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Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease, which affects the joints and causes significant pain, impairing patient's quality of life. Strychni semen showed promising results to treat RA. However, there are increasing safety concerns in using strychni semen due to its severe toxicity. Aim and Objective: The purpose of this review is to provide insight into using Strychni semen as an alternative medicine to treat RA, as well as to offer a method for the safe application of Strychni semen through processing and compatibility studies. Methods: Publications were retrieved and surveyed from CNKI and PubMed relevant to Strychni semen for a literature review. Results: This article summarized the mechanism of function of strychni semen in treating RA with its anti-inflammatory, analgesic, and immunomodulatory effect. Commonly used methods to attenuate the toxicity of Strychni semen were also discussed in this article. Conclusion: Strychni semen has a good therapeutic effect on RA, mainly by the modulation of immunity with anti-inflammatory and analgesic effects. Also, the reported toxicity of strychni semen can be effectively reduced by processing and compatibility methods. Hence, as an alternative medicine for RA treatment, strychni semen has a broad prospect.

In this paper, an analytical two-machine equivalent method of DFIG-based wind power plants (WPPs) is proposed. Through simulations of a 49.5 MW WPP, the root causes of errors of the single-machine equivalent are identified. Taking a four-machine WPP as an example, analytical expressions of fault-ride through (FRT) behaviors of the WPP model and its single-machine equivalent model are derived, and root causes of errors are revealed in theory including: inconsistent transient power behaviors in the initial FRT stage; unequal quasi-steady-state active power during a fault; and mismatching initial points and ramp rates of active power after the fault clearance. To eliminate equivalent errors, an improved equivalent method is proposed with two steps: 1) Group wind turbines into two clusters by the crowbar actions and represent them as two equivalent machines; 2) Correct their controller reference settings based on the derived FRT behavior expressions of the detailed WPP. Simulation results verify that the proposed method can accurately reproduce FRT behaviors of the WPP under various wind scenarios and voltage dips.

In the transmission line grounding system design, spatial and temporal/seasonal variations of tower footing impedance should be considered. However, in real life, only spatial variations are taken into account at the design stage, seasonal variations have not been included. In this article, seasonal variations of tower footing impedance of several types of transmission line grounding systems at Manitoba Hydro, Canada, are investigated through field measurements for a whole year. The industrial practice uses simulated tower footing impedances by the software current distribution, electromagnetic interference, grounding and soil structure analysis (CDEGS) in the design of transmission line grounding systems, however, the accuracy of simulated tower footing impedances as a critical design parameter has never been evaluated. In this article, this industrial practice is evaluated by comparing simulated and measured tower footing impedances for various transmission line grounding systems. The importance to use proper tower footing impedance for lightning conditions is also demonstrated.

Machine learning has paved its way into induction motors fault diagnosis area, where supervised learning and deep learning have been employed. However, both learning methods require a large amount of labeled data to train the model, which pose significant challenges in real life applications. To overcome this issue, in this paper, the graph-based semi-supervised learning (GSSL) is adopted to develop a fault diagnosis method for direct online induction motors due to GSSL’s superior feature that only a small amount of labeled data is needed in training datasets. To evaluate its suitability, the greedy-gradient max cut (GGMC) algorithm in the GSSL family is chosen in this study, and an effective fault diagnosis approach is developed using experimental stator currents recorded in the lab for two induction motors. The developed approach can conduct binary and multiclass classifications for faults on direct online induction motors. As a critical step, curve fitting equations are developed to calculate features for untested motor loadings by using experimental data for tested motor loadings, which enables the proposed approach to remain effective under all potential motor loading conditions.

Background The patterns and risk factors of intentional injuries compared to unintentional injuries among Chinese children and adolescents have not been examined in depth. This work comprehensively describes patterns of intentional injuries in China, for which little information has been previously published. Methods All cases involving individuals 0-17 years old registered at emergency rooms and outpatient clinics were examined using data submitted to the National Injury Surveillance System from 2006 through 2017. A logistic regression model was performed to explore the risk factors related to intentional injuries compared to unintentional injuries. Results A total of 81,459 (95.1%) unintentional injuries, 4,218 (4.9%) intentional injuries (4,013 violent attacks and 205 self-mutilation/suicide) cases were identified. Blunt injuries accounted for 59.4% of violent attacks, while cuts and poisoning accounted for 37.1% and 23.4% of injuries involving self-mutilation/suicide, respectively. For unintentional injuries, falls (50.4%) ranked first. Additional risk factors for intentional injuries included being male (odds ratio [OR] 1.6), coming from rural areas (OR 1.9), being staff or workers (OR 2.2), and being a student (OR 1.8). As the age of the patients increased, so did the risk of intentional injuries (OR 5.0 in the 15-17 age group). Intentional injuries were more likely to occur at 00:00-03:00 am (OR 2.0). Conclusions Intentional injuries affected more males, rural and older children, school students, and staff or workers. The mechanisms and occurrence times differed according to age group. Preventive measures should be taken to reduce the dropout of rural students, strengthen the school's violence prevention plan, and reduce self-harm.

This paper proposes a LoRa-based wireless communication system for data transfer in microgrids. The proposed system allows connection of multiple sensors to the LoRa transceivers, and enables data collection from various units within a microgrid. The proposed system focuses on communications at the secondary communication level of the microgrid between local controllers of each distributed generation (DG) unit and the microgrid central controller due to the possibility of applying low-bandwidth communication systems at this level. In a proof of concept test bed setup, the data collected by the sensors are sent to the LoRa gateway, which serves as the central monitoring system from which control messages are sent to various microgrid components through their local controllers such as DG units, storage systems and load. In this work, to improve communication security, a private server has been developed using Node-Red instead of cloud servers that are currently used in most Internet-of-Things (IoT) monitoring systems. A range test of the proposed system is carried out to observe the rate of data delivery. It demonstrated over 90% data delivery at 4 km. Finally, a test bed experiment is conducted to validate key features of the proposed system by achieving one-directional data transfer in a grid monitoring system.

