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Abstract Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer (LACC), but its responsiveness varies among patients. A reliable tool for predicting CRT responses is necessary for personalized cancer treatment. In this study, we constructed prediction models using handcrafted radiomics (HCR) and deep learning radiomics (DLR) based on pretreatment MRI data to predict CRT response in LACC. Furthermore, we investigated the potential improvement in prediction performance by incorporating clinical factors. A total of 252 LACC patients undergoing curative chemoradiotherapy are included. The patients are randomly divided into two independent groups for the training (167 patients) and test datasets (85 patients). Contrast-enhanced T1- and T2-weighted MR scans are obtained. For HCR analysis, 1890 imaging features are extracted and a support vector machine classifier with a five-fold cross-validation is trained on training dataset to predict CRT response and subsequently validated on test dataset. For DLR analysis, a 3-dimensional convolutional neural network was trained on training dataset and validated on test dataset. In conclusion, both HCR and DLR models could predict CRT responses in patients with LACC. The integration of clinical factors into radiomics prediction models tended to improve performance in HCR analysis. Our findings may contribute to the development of personalized treatment strategies for LACC patients.

Objectives: This study aimed to build a comprehensive deep-learning model for the prediction of radiation pneumonitis using chest computed tomography (CT), clinical, dosimetric, and laboratory data. Introduction: Radiation therapy is an effective tool for treating patients with lung cancer. Despite its effectiveness, the risk of radiation pneumonitis limits its application. Although several studies have demonstrated models to predict radiation pneumonitis, no reliable model has been developed yet. Herein, we developed prediction models using pretreatment chest CT and various clinical data to assess the likelihood of radiation pneumonitis in lung cancer patients. Methods: This retrospective study analyzed 3-dimensional (3D) lung volume data from chest CT scans and 27 features including dosimetric, clinical, and laboratory data from 548 patients who were treated at our institution between 2010 and 2021. We developed a neural network, named MergeNet, which processes lung 3D CT, clinical, dosimetric, and laboratory data. The MergeNet integrates a convolutional neural network with subsequent fully connected layers. A support vector machine (SVM) and light gradient boosting machine (LGBM) model were also implemented for comparison. For comparison, the convolution-only neural network was implemented as well. Three-dimensional Resnet-10 network and 4-fold cross-validation were used. Results: Classification performance was quantified by using the area under the receiver operative characteristic curve (AUC) metrics. MergeNet showed the AUC of 0.689. SVM, LGBM, and convolution-only networks showed AUCs of 0.525, 0.541, and 0.550, respectively. Application of DeLong test to pairs of receiver operating characteristic curves respectively yielded P values of .001 for the MergeNet–SVM pair and 0.001 for the MergeNet–LGBM pair. Conclusion: The MergeNet model, which incorporates chest CT, clinical, dosimetric, and laboratory data, demonstrated superior performance compared to other models. However, since its prediction performance has not yet reached an efficient level for clinical application, further research is required. Contribution: This study showed that MergeNet may be an effective means to predict radiation pneumonitis. Various predictive factors can be used together for the radiation pneumonitis prediction task via the MergeNet.

A modular multilevel converter (MMC) synthesizes output voltages by combining voltages from various submodules (SMs) connected in series; thus, it is ideal for high-voltage transmission and distribution ranging from tens to several hundreds of kilovolts. However, MMCs incur high costs and losses, thus necessitating reliable operation. Research has been conducted to assess and enhance MMC reliability. Prior studies have presumed an exponential distribution for the reliability of SMs, including redundancy, thus implying that the failure rate does not change over time. This assumption signifies a constant system availability for the converter station, regardless of time, thus presenting a limitation in adequately comprehending the breakdown impact caused by a failure of the converter station. Additionally, as no standard exists for determining redundancy in medium-voltage direct current (MVDC) systems, a methodology is required to establish a redundancy criterion during the design process. Particularly for MVDC systems, the impact of power outages must be evaluated by considering the cost of expected outages based on system availability, as the load is interconnected. As a solution, we propose a method to assess the tradeoff between system availability and cost for an MVDC MMC, employing the Weibull distribution-based SM failure rate. The L-BFGS algorithm calculates the optimal value of the availability-cost function, and the number of redundant modules minimizing total operating cost is evaluated. Our case study determines the appropriate number of redundancy modules based on outage costs. The proposed methodology enables redundancy design that considers both reliability and operational costs, thus providing a practical solution to the problem of determining the number of redundant modules needed in MMCs.

