Learning-Augmented K-Means Clustering Using Dimensional Reduction

Learning augmented is a machine learning concept built to improve the performance of a method or model, such as enhancing its ability to predict and generalize data or features, or testing the reliability of the method by introducing noise and other factors. On the other hand, clustering is a fundamental aspect of data analysis and has long been used to understand the structure of large datasets. Despite its long history, the k-means algorithm still faces challenges. One approach, as suggested by Ergun et al,is to use a predictor to minimize the sum of squared distances between each data point and a specified centroid. However, it is known that the computational cost of this algorithm increases with the value of k, and it often gets stuck in local minima. In response to these challenges, we propose a solution to reduce the dimensionality of the dataset using Principal Component Analysis (PCA). It is worth noting that when using k values of 10 and 25, the proposed algorithm yields lower cost results compared to running it without PCA. "Principal component analysis (PCA) is the problem of fitting a low-dimensional affine subspace to a set of data points in a high-dimensional space. PCA is well-established in the literature and has become one of the most useful tools for data modeling, compression, and visualization."Comment: acmart-LaTeX2e v1.84 17 pages with 12 figure

Nitroheterocyclic drugs cure experimental <i>Trypanosoma cruzi</i> infections more effectively in the chronic stage than in the acute stage

The insect-transmitted protozoan parasite Trypanosoma cruzi is the causative agent of Chagas disease, and infects 5-8 million people in Latin America. Chagas disease is characterised by an acute phase, which is partially resolved by the immune system, but then develops as a chronic life-long infection. There is a consensus that the front-line drugs benznidazole and nifurtimox are more effective against the acute stage in both clinical and experimental settings. However, confirmative studies have been restricted by difficulties in demonstrating sterile parasitological cure. Here, we describe a systematic study of nitroheterocyclic drug efficacy using highly sensitive bioluminescence imaging of murine infections. Unexpectedly, we find both drugs are more effective at curing chronic infections, judged by treatment duration and therapeutic dose. This was not associated with factors that differentially influence plasma drug concentrations in the two disease stages. We also observed that fexinidazole and fexinidazole sulfone are more effective than benznidazole and nifurtimox as curative treatments, particularly for acute stage infections, most likely as a result of the higher and more prolonged exposure of the sulfone derivative. If these findings are translatable to human patients, they will have important implications for treatment strategies

Mobile LiDAR Data and Imagery for Digital Twin Generation

Low-cost sensor solutions such as smartphones provide a great opportunity for democratization of mapping among different communities including those working in digital twin application areas. Smartphone acquired imagery and/or built-in LiDAR sensors provide relatively dense point clouds with limited accuracy especially in the absence of GNSS. This type of scanning tool can provide linear measurements in an inexpensive way and can be used with minimal operator training. In this study, we provide two solutions for improving the accuracy of the final point clouds produced by iPhone-based LiDAR and images. One solution utilizes length observations as constraints in the network. The other solution incorporates loosely coupled perspective centre (PC) positions obtained by ultrasonic ranging into a photogrammetric bundle adjustment. The test results show that inclusion of the length observations in the solution improves the relative accuracy of the point cloud for applications such as culvert mapping where absolute accuracy is not of high necessity. In the indoor mapping case, the relative point cloud accuracy for the solutions without and with the PC observations is approximately the same. However, image alignment success and computation time are significantly improved by including the PC observations. Moreover, the inclusion of PC observations provided better compensation of systematic image point errors