The coordinated operation of multiple microgrids (MGs) enables high penetration of locally available distributed energy resources. It enhances the reliability and resiliency of the power network and reduces the cost of energy. Although networked MGs have attracted significant research interests, validation of various studies is difficult because there is no benchmark test system available for such systems. A benchmark test system can be used to validate static and dynamic studies related to the networking of multiple MGs, such as optimal power flow, energy management, control, stability, and protection. To fill in this research gap, a benchmark test system for networked MGs is proposed in this article, where four independent MGs are interconnected and coordinated. Required data, such as line parameters, load data, and power generating sources, have been prepared for each MG considered in the system. To provide a general test platform, typical datasets are made as close to practical MGs as possible. Parameters used to evaluate reliability indices and resiliency measures of the system are given for the entire test system. Future potential studies, which can be tested on the proposed benchmark test system, are discussed.

In this article, an effective machine learning-based fault diagnosis method is developed for induction motors driven by variable frequency drives (VFDs). Two identical 0.25 HP induction motors under healthy, single-, and multifault conditions were tested in the lab with different VFD output frequencies and motor loadings. The stator current and the vibration signals of the motors were recorded simultaneously under steady-state for each test, and both signals are evaluated for their suitability for fault diagnosis. The signal processing technique, discrete wavelet transform, is chosen in this article to extract features for machine learning. Four families of machine learning algorithms in the MATLAB Classification Learner Toolbox, decision trees, support vector machines, k -nearest neighbors, and ensemble, with 20 classifiers are evaluated for their classification accuracy when used for fault diagnosis of induction motors fed by VFDs. To allow fault diagnosis for untested motor operating conditions, the feature calculation formulas are developed through surface fitting using experimental data of a range of tested frequencies and loadings of the motor to provide training data for untested conditions.

Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) connected offshore wind farms could suffer serious challenges: DC overvoltage during a fault and post-fault DC voltage restoration. In this paper, to mitigate DC overvoltage, a two-stage voltage drop control scheme is proposed for offshore MMC to realize a fast active power reduction, by coordinating with a voltage-dependent active current control of individual wind turbines. The voltage drop depths are designed by considering the offshore de-loading efficiency and the stability of WTs. To improve the post-fault DC voltage recovery performance, an adaptive voltage rise control scheme is proposed to coordinate the recovery of onshore and offshore active power. The recovery trajectory of DC voltage is formulated and further employed to design the onshore active power ramp rate for uninterrupted operation of WTs. Case studies have been conducted on two test systems to validate the proposed method.

The introduction of wireless charging systems for electric vehicles (EVs) is a revolutionary step in the field of electrified transportation. Misalignments between transmitting and receiving power pads significantly affect the overall power transfer efficiency of an EV wireless charging system. In this article, a new power pad named “double D circular” (DDC) is designed. To evaluate the proposed DDC power pad design, its performance is compared with that of the existing circular and double D (DD) power pads by analyzing the power transfer efficiency in the wireless charging system considering different vertical and horizontal misalignments between the transmitting and receiving power pads. We use physical dimensions published in the Society of Automotive Engineers (SAE) J2954 recommended practice for existing circular and DD power pads in the simulation software, ANSYS Maxwell 3D. The influence of ferrite plates on the performance of power pads is also evaluated in the article.

Formulated products represent a particular class of complex chemical products, and their design is typically based on experience and extensive experimentation. Though still at an early stage, and despite that their potential is not fully accessed and not fully utilized by industry, Computer-aided design (CAD) methods and tools offer many possibilities in the design of formulated products. The CAD methodology based on computerized models enables the formulation chemists to speed up the design process, without completely replacing experiments. In this work, we summarize previous studies in the field and present important elements of the CAD framework, emphasizing estimation methods for key target properties, link to specifications and finally, some case studies will illustrate how the CAD framework can be used in practice for formulated products.

In this paper, a new non-isolated bidirectional dc-dc converter with zero voltage switching (ZVS) and zero current switching (ZCS) capability is proposed. The proposed converter not only increases the voltage gain, but also eliminates current ripples at the high current port by using a three-winding coupled inductor. Therefore, the proposed topology is suitable for transferring the photovoltaic (PV) generated power to the load, where the maximum power from PV can be extracted. In the proposed converter, the ZVS operation of two main switches is obtained by using auxiliary switches, while the auxiliary switch is tuned OFF under the ZCS state. The ZVS/ZCS operation of switches can be achieved by tuning parameters of the coupled inductor and the required overlapping time for the trigger pulses of main and auxiliary switches at the whole range of duty cycle (0

This paper proposes an expandable high voltage gain non-isolated DC-DC converter. In the proposed converter, different kinds of voltage multiplier cells, such as diode-capacitor (D-C) and diode-capacitor-inductor (D-C-L) cells, are utilized to increase the output voltage gain. The proposed converter uses two switches with a simple switching pattern. Comparing to conventional diode-capacitor-based converters of the same type in the literatures, the proposed DC-DC step-up converter has much better features regarding the voltage conversion ratio and voltages stress on semiconductor elements. A 250 W prototype of the proposed converter was implemented in laboratory, and experimental results are presented to verify the theoretical analysis.

