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Background Precision medicine for cancer treatment relies on an accurate pathological diagnosis. The number of known tumor classes has increased rapidly, and reliance on traditional methods of histopathologic classification alone has become unfeasible. To help reduce variability, validation costs, and standardize the histopathological diagnostic process, supervised machine learning models using DNA-methylation data have been developed for tumor classification. These methods require large labeled training data sets to obtain clinically acceptable classification accuracy. While there is abundant unlabeled epigenetic data across multiple databases, labeling pathology data for machine learning models is time-consuming and resource-intensive, especially for rare tumor types. Semi-supervised learning (SSL) approaches have been used to maximize the utility of labeled and unlabeled data for classification tasks and are effectively applied in genomics. SSL methods have not yet been explored with epigenetic data nor demonstrated beneficial to central nervous system (CNS) tumor classification. Results This paper explores the application of semi-supervised machine learning on methylation data to improve the accuracy of supervised learning models in classifying CNS tumors. We comprehensively evaluated 11 SSL methods and developed a novel combination approach that included a self-training with editing using support vector machine (SETRED-SVM) model and an L2-penalized, multinomial logistic regression model to obtain high confidence labels from a few labeled instances. Results across eight random forest and neural net models show that the pseudo-labels derived from our SSL method can significantly increase prediction accuracy for 82 CNS tumors and 9 normal controls. Conclusions The proposed combination of semi-supervised technique and multinomial logistic regression holds the potential to leverage the abundant publicly available unlabeled methylation data effectively. Such an approach is highly beneficial in providing additional training examples, especially for scarce tumor types, to boost the prediction accuracy of supervised models.

This paper reviews the optimization strategies for the optimal simultaneous allocation of distributed generation (DG) and shunts capacitor banks (SCBs) in electrical distribution networks. These optimization strategies aim to determine the optimal size, location, and combination of DGs and SCBs to constitute a techno-economic system while satisfying the constraints and energy demand of the load. The optimization strategies explicitly reviewed include the problem formulations, optimization techniques, restrictions posed for optimization problems, decision variables, and network operating modes typically assumed while allocating the DGs and SCBs. In addition, there is an attempt to highlight the limitations of the existing literature and future research directions. This study undertakes a comprehensive review of the literature that systematically considers the simultaneous application of DGs and SCBs to advance the existing literature, which lacks such a review. Expectedly, this review will serve as a principle platform for researchers intending to explore the subject area for further improvement.

In remote areas, extending a power line to the primary electricity grid can be very expensive and power losses are high, making connections to the grid almost impossible. A well-designed microgrid that integrates renewable energy resources can help remote areas reduce investment costs and power losses while providing a reliable power source. Therefore, investigating the design of an independent and economically practical microgrid system for these areas is necessary and plays an important role. This paper introduces a design procedure to design an isolated microgrid using HOMER software (HOMERPro 3.14.5) for remote areas. In Vietnam, due to the obstruction of the mountainous terrain or the isolated island location, many remote areas or islands need electrification. A simple case study of a hybrid system with a 60 kW peak load demand on Con Dao island in Vietnam is used to illustrate the proposed design method. Specifically, a hybrid system that includes a PV system, batteries, and a diesel generator is designed. To provide the full information of the designed hybrid system designed, each solution is analyzed and evaluated in detail according to the sensitivity parameters.

The doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) suffers from voltage and frequency fluctuations due to the stochastic nature of wind speed as well as nonlinear loads. Moreover, the high penetration of wind energy into the power grid is a challenge for its smooth operation. Hence, symmetrical faults are most intense, inflicting the stator winding to low voltage, disturbing the low-voltage ride-through (LVRT) functionality of a DFIG. The vector control strategy with proportional–integral (PI) controllers was used to control rotor-side converter (RSC) and grid-side converter (GSC) parameters. During a symmetrical fault, however, a series grid-side converter (SGSC) with a shunt injection transformer on the stator side was used to keep the rotor current at an acceptable level in accordance with grid code requirements (GCRs). For the validation of results, the proposed scheme of PI + SGSC is compared with PI and a combination of PI with Dynamic Impedance Fault Current Limiter (DIFCL). The MATLAB simulation results demonstrate that the proposed scheme provides superior performance by providing 77.6% and 20.61% improved performance in rotor current compared to that of PI and PI + DIFCL control schemes for improving the LVRT performance of DFIG.