Towards Urban Heat Loss Modeling Using Building Digital Twin

Seventy percent of global greenhouse gas (GHG) emissions originate from urban areas, with urban heat loss contributing significantly to energy consumption (UNEP, 2020). Digital twins offer a potential solution and insight into the problem and its causes. This is a study started as an undergraduate Engineering Capstone Project with a collaborative effort between the University of New Brunswick and the National Research Council of Canada to develop a workflow to aid thermal efficiency modeling using Digital twins. This project uses the University of New Brunswick (UNB) Fredericton campus as a case study to capture UAV, nadir perspective LiDAR, Panchromatic imagery and long wave infrared (LWIR) thermal imagery. The workflow includes 4 major steps following the preprocessing: (1) creating point clouds from the LiDAR and Panchromatic sources, (2) merging point clouds using grid-based segmentation and iterative closest point algorithm (ICP), (3) classifying the point cloud using Point CNN networks aided by manual refinement, and (4) overlaying thermal data. The resulting digital twin achieved a high level of spatial alignment accuracy, with 95% of points falling on building surfaces falling within an 11 cm tolerance as assessed by quadric cloud-to-cloud distance. Semantic classification performed using Point CNN and faster R-CNN object detection identified fa&ccedil;ade features such as windows and doors with a precision of 91.8% and an F1 score of 83%. Thermal data was successfully integrated and converted to approximate temperature values, enabling further analysis of surface heat behavior and laying the groundwork for future energy modeling applications. This case study demonstrates a scalable framework for high-detail drone based digital twin development with practical relevance to urban thermal efficiency analysis

Improving Active Resonance Damping and Unbalanced Voltage Mitigation Based on Combined DDSRF and Washout Filter in Islanded Microgrids

The existence of imbalanced single- and two-phase loads, coupled with short-circuit faults, contributes to voltage imbalance. It is inevitable to use LCL filters to remove harmonics caused by converter switching. While effective in removing harmonic components, these filters may inadvertently amplify and propagate resonance. Both voltage imbalance and active resonance significantly degrade power quality. This paper proposes a multi-level hierarchical control system to mitigate both phenomena. At Level 1, the controller incorporates proportional-resonant regulators for current and voltage. Level 2 employs common droop control to establish microgrid voltage and frequency reference values. Level 3 introduces a washout filter with an adjustable Kd gain, capable of attenuating active resonance harmonics originating from the LCL filter. Finally, Level 4 integrates a controller based on extracting positive and negative sequence components using the dual decoupled synchronous reference frame (DDSRF) algorithm. This controller effectively compensates for negative sequence components of voltage in the load bus, balancing load voltage. Simulation results for a standalone microgrid demonstrate that the proposed 4-level hierarchical control algorithm can reduce the load bus voltage imbalance from 10% to 2.5% under the most challenging conditions. It diminishes harmonic distortion caused by active resonance from 11% to 2.5%. The presented control algorithm can perform optimally under dynamic changes of active and reactive power of nonlinear and imbalanced loads. The main merit of this approach is not to use physical devices and Flexible Alternative Current Transmission Systems (FACTS), such as Active Power Filters (APF), Distribution Static Synchronous Compensators (DsSTATCOM), SVC, etc

Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm

The escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta-heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio-dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future

Maternal Early-Life Risk Factors and Later Gestational Diabetes Mellitus: A Cross-Sectional Analysis of the UAE Healthy Future Study (UAEHFS).

Limited studies have focused on maternal early-life risk factors and the later development of gestational diabetes mellitus (GDM). We aimed to estimate the GDM prevalence and examine the associations of maternal early-life risk factors, namely: maternal birthweight, parental smoking at birth, childhood urbanicity, ever-breastfed, parental education attainment, parental history of diabetes, childhood overall health, childhood body size, and childhood height, with later GDM. This was a retrospective cross-sectional study using the UAE Healthy Future Study (UAEHFS) baseline data (February 2016 to April 2022) on 702 ever-married women aged 18 to 67 years. We fitted a Poisson regression to estimate the risk ratio (RR) for later GDM and its 95% confidence interval (CI). The GDM prevalence was 5.1%. In the fully adjusted model, females with low birthweight were four times more likely (RR 4.04, 95% CI 1.36-12.0) and females with a parental history of diabetes were nearly three times more likely (RR 2.86, 95% CI 1.10-7.43) to report later GDM. In conclusion, maternal birthweight and parental history of diabetes were significantly associated with later GDM. Close glucose monitoring during pregnancy among females with either a low birth weight and/or parental history of diabetes might help to prevent GDM among this high-risk group