Wind power generation is a dominant form of renewable energy sources with significant technical progress over the past decades. One of the major challenges in wind power generation is the intermittent nature of wind speed. In this paper, wind speed prediction techniques are investigated using wind speed measurement data in Saskatchewan, Canada. Despite excellent wind power potential in Saskatchewan, currently, only 6.5 % of total electricity demand is supplied by wind power in this province. In this paper, three hybrid Neural Network methods (Wavelet Neural Network (WNN) trained by Improved Clonal Selection Algorithm (ICSA); WNN trained by Particle Swarm Optimization (PSO); and Extreme Learning Machine (ELM)-based Neural Network) are implemented and compared for wind speed forecasting using actual recorded wind speed data of Saskatchewan, which paves the way for economical operation, planning, and optimization of the current and future wind farms in Saskatchewan.

Wind speed prediction plays an essential role in planning and operation of wind power systems. An accurate wind speed prediction can reduce costs and enhance the proper use of resources. Wind speed series have high nonlinearity and volatility. In this paper, data-driven models using machine learning (ML) algorithms have been developed to predict a very short-term wind speed. Historical wind speeds lagging up to 20 minutes with 1 minute time interval are used to predict the current and future (up to 5 minutes with L-minnte interval) wind speed. A performance comparative analysis of four ML algorithms including Multiple-Layer Perception Regressor (MLPR), Random Forest Regressor (RFR), K-nearest Neighbors Regressor (KNNR), and Decision Tree Regressor (DTR), is conducted, and their accuracy is evaluated by their R2 values, mean absolute error (MAE), standard deviation (SD) of MAE, mean absolute percentage error (MAPE), SD of MAPE and root mean square error (RMSE). It is found that MLPR gives the best prediction accuracy of 95.3%.

Purpose: To investigate the survival outcomes, prognostic factors and treatment modalities of stage I-III cervical esophageal carcinoma (CEC) patients using data from the Surveillance, Epidemiology, and End Results (SEER) database from the period 2004–2016.Methods: Patients with a histopathologic diagnosis of CEC were included. The primary endpoint was overall survival (OS). Univariate and multivariate analyses of OS were performed using Cox proportional hazards models, and OS was compared using the Kaplan-Meier method and log-rank test.Results: A total of 347 patients in the SEER database were enrolled. The median OS was 14.0 months, with a 5-year OS rate of 20.9%. The parameters that were found to significantly correlate with OS in the multivariate analysis were age at diagnosis [P < 0.001, hazard ratio (HR) = 1.832], sex [P < 0.001, HR= 1.867], histology [P = 0.001, HR = 0.366], surgery at the primary site [P = 0.021, HR = 0.553], radiotherapy (RT, P = 0.017, HR = 0.637) and chemotherapy (CT, P < 0.001, HR = 0.444). Comparison among the three treatment modalities demonstrated that a triple therapy regimen consisting of surgery, RT and CT was associated with a longer survival time than the other two treatment modalities before and after propensity score matching (PSM). However, triple therapy showed no significant survival benefit over double therapy (P = 0.496 before PSM and P = 0.184 after PSM).Conclusions: The survival of patients with CEC remains poor. Surgery, RT and CT were all strongly correlated with OS. We recommend a triple therapy regimen for select CEC patients based on the findings of the current study, although this recommendation should be further confirmed by prospective studies with large sample sizes.

Due to increasing penetration of wind power plants (WPP), power grids are facing significant power quality challenges at the point of interconnection (POI). To achieve stable and robust power system operation, reactive power plays a vital role. Reactive power needed to compensate voltage fluctuations at the POI of the WPP varies with a short-circuit ratio (SCR). The reactive power capability of a WPP is also limited and largely depends on various operating conditions. In a physical system, it is difficult to find the correlation among critical parameters for voltage control. In this paper, a data-driven voltage control approach is proposed for a grid-connected WPP. Two regression models are developed through surface fitting using MATLAB curve fitting toolbox: One model based on simulation data is to determine the required reactive power for grid voltage compensation; another model based on field measurement data is to determine the reactive power characteristics of the WPP. The reactive power compensation device is capacitors in this study. Two controllers, a central WPP controller, and a capacitor controller are designed, and their effectiveness is validated through several case and sensitivity studies.

In this paper, a practical machine learning-based fault diagnosis method is proposed for induction motors using experimental data. Various single- and multi-electrical and/or mechanical faults are applied to two identical induction motors in lab experiments. Stator currents and vibration signals of the motors are measured simultaneously during experiments and are used in developing the fault diagnosis method. Two signal processing techniques, matching pursuit, and discrete wavelet transform, are chosen for feature extraction. Three classification algorithms, support vector machine (SVM), K-nearest neighbors (KNN), and ensemble, with 17 different classifiers offered in MATLAB Classification Learner toolbox are used in the study to evaluate the performance and suitability of different classifiers for induction motor fault diagnosis. It is found that five classifiers (fine Gaussian SVM, fine KNN, weighted KNN, bagged trees, and subspace KNN) can provide near 100% classification accuracy for all faults applied to each motor, but the remaining 12 classifiers do not perform well. A novel curve fitting technique is developed to calculate features for the motors that stator currents or vibration signals under certain loadings are not tested for a particular fault. The proposed fault diagnosis method can accurately detect single- or multi-electrical and mechanical faults in induction motors.

This paper investigates the issues of day-ahead and real-time cooperative energy management for multienergy systems formed by many energy bodies. To address these issues, we propose an event-triggered-based distributed algorithm with some desirable features, namely, distributed execution, asynchronous communication, and independent calculation. First, the energy body, seen as both energy supplier and customer, is introduced for system model development. On this basis, energy bodies cooperate with each other to achieve the objective of maximizing the day-ahead social welfare and smoothing out the real-time loads variations as well as renewable resource fluctuations with the consideration of different timescale characteristics between electricity and heat power. To this end, the day-ahead and real-time energy management models are established and formulated as a class of distributed coupled optimization problem by felicitously converting some system coordinates. Such problems can be effectively solved by implementing the proposed algorithm. With the effort, each energy body can determine its owing optimal operations through only local communication and computation, resulting in enhanced system reliability, scalability, and privacy. Meanwhile, the designed communication strategy is event-triggered, which can dramatically reduce the communication among energy bodies. Simulations are provided to illustrate the effectiveness of the proposed models and algorithm.