Abstract Background To map anatomic patterns of para-aortic lymph node (PALN) recurrence in cervical cancer patients and validate currently available guidelines on PA clinical target volumes (CTV). Methods Cervical cancer patients who developed PALN recurrence were included. The PALNs were classified as left-lateral para-aortic (LPA), aorto-caval (AC), and right para-caval (RPC). Four PA CTVs were contoured for each patient to validate PALN coverage. CTVRTOG was contoured based on the Radiation Therapy Oncology Group guideline. CTVK was contoured as proposed by Keenan et al. CTVM was contoured by expanding symmetrical margins around the aorta and inferior vena cava of 7 mm up to the T12–L1 interspace. CTVnew was created by modifying CTVRTOG to obtain better coverage. Results We identified 92 PALNs in 35 cervical cancer patients. 46.8% of the PALNs were at LPA, 38.0% were at AC, and 15.2% were at RPC areas. CTVRTOG, CTVK, and CTVM covered 87.0%, 88.0%, and 62.0% of all PALNs, respectively. PALN recurrence above the left renal vein was associated with PALN involvement at diagnosis (p = 0.043). Extending upper border to the superior mesenteric artery allowed the CTVnew to cover 96.7% of all PALNs and all nodes in 91.4% of patients. Conclusion CTVRTOG and CTVK encompassed most PALN recurrences. For high-risk patients, such as those having PALN involvement at diagnosis, extending the superior border of CTV from the left renal vein to superior mesenteric artery could be considered.

Abstract Background Radiomics is a promising field in oncology imaging. However, the implementation of radiomics clinically has been limited because its robustness remains unclear. Previous CT and PET studies suggested that radiomic features were sensitive to variations in pixel size and slice thickness of the images. The purpose of this study was to assess robustness of magnetic resonance (MR) radiomic features to pixel size resampling and interpolation in patients with cervical cancer. Methods This retrospective study included 254 patients with a pathological diagnosis of cervical cancer stages IB to IVA who received definitive chemoradiation at our institution between January 2006 and June 2020. Pretreatment MR scans were analyzed. Each region of cervical cancer was segmented on the axial gadolinium-enhanced T1- and T2-weighted images; 107 radiomic features were extracted. MR scans were interpolated and resampled using various slice thicknesses and pixel spaces. Intraclass correlation coefficients (ICCs) were calculated between the original images and images that underwent pixel size resampling (OP), interpolation (OI), or pixel size resampling and interpolation (OP+I) as well as among processed image sets with various pixel spaces (P), various slice thicknesses (I), and both (P + I). Results After feature standardization, ≥86.0% of features showed good robustness when compared between the original and processed images (OP, OI, and OP+I) and ≥ 88.8% of features showed good robustness when processed images were compared (P, I, and P + I). Although most first-order, shape, and texture features showed good robustness, GLSZM small-area emphasis-related features and NGTDM strength were sensitive to variations in pixel size and slice thickness. Conclusion Most MR radiomic features in patients with cervical cancer were robust after pixel size resampling and interpolation following the feature standardization process. The understanding regarding the robustness of individual features after pixel size resampling and interpolation could help future radiomics research.

Abstract Background Current chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients. Methods We included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38 months (range, 5–128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n = 62). The generated models were then tested in the validation dataset (n = 31). Results For predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset. Conclusions Our results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.

We propose an algorithm to set the parameters of volt-var curves to improve the performance of distributed generation. Specifically, we optimally set the volt-var curves of the smart inverter for proper control of the distributed generation output. To improve the overall distribution system performance, we consider the minimization of voltage deviation, system loss, and peak of reactive power in an objective function, thereby providing optimal volt-var curve parameters. The proposed algorithm overcomes various limitations of existing studies. First, we use distribution system factors in a multiobjective optimization function to adjust volt-var curve parameters that deviate from the default settings. Second, we consider the service transformers that affect the system voltage according to the photovoltaic generation output in the test model during optimization. Third, we analyze the system improvement according to the number of parameters to be optimized. Fourth, we analyze the implementation of the optimized volt-var curve according to the practical periods when the smart inverter settings can be updated. Fifth, we evaluate different suitable volt-var curves for multiple photovoltaic generators grouped using clustering. Using the proposed algorithm, the distribution system operator can set the optimal volt-var curve according to the system conditions and requirements. We conducted simulations of the proposed method using OpenDSS, a quasistatic time-series simulation, on a test model reflecting the characteristics of a South Korean distribution system. The simulation results confirm that the overall system performance increases when the optimal volt-var curve settings obtained from the proposed algorithm are used.

