involves large eddy simulations of a single-hole injection of n-dodecane in an Eulerian-Lagrangian framework performed in the CFD solver CONVERGE. The boundary conditions of the study are in the range of Engine Combustion Network’s “Spray A” conditions. At the baseline boundary conditions of “Spray A”, two chemical kinetic mechanisms are compared with experimental data. With the selected chemical mechanism, the custom implementation of CEMA is used to determine the flame features AEOI and the propagation regime of combustion recession to provide insight into the flame re-initiation mechanism. Through CEMA, it was determined that combustion recession is auto-ignition dominated: the reactive mixtures near the nozzle auto-ignite, and the ignited kernels develop through flame propagation. Lower ambient temperatures cannot support auto-ignition, which leads to the extinction of the flame near the nozzle

Painless Presentation of a Deadly Disease:Type A Aortic Dissection Requiring the Bentall Procedure

Aortic dissection is a relatively uncommon, although catastrophic, disease which requires early and accurate diagnosis and treatment for patient survival. Aortic dissection can be difficult to diagnose due to the diverse symptom presentation, which can lead to later diagnosis, resulting in a higher mortality rate. Here we present a case of type A aortic dissection with a varied symptom presentation, highlighting the importance of early detection and the Bentall procedure for management of such cases. A 50-year-old man with no known medical history presented with bilateral lower extremity swelling and fatigue for 2 weeks. The patient denied any chest pain or dyspnoea. Vital signs showed blood pressure of 160/76 mmHg, pulse of 103 bpm, respiratory rate of 18, and temperature of 36.7°C. Laboratory findings indicated a BNP of 1901 pg/ml and troponin of 0.5 ng/ml. An initial diagnosis of decompensated heart failure was made, and IV Lasix was started. Subsequently, an echocardiogram indicated an EF of 50–55% and ascending dissection of the aorta. A CT angiogram of the chest and abdomen confirmed this diagnosis. This patient presented with unusual symptoms of aortic dissection without the typical presentation of chest pain. It is important to consider aortic dissection in a cardiac-related case as prompt imaging can help confirm the diagnosis. We explore the risks and benefits of the Bentall procedure for the management and early detection of aortic dissectio

3D Printing‐Enabled Design and Manufacturing Strategies for Batteries: A Review

Lithium-ion batteries (LIBs) have significantly impacted the daily lives, finding broad applications in various industries such as consumer electronics, electric vehicles, medical devices, aerospace, and power tools. However, they still face issues (i.e., safety due to dendrite propagation, manufacturing cost, random porosities, and basic & planar geometries) that hinder their widespread applications as the demand for LIBs rapidly increases in all sectors due to their high energy and power density values compared to other batteries. Additive manufacturing (AM) is a promising technique for creating precise and programmable structures in energy storage devices. This review first summarizes light, filament, powder, and jetting-based 3D printing methods with the status on current trends and limitations for each AM technology. The paper also delves into 3D printing-enabled electrodes (both anodes and cathodes) and solid-state electrolytes for LIBs, emphasizing the current state-of-the-art materials, manufacturing methods, and properties/performance. Additionally, the current challenges in the AM for electrochemical energy storage (EES) applications, including limited materials, low processing precision, codesign/comanufacturing concepts for complete battery printing, machine learning (ML)/artificial intelligence (AI) for processing optimization and data analysis, environmental risks, and the potential of 4D printing in advanced battery applications, are also presented

Participatory Research for Low-resourced Machine Translation:A Case Study in African Languages

Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved. "Low-resourced"-ness is a complex problem going beyond data availability and reflects systemic problems in society. In this paper, we focus on the task of Machine Translation (MT), that plays a crucial role for information accessibility and communication worldwide. Despite immense improvements in MT over the past decade, MT is centered around a few high-resourced languages. As MT researchers cannot solve the problem of low-resourcedness alone, we propose participatory research as a means to involve all necessary agents required in the MT development process. We demonstrate the feasibility and scalability of participatory research with a case study on MT for African languages. Its implementation leads to a collection of novel translation datasets, MT benchmarks for over 30 languages, with human evaluations for a third of them, and enables participants without formal training to make a unique scientific contribution. Benchmarks, models, data, code, and evaluation results are released under https://github.com/masakhane-io/masakhane-mt

Session 7 : \u3cem\u3eInvestigating Application of Deep Neural Networks in Intrusion Detection System Design\u3c/em\u3e

Despite decades of development, existing IDSs still face challenges in improving detection accuracy, evasion, and detection of unknown attacks. To solve these problems, many researchers have focused on designing and developing IDSs that use Deep Neural Networks (DNN) which provides advanced methods of threat investigation and detection. Given this reason, the motivation of this research then, is to learn how effective applications of Deep Neural Networks (ANN) can accurately detect and identify malicious network intrusion, while advancing the frontiers of their optimal potential use in network intrusion detection. Using the ASNM-TUN dataset, the study used a Multilayer Perceptron modeling approach in Deep Neural Network to identify network intrusions, in addition to distinguishing them in terms of legitimate network traffic, direct network attacks, and obfuscated network attacks. To further enhance the speed and efficiency of this DNN solution, a thorough feature selection technique called Forward Feature Selection (FFS), which resulted in a significant reduction in the feature subset, was implemented. Using the Multilayer Perceptron model, test results demonstrate no support for the model to accurately and correctly distinguish the classification of network intrusion