In this paper, the measurement-based characteristic curves, reactive power–voltage ( $Q$ – $V$ ) curve and reactive power–active power–voltage ( $Q$ – $P$ – $V$ ) curve, are developed for voltage stability and control at the point of interconnection (POI) between a wind power plant (WPP) and a utility grid. The procedure to obtain an effective $Q$ – $V$ curve using the supervisory control and data acquisition (SCADA) measurement data is proposed. The SCADA data used in this paper were recorded in 2016 at the POI of a 27-MW WPP currently in operation in Newfoundland and Labrador (NL), Canada. Each $Q$ – $V$ curve is created for a particular month using the SCADA data corresponding to a very narrowly defined active power range, so the active power is considered constant as the conventional $Q$ – $V$ curve concept. The MATLAB curve fitting toolbox is used to realize the most fit mathematical equations for $Q$ – $V$ and $Q$ – $P$ – $V$ curves. The developed $Q$ – $V$ and $Q$ – $P$ – $V$ curves can be used for the data-driven voltage control for WPPs; and the $Q$ – $V$ curves can also be used for the voltage stability evaluation.

Objectives The aim of the present study was to investigate changes in the psychological state of medical personnel in the Department of Radiotherapy during the COVID-19 epidemic. Methods Psychological state was evaluated using the Pittsburgh Sleep Quality Index (PSQI), Self-Rating Depression Scale (SDS), and Self-Rating Anxiety Scale (SAS). All three questionnaires were first completed by medical personnel on 17–18 February 2020 and were repeated every 3 months thereafter until 17–18 August. The number and intentions of patients receiving radiotherapy (RT) in our department were also collected. Results Twenty medical personnel participated in the present study. The global PSQI score recorded in August was significantly lower than that recorded in February (P = 0.045). Among the seven components of the PSQI, sleep quality (P = 0.048) and daytime dysfunction (P = 0.006) in August were significantly improved compared with February, whereas SDS and SAS did not significantly differ among the three different time points. The proportion of patients who received palliative radiotherapy was significantly higher on 18 May than on 17 February (P = 0.005). Conclusions Medical personnel in the Department of Radiotherapy experienced a significantly elevated incidence of sleeping problems during the early COVID-19 outbreak period. Multiple combinations of protective measures to avoid infection could improve sleep quality and ensure the safe delivery of RT to cancer patients.

In transmission line grounding system design, spatial and temporal/seasonal variations of tower footing impedance should be considered. However, in real life, only spatial variations are taken into account at the design stage, seasonal variations have not been included. In this paper, seasonal variations of tower footing impedance of several types of transmission line grounding systems at Manitoba Hydro, Canada are investigated through field measurements for a whole year. The industrial practice uses simulated tower footing impedances by the software CDEGS in the design of transmission line grounding systems, however, the accuracy of simulated tower footing impedances as a critical design parameter has never been evaluated. In this paper, this industrial practice is evaluated by comparing simulated and measured tower footing impedances for various transmission line grounding systems. The importance to use proper tower footing impedance for lightening conditions is also demonstrated.

To meet grid code requirements, handle steady-state and transient uncertainties, and maintain the system stability and power quality, a wind power plant (WPP) must have adequate reactive power reserve. To estimate reactive power reserve of WPPs, an accurate assessment of the plant-level capacity is crucial. In practice, many variables affect the reactive power capability of a WPP, a model without considering such variables cannot represent the system characteristics adequately. In this paper, an analytical approach to determine the reactive power capability at individual doubly-fed induction generators (DFIGs) and the plant-level of WPPs is proposed. For ease of use, the proposed approach is developed based on well-known standard parameters. The reactive power capability model of a DFIG is validated by comparing with two existing methods, and the reactive power capability model at the plant-level is compared with Supervisory Control and Data Acquisition (SCADA) field measurement data of two WPPs currently operating in Newfoundland, Canada.

BackgroundFlow diverter (FD) is widely used in the treatment of intracranial aneurysms. However, thromboembolic events (TEs) continue to be the major complications during the periprocedural phase. To evaluate the safety and efficacy of the prophylactic use of tirofiban, combined with the conventional dual antiplatelet therapy (DAT), as a new antiplatelet protocol in patients with intracranial aneurysms treated with FDs.MethodsAt least 3–5 days before the procedure, daily DAT were administrated to the patients. Tirofiban was administered as an intravenous bolus (5 µg/kg) over a 3 min period during or immediately after FD deployment, followed by a 0.05 µg/kg/min maintenance infusion for 24–48 hours. Periprocedural TEs and hemorrhagic events (HEs) were recorded.ResultsA total of 331 patients were included, including 229 (69.2%) who received tirofiban administration (tirofiban group) and 102 (30.8%) who received only DAT (non-tirofiban group). Periprocedural TEs occurred in 12 (3.6%) patients, including eight (7.8%) in the non-tirofiban group and four (1.7%) in the tirofiban group. In multivariate analysis, patients receiving tirofiban administration had significantly lower TEs as compared with those who received only DAT (P=0.004). Balloon angioplasty and longer procedure time (>137 min) were also risk factors for TEs. Also, no increase was observed in the rate of HEs related to tirofiban administration.ConclusionsThe current study suggested that prophylactic administration of tirofiban combined with conventional oral DAT seems safe and efficient for preventing TEs during FD treatment of unruptured intracranial aneurysms. Balloon angioplasty and prolonged procedure are associated with a high risk of TEs.