The increasing use of photovoltaics (PVs) in distribution systems owing to the low-carbon policy has given rise to the need for various technological changes. In particular, the operation of on-load tap changers (OLTCs) has attracted attention. In traditional distribution systems, the OLTC operates via a line-drop compensator (LDC), which focuses on the load to solve the low-voltage problem; however, the problem of over-voltage caused by PVs persists. Currently, a method for operating an OLTC using the measured voltage is being researched; however, solving the voltage problem for several feeders connected to a main transformer (MT) is not viable. Therefore, this study proposes an OLTC operation method to address the feeder with the largest voltage problem depending on the direction of power flow. The proposed method selects a point where the OLTC operates using the difference between the measured and reference voltages. Setting the reference voltage can solve the problem that occurs due to the direction of power flow. Finally, the effectiveness of the proposed method is verified via case studies. Based on the results, we can conclude that the proposed method effectively solves the voltage problem, and an increase in hosting capacity can be expected.

Owing to the outbreak of COVID-19, researchers are exploring methods to prevent contact and non-contact infections that occur via multiple transmission routes. However, studies on preventing infections caused by droplet transmission in public transportation are insufficient. To prevent the spread of infectious diseases, a new ventilation system in railway vehicles must be developed. In this study, a novel vertical drop airflow (VDA) system is proposed to mitigate the effect of droplet transmission in a high-speed train cabin. The droplet transmission route and droplet fate are investigated using three-dimensional fluid dynamics simulations, performed employing the Eulerian–Lagrangian model. Additionally, a porous model is adopted to simulate the effect of close-fitting masks. The results indicate that 120 s after coughing, the decrease in the droplet number in the VDA system is 72.1% of that observed in the conventional system. Moreover, the VDA system effectively suppresses droplet transmission because the maximum droplet travel distances of the VDA systems are 49.9% to 67.0% of those of the conventional systems. Furthermore, the effect of reducing droplet transmission by wearing a close-fitting mask is confirmed in all systems. Thus, the decrease in both droplet number and droplet transmission area in train cabins validate that the proposed VDA system has an effective airflow design to prevent droplet infection.

Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a specialized hospital setting. Gait is a suitable indicator of musculoskeletal diseases; therefore, we analyzed the gait signal obtained from an inertial-sensor-based wearable gait device as a tool to manage bone loss and muscle loss in daily life. To analyze the inertial-sensor-based gait, the inertial signal was classified into seven gait phases, and descriptive statistical parameters were obtained for each gait phase. Subsequently, explainable artificial intelligence was utilized to analyze the contribution and importance of descriptive statistical parameters on osteopenia and sarcopenia. It was found that XGBoost yielded a high accuracy of 88.69% for osteopenia, whereas the random forest approach showed a high accuracy of 93.75% for sarcopenia. Transfer learning with a ResNet backbone exhibited appropriate performance but showed lower accuracy than the descriptive statistical parameter-based identification result. The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management.

Owing to the increasing penetration level of distributed energy resources (DER) and direct current (DC) load, the usage of low-voltage direct current (LVDC) systems has expanded to achieve efficient operations. However, because the LVDC system reaches the peak fault current at a faster rate than the alternating current (AC) system, a solution that protects the system components is necessary to maintain system integrity. It is required by the low-voltage ride-through (LVRT) that the DERs maintain their interconnections with the LVDC system and support fault recovery. In this study, a method is proposed to allow the application of the superconducting fault current limiter (SFCL) to reduce the fault current and enhance the LVRT capability. However, when the DER maintain a connection to support fault recovery, the conventional resistive-type SFCL must withstand the burden of high-temperature superconducting (HTSC) operation during fault state dependence on LVRT. Therefore, this study proposes a trigger-type SFCL to reduce the burden of the HTSC element and enhance the LVRT capability. The voltage sag related to the LVRT was improved owing to the SFCL. The proposed solution was confirmed using PSCAD/EMTDC, which is a commercial software.