Macrophages play a major role in the removal of foreign materials, including nano-sized materials, such as nanomedicines and other nanoparticles, which they accumulate very efficiently. Because of this, it is recognized that for a safe development of nanotechnologies and nanomedicine, it is essential to investigate potential effects induced by nano-sized materials on macrophages. To this aim, in this work, a recently established model of primary murine alveolar-like macrophages was used to investigate macrophage responses to two well-known nanoparticle models: 50 nm amino-modified polystyrene, known to induce cell death via lysosomal damage and apoptosis in different cell types, and 50 nm silica nanoparticles, which are generally considered non-toxic. Then, a time-resolved study was performed to characterize in detail the response of the macrophages following exposure to the two nanoparticles. As expected, exposure to the amino-modified polystyrene led to cell death, but surprisingly no lysosomal swelling or apoptosis were detected. On the contrary, a peculiar mitochondrial membrane hyperpolarization was observed, accompanied by endoplasmic reticulum stress (ER stress), increased cellular reactive oxygen species (ROS) and changes of metabolic activity, ultimately leading to cell death. Strong toxic responses were observed also after exposure to silica, which included mitochondrial ROS production, mitochondrial depolarization and cell death by apoptosis. Overall, these results showed that exposure to the two nanoparticles led to a very different series of intracellular events, suggesting that the macrophages responded differently to the two nanoparticle models. Similar time-resolved studies are required to characterize the response of macrophages to nanoparticles, as a key parameter in nanosafety assessment.

Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues.

Due to the large number of distribution transformers in the distribution grid, the status of distribution transformers plays an important role in ensuring the safe and reliable operation of the these grids. To evaluate the distribution transformer health, many assessment techniques have been studied and developed. These tools will support the transformer operators in predicting the status of the distribution transformer and responding effectively. This paper will review the literature in the area, analyze the latest techniques as well as highlight the advantages and disadvantages of current methodologies.

In this paper, we present a low-cost health assessment system for oil-immersed service transformers using a monitoring device to measure energy in real time. By assessing the important level of transformer components, three indicators, top oil temperature, vibration, and transformer load, were selected as main indicators to investigate the service transformer’s condition. An evaluation system using Fuzzy logic method is also presented in the paper to support monitor transformer health without adding the extra cost of installing expensive sensors. Different testing scenarios with different case studies were carried out on a simulated 50 kVA oil-immersed service transformer to express the feasibility and effectiveness of this low-cost, fast response health assessment system.

EV development is being prioritized in order to attain the target of net zero emissions by 2050. Electric vehicles have the potential to decrease greenhouse gas (GHG) emissions, which contribute to global warming. The driving range of electric vehicles is a significant limitation that prevents people from using them generally. This paper proposes a comprehensive model for calculating the amount of energy needed to charge EVs for a scheduled trip. The model contains anticipated consumption energy for the whole trip as well as contingency energy to account for unpredictable conditions. The model is simple to apply to various types of electric vehicles and produces results with sufficient precision. A number of driving tests with different road characteristics and weather conditions were implemented to evaluate the success of the proposed method. The findings could help the users feel more confidence when driving EVs, promote the usage of EVs, and advocate for the increased use of green and renewable energy sources.

Offshore wind energy is a renewable energy source that is developing fast. It is considered to be the most promising energy source in the next decade. Besides, the expanding trend for this technology requires the consideration of diversified seabeds. In deep seabeds, floating offshore wind technology (FOWT) is needed. For this latter technology, such as for conventional WT, we need to consider aspects related to performance, aerodynamic force, and forces during operation. In this paper, a two-bladed downwind wind turbine model is utilized to conduct experiments. The collective pitch and cyclic pitch angle are adjusted using swashplated equipment. The fluid forces and moments acting on the rotor surface are measured by a six-component balancing system. By changing the pitch angle of the wind turbine blades, attempts are made to manage the fluid forces generated on the rotor surface. Under varied uniform wind velocities of 7, 8, 9, and 10 m/s, the effect of collective pitch control and cyclic pitch control on the power coefficient and thrust coefficient of FOWT is then discussed. Furthermore, at a wind speed of 10 m/s, both the power coefficient and loads are investigated as the pitch angle and yaw angle change. Experimental results indicate that the combined moment magnitude can be controlled by changing the pitch-angle amplitude. The power coefficient is adjusted by the cyclic pitch-angle controller when the pitch-angle phase changes. In addition, the thrust coefficient fluctuated when the pitch angle changed in the oblique inflow wind condition.