In this paper, a graph-based semi-supervised learning (GSSL) method is proposed for fault diagnosis of direct online induction motors using stator current and vibration signals. A 0.25 HP induction motor under healthy, single- and multi-fault conditions is tested in the lab. Three-phase stator currents and three-dimensional vibration signals of the motor are recorded simultaneously under steady-state operation in each test. Features for machine learning are extracted from the raw experimental stator current and vibration data using the discrete wavelet transform (DWT). Three GSSL algorithms, local and global consistency (LGC), Gaussian field and harmonic function (GFHF), and greedy-gradient max cut (GGMC), are used in the paper. It is found that both stator current and vibration signals perform well for one individual fault diagnosis using GSSL algorithms, but for classification of a combination of five different faults, the stator current outperforms the vibration signal significantly. Among the three GSSL algorithms, GGMC shows better classification results over LGC and GFHF for both stator current and vibration signals.

Supervised learning has been commonly used for induction motor fault diagnosis, and requires large amount of labeled samples. However, labeling recorded data is expensive and challenging, while unlabeled samples are available abundantly and contain significant information about motor conditions. In this paper, a graph-based semi-supervised learning (GSSL) approach using both labeled and unlabeled data is proposed. Experimental data for two 0.25 HP induction motors under healthy and faulty conditions are used. Discrete Wavelet Transform (DWT) is employed to extract features from recorded stator current signals. Three GSSL algorithms (local and global consistency (LGC), Gaussian field and harmonic function (GFHF), and greedy-gradient max cut (GGMC)) are evaluated in this study, and GGMC shows superior performance over other two. They are also compared with a supervised learning algorithm, support vector machine (SVM). As induction motors often operate under variable loadings, curve fitting equations are developed based on experimental data to generate training data for untested motor loadings.

As a new emerging approach, Virtual Synchronous Generator (VSG) control for interfacing inverters in renewable energy-based microgrids has attracted significant research interest. However, VSG control cannot ensure proportional reactive power sharing among distributed generation (DG) units when unequal transmission line impedances are involved, although active power sharing remains proportional among DGs. In this paper, an adaptive virtual impedance approach is proposed, which can be incorporated in a VSG control to alleviate impedance difference at the inverter output and improve reactive power sharing among DGs. It is applicable to any VSG control, either conventional or modified types. The proposed approach is implemented on a modified VSG control scheme, known as Fuzzy secondary controller (FSC)-based VSG control, in this paper. Its performance is demonstrated through several case and sensitivity studies using MATLAB/Simulink simulation; and is further validated by comparing with an existing method.

Locally resonant phononic crystals (LRPCs) have the capacity to adjust elastic waves with the structure sizes much smaller than the incident wavelengths, the unique property is called low-frequency bandgap, but it is not easily applied in practical engineering because of narrow bandgap width. Multilayered LRPCs are helpful in generating several bandgaps, in the meanwhile the designs of multilayered LRPCs proposed in previous study result in the larger filling fraction, whereas the bandwidth of LRPCs increases monotonically with filling fraction, thus the pure contribution of concentric ring configuration to the bandwidth extending is less involved. Keeping the filling fraction constant, this paper carefully designs the microstructure of concentric ring locally resonant phononic crystals, and investigates the effects of structure configuration on the bandgap property. To this end, an updated improved plane wave expansion (UIPWE) method is developed to calculate the band structure, and finite element method (FEM) is used to obtain transmission spectra and vibration mode. The results demonstrate that UIPWE method is valid and is able to give precise outcomes, which is verified by FEM. In addition, the concentric ring configuration equivalently produces dual-oscillator system, relative movements between the oscillators generate coupling effect, thus, the bandgaps can be extended by configurating rightly the microstructure of single cell. Further studies about different models indicate that the combination of smaller inner scatterers and larger inner coating layers are beneficial to wider bandgap. These conclusions presented herein provide insights in the design of three-component PCs in multi-frequency vibration control field.

Accurate prediction of the solubilities of air (as well as nitrogen: N2; oxygen: O2; and argon) in aqueous electrolyte solutions is very important for geological studies and in the chemical industry. However, very few electrolyte equations of state have been successfully used for the calculations of these gas solubilities. This work presents a thermodynamic modeling study on the solubilities of N2, O2, argon, and gas mixtures in pure water and aqueous solutions of several inorganic salts with the electrolyte Cubic Plus Association equation of state (e-CPA). The binary interaction parameters between the cation/anion and gas are obtained by fitting the experimental data of gas solubilities in single-salt solutions. The results show that the e-CPA can satisfactorily correlate the gas solubilities for most systems. For example, the e-CPA gives deviations of 4% for the O2 solubilities in aqueous NaCl solution. The e-CPA is then applied to predict the solubilities of gas mixtures and multisalt systems (gas mixture in pure water and gas mixture in aqueous multisalt solutions), and satisfactory performance is achieved. For example, the e-CPA gives deviations of 4% for the air solubilities in aqueous NaCl solution.

Selection of solvents and calculation of their composition in the final product, is a critical step in coating formulation design. This task can be facilitated by computer-aided methods if the necessary thermodynamic and group contribution property models along with chemical property data are available for the ingredients under consideration. However, these computer-aided procedures must be supplemented and verified by using experimental procedures. Hence, the computer-aided stage can be used to speed-up the solvent selection and design process, efficiently utilize experimental resources and serve as a guide for the formulation chemist. We present in this work an adaption of the generic Computer-Aided Product Design (CAPD) methodology for organic coating formulations. Herein, the paper discusses a model-based framework developed for the design of solvents for such coatings. The applicability of this framework is tested via a case study wherein, solvents for the dispersion of two specific organic pigments are required to be determined, when acrylic polymers are used as dispersing aids.