In the power distribution system of South Korea, the sectionalizer minimizes and recovers the system from failures, and prohibits independent operation of distributed energy resources. Therefore, it is difficult to expect reliability improvements owing to a photovoltaic system in the power distribution system. Herein, we propose a reliability-centered maintenance-scheduling method based on an economic analysis of photovoltaic integration facilities to ensure the economic feasibility of the photovoltaic system operator and the reliability of the power distribution system. Photovoltaic integration facilities are divided into two categories based on their spread of the failure, and the maintenance scheduling is evaluated according to the maximum and minimum losses according to the time of failure. Facilities that cause power outages to photovoltaics and to loads have relatively little effect on the time of failure. This is because the effects of the power outages in the loads predominate. In contrast, facilities that only cause photovoltaic outages are affected by the generation time. In particular, insulated gate bipolar transistors that operate only during photovoltaic generation have been found to be more economical for corrective replacement than preventive replacement.

Background: Our previous study demonstrated that tumor budding (TB) status was associated with inferior overall survival in cervical cancer. The purpose of this study is to evaluate whether radiomic features can predict TB status in cervical cancer patients. Methods: Seventy-four patients with cervical cancer who underwent preoperative MRI and radical hysterectomy from 2011 to 2015 at our institution were enrolled. The patients were randomly allocated to the training dataset (n = 48) and test dataset (n = 26). Tumors were segmented on axial gadolinium-enhanced T1- and T2-weighted images. A total of 2074 radiomic features were extracted. Four machine learning classifiers, including logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (NN), were used. The trained models were validated on the test dataset. Results: Twenty radiomic features were selected; all were features from filtered-images and 85% were texture-related features. The area under the curve values and accuracy of the models by LR, RF, SVM and NN were 0.742 and 0.769, 0.782 and 0.731, 0.849 and 0.885, and 0.891 and 0.731, respectively, in the test dataset. Conclusion: MRI-based radiomic features could predict TB status in patients with cervical cancer.

This paper proposes a method to improve the performance of a distribution system by optimizing volt–var function of a smart inverter to alleviate the voltage deviation problem due to distributed generation connection. In order to minimize voltage deviation and line losses which represent the performance of a distribution system, this paper proposes an algorithm that optimally sets the parameters of the volt–var function. In the process of optimizing the parameters of the volt–var function, the algorithm proposed in this paper considers minimizing the contribution of the reactive power in order not to affect the output of the distributed generation. In order to apply to the field, the distribution system in South Korea considering the configuration and operation regulation was selected as a test model for algorithm verification. As a result, the system performance was successfully improved by optimally setting the volt–var function of the smart inverter which is an effective way to solve the over-voltage problem caused by distributed generation connection. This paper verified the proposed method through OpenDSS, a quasi-static time-series simulation, for the test model considering the characteristics of the distribution system in South Korea.

This study proposes a method for optimally selecting the operating parameters of an energy storage system (ESS) for frequency regulation (FR) in an electric power system. First, the method allows the optimal objective function of the selected parameters to be set in a flexible manner according to the electric market environment. The objective functions are defined so that they could be used under a variety of electricity market conditions. Second, evaluation frequencies are created in order to simulate the overall lifespan of the FR-ESS. Third, calendar and cycle degradation models are applied to the battery degradation, and are incorporated into evaluations of the degradation progress during the entire FR-ESS lifespan to obtain more accurate results. A calendar life limit is set, and the limit is also considered in the objective function evaluations. Fourth, an optimal parameter calculation algorithm, which uses the branch-and-bound method, is proposed to calculate the optimal parameters. A case study analyzes the convergence of the proposed algorithm and the results of the algorithm under various conditions. The results confirmed that the proposed algorithm yields optimal parameters that are appropriate according to the objective function and lifespan conditions. We anticipate that the proposed FR-ESS algorithm will be beneficial in establishing optimal operating strategies.

