Solution of Linear Programming Problems using a Neural Network with Non-Linear Feedback

This paper presents a recurrent neural circuit for solving linear programming problems. The objective is to minimize a linear cost function subject to linear constraints. The proposed circuit employs non-linear feedback, in the form of unipolar comparators, to introduce transcendental terms in the energy function ensuring fast convergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit compares favorably with other proposed circuits for the same task. PSPICE simulation results are presented for a chosen optimization problem and are found to agree with the algebraic solution. Hardware test results for a 2–variable problem further serve to strengthen the proposed theory

Enhancing Performance and Reducing Emissions in Natural Gas Aspirated Engines through Machine Learning Algorithm

In an era where the global energy landscape is increasingly defined by the dual imperatives of efficiency and sustainability, the natural gas sector stands at a crucial juncture. The engines powering this sector, especially Natural Gas Fired Reciprocating Engines (NGFRE), are well known for their performance as well as considerable emissions, posing a stark challenge to environmental sustainability goals. This thesis addresses this pivotal issue, presenting a machine learning-based solution to optimize NGFRE performance while substantially reducing their environmental footprint. The research is anchored in an experimental framework involving the AJAX DPC-81 engine compressor, evaluated across a spectrum of operational loads from 40% to 75%. The study leverages an extensive array of sensors to collect detailed real-time data on engine performance, emissions, and vibration parameters. Central to the methodology is the strategic adjustment of the Air Management System (AMS), varying air/fuel ratio to explore their impact on engine dynamics and emissions. The study also incorporates a comprehensive vibration analysis, providing critical insights into the engine's operational stability under different load conditions. Machine Learning (ML) techniques, including Linear Regression, Artificial Neural Networks (ANN), and Support Vector Machines (SVM), are integrated with a Programmable Logic Controller (PLC). This integration not only facilitates a nuanced analysis of the collected data but also enables the accurate prediction of engine performance, paving the way for real-time adaptive control systems. The findings of this research are both revealing and impactful. A notable instance is observed at a 40% engine load with a 70% bypass valve opening, where emissions of methane (CH4) plummet by 64%, nitrogen oxides (NOx) by 52%, and Volatile Organic Compounds (VOC) by 50%. This substantial decrease highlights the effectiveness of the ML-driven approach in curbing harmful emissions. Further, the study unveils the manipulation of the bypass valve position can lead to enhanced fuel efficiency and improved engine stability. For example, at a 75% engine load, the research demonstrates that optimal emission reduction is achieved with a mere 10% bypass valve opening, illuminating the delicate interplay between engine load parameters and environmental emissions. In conclusion, the study demonstrates the effectiveness of ML in enhancing NGFRE performance. It sets a foundation for developing intelligent engine systems that can self-adjust for optimal performance and minimal environmental impact, forging a path to a future where the two are seamlessly integrated

Local-Partial Signal Combining Schemes for Cell-Free Large-Scale MU-MIMO Systems with Limited Fronthaul Capacity and Spatial Correlation Channels

Cell-free large-scale multi-user MIMO is a promising technology for the 5G-and-beyond mobile communication networks. Scalable signal processing is the key challenge in achieving the benefits of cell-free systems. This study examines a distributed approach for cell-free deployment with user-centric configuration and finite fronthaul capacity. Moreover, the impact of scaling the pilot length, the number of access points (APs), and the number of antennas per AP on the achievable average spectral efficiency are investigated. Using the dynamic cooperative clustering (DCC) technique and large-scale fading decoding process, we derive an approximation of the signal-tointerference-plus-noise ratio in the criteria of two local combining schemes: Local-Partial Regularized Zero Forcing (RZF) and Local Maximum Ratio (MR). The results indicate that distributed approaches in the cell-free system have the advantage of decreasing the fronthaul signaling and the computing complexity. The results also show that the Local-Partial RZF provides the highest average spectral efficiency among all the distributed combining schemes because the computational complexity of the Local-Partial RZF is independent of the UTs. Therefore, it does not grow as the number of user terminals (UTs) increases

Glycated Lysine Residues: A Marker for Non-Enzymatic Protein Glycation in Age-Related Diseases

Nonenzymatic glycosylation or glycation of macromolecules, especially proteins leading to their oxidation, play an important role in diseases. Glycation of proteins primarily results in the formation of an early stage and stable Amadori-lysine product which undergo further irreversible chemical reactions to form advanced glycation endproducts (AGEs). This review focuses these products in lysine rich proteins such as collagen and human serum albumin for their role in aging and age-related diseases. Antigenic characteristics of glycated lysine residues in proteins together with the presence of serum autoantibodies to the glycated lysine products and lysine-rich proteins in diabetes and arthritis patients indicates that these modified lysine residues may be a novel biomarker for protein glycation in aging and age-related diseases.</jats:p

Perception and practices regarding cannabis consumption in Karachi, Pakistan: A cross-sectional study

Several studies have been carried out regarding the awareness and usage of cannabis around the world, especially in developed countries. Pakistan ranks amongst the top nations in regards to cannabis consumption. However, the amount of literature shedding light on people\u27s perception, knowledge and practices are scarce. Therefore, the authors sought to establish a baseline study to ignite the discussion on the possibility of cannabis\u27 induction in the medical field in Pakistan, and additionally provide a foundation for further research. The purpose of this study was to investigate the level of understanding and consumption practices in Karachi with respondents from different socio-economic backgrounds, age groups and gender regarding cannabis use and assessing the awareness of the general population. The null hypothesis is that the usage of cannabis does not have a significant correlation with age, gender, or socio-economic status of a population. We conducted a cross-sectional study in November 2018 using convenience sampling and interviewed 518 individuals for their gender, age, and socio-economic status, to determine their knowledge, attitudes, and practices regarding cannabis usage. The participants were questioned about their knowledge and its source. Attitudes were judged using three and five-point Likert scales while questions regarding practices centered upon the past and current usage of cannabis. One-way analysis of variance and chi-square tests were used as the primary statistical tests. Out of the 518 people who responded, more than half of the respondents were males (n = 340, 65.6%). The majority was familiar with the use of cannabis (n = 514, 99.2%), and the different ways in which it is consumed (n = 435, 84%). About one-third of the participants happened to consume cannabis (n = 168, 32.4%), and a quarter mentioned recreational use/curiosity as the principal reason (n = 134, 25.9%). Majority of the respondents agreed upon the harmful effects of consuming cannabis (n = 364, 70.3%), while when compared to other inimical drugs, half of them believed it to be less harmful (n = 259, 50%). Besides, an overwhelming majority stated, that if they were to consume cannabis, they would not consider taking permission from their parents/guardians (n = 441, 85.2%). Concerning legality, three-fifths of the participants chose cannabis to remain illegal in Pakistan (n = 307, 59.3%) and, for not consuming/quitting cannabis, the primary reason chosen was its harmful consequences (n = 210, 40.5%). Our study revealed that knowledge about usage of cannabis still requires a great deal of attention. Only individuals from higher socio-economic backgrounds have a positive attitude towards cannabis usage and are aware of it. There is an urgent need for awareness programs that especially reach out to the lower socio-economic status population, who otherwise do not have access to essential information resources. We also found that males were more likely to be consumers and to have more knowledge about cannabis, therefore, it is equally important to educate females about this topic so that an informed discussion about cannabis use and its medical benefits can be generated in Pakistan