A methodology for product design of organic coatings is developed to help a formulator screen, modify, or design a paint formulation according to the desired functionality. The computer-aided product design (CAPD) method aims to improve and expand the current state of computer-aided coating formulation through a combination of databases and models capable of estimating various physicochemical properties. Additional paint-specific component interactions are considered, and new properties and estimation methods are included that better suit modern environmental, health, and safety-related considerations in the product design of paints. This work aims to provide a clear description of all property models, estimation methods, tools, and software used throughout the systematic framework. The solvent selection methodology is tested for several commercial paint formulations, using the original solvent mixtures to verify the results, while providing formulation alternatives to guide further experimental testing. For investigated case studies, the methodology provides both viable formulations and solvent selection alternatives. The suggested options can be used to retain key functionalities while lowering cost, substituting unwanted ingredients, or improving factors including health and safety, as indicated by lethal concentration, flash points, and biodegradability.

An improved single-machine equivalent method is proposed for wind power plants (WPPs) by calibrating the postfault power recovery behaviors. A real WPP is simulated with extensive wind scenarios to evaluate the traditional single-machine equivalent model, and it is found that its equivalent error is mainly resulted from the postfault recovery process. The simulation analysis further indicates that a wind turbine operating at different wind speeds restores to its prefault active power at a certain ramp rate after fault clearance, with different starting power and different recovery time. Taking a two-machine WPP as an example, the analytical expression for active power of the WPP is derived for the fault ride through process, and thus the equivalent error of traditional single-machine model is found to be resulted from two aspects. One is the mismatch of the starting power of the postfault recovery process, and another is the mismatch of the power recovery rates between the complete WPP and its equivalent model. The analytical expressions are extended to multimachine WPP to calibrate the starting power and the ramp rates of the postfault recovery process. Simulation results show that the proposed method has good performance for various voltage drops, wind scenarios, and grid characteristics.

With more renewable energy based distributed generation (DG) units connected to utility power grids, deterioration of power quality at the point of common coupling (PCC) becomes a major concern. There are two types of harmonics associated with DG units, and together they may cause excessive harmonic distortion at the PCC. The first type of harmonics is generated by power electronic devices in DG units such as photovoltaic systems, which contains high-frequency harmonic components at multiples of the carrier frequency of the DG interfacing inverter. Such harmonics are first reviewed in this paper, and the potential operational effect at the system level due to LCL or LC filters installed at the inverter output to mitigate such harmonics are discussed. The second type of harmonics is generated by other nonlinear local, PCC, and utility loads in the system, which are common type of harmonics at multiples of the power grid frequency, 50/60 Hz. Harmonic mitigation for such harmonics achieved through advanced control of the DG interfacing inverter operated as a power quality conditioner are reviewed and summarized. This systematic review can facilitate better understanding of harmonics associated with renewable energy based DG units and provide guidelines on advanced control schemes to realize ancillary harmonic compensation service through DG interfacing inverters.

With increasing penetration of wind power, it is recognized that manufacturer-specific models of wind turbines are not appropriate for power system studies, and the equivalent model of wind farms required by dynamic modeling poses a complicated technical challenge. In this paper, a practical equivalent method for wind farms with doubly fed induction generator (DFIG) wind turbines is proposed. Three critical steps are involved in this method. Step 1, modeling of a wind turbine, the model incorporating the complete fault ride through process is constructed and validated for an operational DFIG wind turbine. Step 2, clustering of wind turbines, a practical four-machine equivalent method is proposed. Initially, dynamic responses of active power of a DFIG wind turbine operating at various wind speeds are investigated to obtain three clusters of wind turbines, then four clusters are further determined by improving equivalency error at some typical wind speed scenarios. Step 3, an analytical method is proposed to determine the equivalent collector network parameters for each of the four equivalent machines. The proposed method is validated by comparing with the single-machine representation and the detailed wind farm model. Simulation results show that the proposed method has good performance for different fault durations, voltage dips, and changing wind scenarios.

Due to increasing penetration of wind power, post-fault power recovery behaviours of wind turbines could have significant effect on power system stability. To address this issue, a novel model representing the complete fault ride-through processes is proposed considering power recovery behaviours of doubly-fed induction generator wind turbines in this paper. The control strategies of active and reactive power during the fault ride-through are formulated, and controllers for grid- and rotor-side converters are designed. The proposed model is verified by simulation and field testing using wind turbines operating in wind farms.

In this paper, a fuzzy-secondary-controller-based virtual synchronous generator (VSG) control scheme for interfacing inverters of distributed generation units is proposed for voltage and frequency regulation in microgrids. The proposed control scheme shows significantly improved dynamic performance when compared with the conventional droop control and conventional VSG control through several case studies. Its effectiveness and robustness is further evaluated by sensitivity studies.

Background The effects and safety of transradial access for stenting of carotid artery stenosis in patients with bovine-type and type III aortic arch are currently unknown and are the purpose of this study. Methods Patients who were treated with stenting via transradial (n = 28) compared with transfemoral (n = 30) access of carotid artery stenosis complicated with bovine-type and type III aortic arch were enrolled. The clinical data were studied for comparison. Results The success rate of transradial access for carotid artery stenting was 100%. During the transradial access procedure, 3 patients experienced a temporary blood pressure drop and bradycardia that was resolved with atropine injection. No radial artery occlusion or severe cardiac or cerebral events occurred. The success rate of transfemoral access for carotid artery stenting was 90% with failure in 3 patients. No complications occurred during or after the stenting procedure. The irradiation duration was significantly (P = 0.001) smaller in transradial than in transfemoral access stenting (8.6 ± 0.87 vs. 11.4 ± 1.25 minutes). The arterial stenosis ranged 71.25%–96.50% (mean, 86.50% ± 10.15%) for transradial and 78%–97.75% (85.1% ± 10.42%) for transfemoral access groups before treatment, and the residual stenosis following treatment was significantly smaller in either the transradial (2.4%–17.75%; mean, 11.2% ± 6.03%) or transfemoral (4%–18.6%; mean, 12.4% ± 6.3%) groups compared with before treatment, respectively. Conclusions The transradial access for stenting of patients with type III aortic arch plus left carotid artery stenosis or in the bovine-type aortic arch combined with right carotid artery stenosis can decrease the stenting difficulty, risks of embolization, and radiation time and increase the success rate.