The penetration of wind turbine generators onto the grid has grown worldwide at unprecedented rates in recent years. This raises the concern that the tripping of wind turbine generators could potentially cause system collapses. To alleviate these concerns, wind turbine generators need to maintain connection with the grid when a grid fault occurs. This has provoked many countries to adopt low-voltage ride-through (LVRT) for wind turbine generators. The LVRT is the capability of wind turbine generators to maintain connectivity during certain periods of voltage sag. The wind turbine generators should be connected to the grid to support fault recovery. Also, wind turbine generators must provide reactive power according to the grid voltage sag. Therefore, much research has been focused on enhancing LVRT capability. To enhance LVRT capability, this paper proposes the application of a superconducting fault current limiter (SFCL) in the system. The fault current was suppressed and the voltage sag was improved through the application of the SFCL. By improving the voltage sag, the wind turbine generator and the grid were able to maintain a connection. However, suppression of the fault current can cause a problem in the overcurrent relay (OCR) trip time delay. The trip time delay was solved by OCR resetting. Through a power system computer-aided design/electromagnetic transients including DC (PSCAD/EMTDC), the enhancement of LVRT capability and improvement of the trip delay was confirmed.

Offshore wind farms with good wind quality are exponentially increasing. To take advantage of this, the offshore wind farms and the grid are connected using the MVDC. In the event of a grid fault, the traditional wind generators and the MVDC are disconnected from the grid fault to protect the devices. However, the wind generators and the MVDC must support the recovery of the grid fault because the disconnection of large capacity wind farms will cause a grid collapse. To prevent this problem, the LVRT requires maintaining the connection between the wind generators and the grid to contribute to the recovery of the grid fault. In this situation, the DC voltage of the MVDC rises due to the unbalanced power of the input and output. Several methods have been proposed to suppress the DC voltage rise of the MVDC. Among various methods, the CR is an effective method to suppress the DC voltage rise of the wind generators and the MVDC. However, the conventional CR designs only consider rated voltage and system capacity. Therefore, this paper proposes the parameter estimation of the CR considering the important factors. The proposed method is verified by PSCAD/EMTDC.

Lithium (Li)-ion batteries have stimulated the societal transformation to clean energy systems. This carry-on electricity is revolutionizing how society communicates, functions, and evolves efficiently by enabling mobile electronics, zero-emission electric vehicles, and stationary energy storage. In preparation for the sustainable energy future, however, there are growing concerns about depleting critical elements used in the Li technology (e.g., lithium, cobalt, and nickel), especially for large-scale applications that will accelerate the rate of elemental consumption. Various non-Li-based rechargeable batteries composed of earth-abundant elements, such as sodium, potassium, magnesium, and calcium, have been proposed and explored as alternative systems to promote sustainable development of energy storage. In this perspective, we discuss challenges in Li-ion batteries in the sustainability aspect and provide our opinions on the potential applications of non-Li-based batteries. We also highlight the current status, important progress, and remaining challenges of the Li-alternative technologies.

This study investigated the metabolic parameters of primary tumors and regional lymph nodes, as measured by pre-treatment F-18 fluorodeoxyglucose positron emission tomography/computed tomography (F-18 FDG PET/CT) to compare the prognostic value for the prediction of tumor recurrence. This study also identified the most powerful parameter in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy.Fifty-six patients who were diagnosed with cervical cancer with pelvic and/or paraaortic lymph node metastasis were enrolled in this study. Metabolic parameters including the maximum standardized uptake value (SUVmax), the metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumors and lymph nodes were measured by pre-treatment F-18 FDG PET/CT. Univariate and multivariate analyses for disease-free survival (DFS) were performed using the clinical and metabolic parameters.The metabolic parameters of the primary tumors were not associated with DFS. However, DFS was significantly longer in patients with low values of nodal metabolic parameters than in those with high values of nodal metabolic parameters. A univariate analysis revealed that nodal metabolic parameters (SUVmax, MTV and TLG), paraaortic lymph node metastasis, and post-treatment response correlated significantly with DFS. Among these parameters, nodal SUVmax (hazard ratio [HR], 4.158; 95% confidence interval [CI], 1.1-22.7; p = 0.041) and post-treatment response (HR, 7.162; 95% CI, 1.5-11.3; p = 0.007) were found to be determinants of DFS according to a multivariate analysis. Only nodal SUVmax was an independent pre-treatment prognostic factor for DFS, and the optimal cutoff for nodal SUVmax to predict progression was 4.7.Nodal SUVmax according to pre-treatment F-18 FDG PET/CT may be a prognostic biomarker for the prediction of disease recurrence in patients with locally advanced cervical cancer.