To resolve power crisis and reduce environmental effect of conventional power generation, a concentrated solar power (CSP) plant is a viable solution. This paper provides a comprehensive evaluation of four different CSP plant configurations, offers a comparison between CSP technologies and solar photovoltaic power plants, and proposes a general guideline for feasibility analysis and design of CSP plants. The detailed technical and financial specifications and analysis for setting up a parabolic trough-type CSP plant in Chittagong, Bangladesh, are provided as a case study. The simulation is carried out using the software known as “System Advisor Model.” This paper can serve as an important guideline for the future CSP plant design.

Due to varying and intermittent nature of wind resource, grid connected wind farms pose significant technical challenges to power grid on power quality and voltage stability. Wind farm Q-V characteristic curve at the point of interconnection (POI) can offer valuable information for voltage control actions and provide essential indication about voltage stability. Data driven analytics is a promising approach to determine characteristics of a large complex system, physical model of which is difficult to obtain. In this paper, the data driven analytics is used to determine Q-V curve of grid connected wind farms based on measurement data recorded at the POI. Different curve fitting models, such as Polynomial, Gaussian and Rational, are evaluated and best fit is determined based on different graphical and numerical evaluation metrics. A case study is conducted using field measurement data at two grid connected wind farms currently in operation in Newfoundland and Labrador, Canada. It is found that the Gaussian (degree 2) model describes the Q-V relationship most accurately for the two wind farms. The obtained functions and processed data can be used in the voltage controller design. The plotted QV curve can also be used to determine the reactive margin at the POI for voltage stability evaluation. As a generic method, the proposed approach can be employed to determine Q-V characteristic curve of any grid connected large wind farms.

Synchrophasor technology, also known as Wide-Area Monitoring System (WAMS) technology, utilizes Phasor Measurement Unit (PMU) to monitor real-time system data, which can provide unique insights into the operation of a power grid. In this paper, a rule-based data-driven analytics method for wide-area fault detection in a power system using synchrophasor data is proposed. As a data-driven approach, this method relies on rules created using PMU measurement data, and does not require knowledge of the power system's topology and model. It can detect fault location (bus and line) and fault type for a particular fault event. Three common types of short circuit faults in a power grid, single-line-to-ground (SLG), line-to-line (LL), and three phase faults, can be identified using the proposed method. Fault thresholds used in rules are determined based on theoretical values and recorded PMU data during fault events in Bonneville Power Administration (BPA)'s large power grid. The proposed method is validated by comparing with the recorded field data for fault events provided by BPA. It is found that it can effectively detect most faults with a great accuracy. It has been developed into a software program, and can be readily used by utility companies.

The electrical submersible pump (ESP) systems rely on downhole tools to monitor the operation of electrical submersible motors and pumps. The monitored parameters include temperature and vibration of the motor, and the pump intake pressure. Transmitting these parameters successfully and reliably through a downhole tool from downhole to surface under various ESP system operating conditions is critical for production optimization and equipment safety. In this paper, a new downhole tool design method using a phase-to-neutral communication scheme is proposed, which can overcome issues of current downhole tool products installed in the field. In this design, an effective signal communication path is formed by external impedance on the wellhead connecting one phase to the ground combined with a controlled thyristor at downhole connecting the neutral of an electrical submersible motor to the ground. Extensive analytical calculation and PSCAD simulation of the proposed method are demonstrated in the paper. Two case studies are conducted using a real ESP system under normal and single-line-to-ground fault conditions. A sensitivity study is performed to evaluate the influence of various factors on the signal strength for signal communication purpose. A lab testing further provides proof of concept. The proposed method in this paper will result in a reliable and effective downhole tool design for an ESP system operation.

Jia Yang, Hong’en Xu, Xiaodong Liang, Shiliang Lv, Baihua Lin, Yongshi Jia Department of Radiotherapy, Zhejiang Provincial People’s Hospital, Hangzhou, People’s Republic of China Abstract: We aimed to systematically evaluate the association between allergic conditions and the risk of Hodgkin’s lymphoma (HL) and non-HL (NHL). Systematic literature searches in PubMed and Embase were conducted up to October 2015 to identify eligible studies. Either a fixed-effects model or a random-effects model was adopted to estimate overall odds ratios (ORs) according to heterogeneity across studies. Subgroup and publication bias analyses were applied. A total of 24 case–control studies and 13 cohort studies (conducted from 1987 to 2015) were included in the analysis of the risk of NHL. History of any allergic condition was inversely associated with the risk of NHL in case–control studies (OR=0.83, 95% CI 0.76–0.91), while the reduction in the risk of NHL was not observed in cohort studies (OR=1.18, 95% CI 0.98–1.42). Significant association with the risk of NHL was found for asthma, hay fever, food allergy, allergic rhinitis, and hives. In the pooled analysis of the risk of HL, 12 studies (two were cohort studies) were included. The pooled OR was 0.96 (95% CI 0.84–1.09) for case–control studies and 1.46 (95% CI 0.63–3.38) for cohort studies. For specific allergic condition, we observed a reduced risk of HL in individuals with hay fever and food allergy. In conclusion, history of any allergic condition was not significantly associated with the risk of NHL or HL. Several specific allergic conditions, including asthma, hay fever, food allergy, and allergic rhinitis, might be associated with a reduced risk of NHL, while individuals with hay fever or food allergy may have a reduced risk of HL. Keywords: allergic condition, non-Hodgkin’s lymphoma, Hodgkin’s lymphoma, risk

The multigrounded shield wire transmission scheme is common within North American utilities. The ground potential rise (GPR) for this transmission scheme is important as it is to be used to develop specifications for systems and component protection. No hand calculation methods for GPR for high-voltage transmission line faults exist due to the complexity of the problem itself and vast amount of data input required. Such calculation depends only on computer simulation software, and very limited information has been reported in the literature. This paper aims to provide insights into GPR for high-voltage transmission line faults by demonstrating quantity values for the very first time through extensive computer simulation using the ATP-EMTP software. A practical design for a 160 km 230 kV single-circuit transmission line with two multigrounded shield wires is used as a case study in the paper. Different factors affecting GPR and fault current levels in the system are investigated through a sensitivity study. The underlying relationship between parameters is analyzed and summarized based on simulation results, which can be used to assist practicing engineers’ design work.

Background Colorectal cancer is the fourth most common cancer globally and neoadjuvant concurrent chemoradiotherapy (nCRT) and surgery are the standard treatments for locally advanced colorectal carcinoma. This study investigated the association between dynamic changes in absolute lymphocyte counts (ALCs) and disease-free survival (DFS) in rectal cancer patients receiving nCRT and identified factors associated with these changes. Methods We retrospectively examined 34 patients with locally advanced rectal cancer who received nCRT followed by surgery and adjuvant chemotherapy. The association between ALCs and DFS and that between ALCs and downstaging were analyzed and potential clinical- and treatment-related factors related to dynamic changes in ALCs were subsequently evaluated. The patient eligibility criteria were as follows: pathologically confirmed rectal adenocarcinoma, clinical stages II–III, ≥ 18 years of age, and so on. Pre-RTL was defined as ALCs obtained before the initiation of nCRT and pre-SL was defined as ALCs obtained before surgery. We measured pre-SL to pre-RTL ratio (pre-SLR), DFS, and ALCs. Results The median ALC declined significantly during nCRT. A lower pre-SLR was associated with poorer DFS with statistical significance in Kaplan–Meier (p = 0.007), univariate regression (hazard ratio [HR] = 6.287, 95% confidence interval [CI] 1.374–28.781, p = 0.018), and multivariable regression (HR = 7.347, 95% CI 1.595–33.850, p = 0.011) analyses. Neither patient characteristics nor treatment-related factors were related to downstaging. The pelvic bone marrow (PBM) volume receiving at least 30 Gy (V30) was significantly associated with pre-SLR in the univariate (HR = 5.760, 95% CI 1.317–25.187, p = 0.020) and multivariable (HR = 5.760, 95% CI 1.317–25.187, p = 0.020) regression analyses. Limitations Our study had several limitations. The sample size was small and the study was performed in a selected population, which may limit the generalization of the findings. Conclusions Radiotherapy had a profound impact on the change in ALCs. A lower pre-SLR was significantly associated with poorer DFS in rectal cancer patients receiving nCRT. The V30 of PBM was a predictor of pre-SLR.

In this paper, an effective machine learning based fault diagnosis method is developed for induction motors fed by variable frequency drives (VFDs). Two identical 0.25 HP induction motors under healthy, single-and multi-fault conditions were tested in the lab with different VFD output frequencies and motor loadings. The stator current and the vibration signals of the motors were recorded simultaneously under steady-state for each test, and both signals are evaluated for their suitability for fault diagnosis. The signal processing technique, Discrete Wavelet Transform (DWT), is chosen in this paper to extract features for machine learning. Four families of machine learning algorithms in the MATLAB Classification Learner Toolbox, decision trees, support vector machines (SVM), k-nearest neighbors (KNN), and ensemble, with twenty classifiers are evaluated for their classification accuracy when used for fault diagnosis of induction motors fed by VFDs. To allow fault diagnosis for untested motor operating conditions, the feature calculation formulas are developed through surface fitting using experimental data of a range of tested frequencies and loadings of the motor in order to provide training data for untested conditions.

Inductive power transfer (IPT) systems unleashed a new era for wireless charging systems of electric vehicles (EVs). EVs can be charged stationary or in motion through IPT systems. Different types of power pads are introduced to increase the efficiency of IPT systems, among them, the most commonly used power pads is circular structure, another type with good potential is double D (DD) structure as recommended in Society of Automotive Engineers (SAE) recommended practice J2954. In this paper, a new power pad structure is proposed by combining circular and DD power pads to further improve the performance. Physical models of circular and DD power pads are first built by using specifications in SAE J2954, these models are then combined to form the proposed power pad. The design and simulation of the proposed power pad are carried out using the Finite Element Analysis (FEA) simulation software, ANSYS Maxwell 3D. Simulation results indicate that the proposed power pad shows improved performance compared to circular and DD power pads.

To provide safe and flexible power transfer to electric vehicles (EVs), wireless charging systems have been introduced. By improving the overall system efficiency of wireless charging systems for EVs, the range anxiety of EVs can be significantly reduced. In this paper, a comparative characteristic analysis is carried out between circular and double D (DD) power pads for EV wireless charging systems. The Society of Automotive Engineers (SAE) recommended practice J2954 is followed for designing physical dimension of these power pads. Finite Element Analysis (FEA) tool ANSYS Maxwell 3D is used for simulation. Parameters such as coupling coefficient and mutual inductance are evaluated for each type of power pads by applying vertical and horizontal misalignment. It is found that DD power pads exhibit promising characteristics for EV wireless charging systems.