Multiple Types Research Articles

IRNLGD: An Edge Detection Algorithm with Comprehensive Gradient Directions for Tidal Stream Turbine

Tidal stream turbines (TSTs) harness the kinetic energy of tides to generate electricity by rotating the rotor. Biofouling will lead to an imbalance between the blades, resulting in imbalanced torque and voltage across the windings, ultimately polluting the grid. Therefore, rotor condition monitoring is of great significance for the stable operation of the system. Image-based attachment detection algorithms provide the advantage of visually displaying the location and area of faults. However, due to the limited availability of data from multiple machine types and environments, it is difficult to ensure the generalization of the network. Additionally, TST images degrade, resulting in reduced image gradients and making it challenging to extract edge and other features. In order to address the issue of limited data, a novel non-data-driven edge detection algorithm, indexed resemble-normal-line guidance detector (IRNLGD), is proposed for TST rotor attachment fault detection. Aiming to solve the problem of edge features being suppressed, IRNLGD introduces the concept of “indexed resemble-normal-line direction” and integrates multi-directional gradient information for edge determination. Real-image experiments demonstrate IRNLGD’s effectiveness in detecting TST rotor edges and faults. Evaluation on public datasets shows the superior performance of our method in detecting fine edges in low-light images.

Design and experiment of an intelligent surface mobile robot for the microbial slow-release control of pollution

In this paper, to solve the current problems of water quality pollution of rivers, such as multiple types, high toxicity and difficult degradation, we design an intelligent surface mobile robot based on the slow release of microorganisms. In the robot, the skeleton has a ship-type structure welded with stainless steel equilateral angle steel; the function module comprises a power supply part, motor part, transmission part, and release part; the wireless monitoring module is based on GPRS and EtherCAT networking technologies and consists of four submodules, including a data acquisition submodule, transmission submodule, database server and monitoring center. The field test results show stable overall transmission performance and wireless data transmission performance of the robot. When the water depth of the rotating cylinder is 0.3 m, and the speed of the rotating cylinder is 15 r/min, the pollution control ability of the robot is outstanding. The experimental results also verify the feasibility of the robot in treating water pollution. The robot can greatly improve the mechanization, automation and efficiency of water pollution control and facilitate the centralized monitoring of joint operations, so it is worth popularizing.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Clinical utility of multiplex PCR in the detection of pathogens from sterile body fluids.

The identification of pathogens in normally sterile body fluid (NSBF) is performed using routine culture, the current gold standard. Limitations of this method include sensitivity and increased turnaround times which could potentially delay vital patient care, especially antimicrobial therapy. Adaptations of NSBF in blood culture bottles prompted us to consider the utility of additional methods to bridge the gap in diagnostic challenges for these life-threatening infections. Multiplex molecular panels have been manufactured for use with multiple specimen types including blood, cerebral spinal fluid, stool, and respiratory. Therefore, the purpose of this study was to evaluate the off-label use of ePlex Blood Culture Identification Panels on positive body fluids grown in blood culture bottles for the detection of microorganisms for research purposes.

Subscription Pricing for Free Delivery Services

Many firms that offer home delivery of products purchased online now provide consumers the option of subscribing to a membership plan for free delivery of limited or unlimited number of orders instead of customers paying delivery charges for each order. This paper seeks to understand the benefits to firms and consumers of introducing such free-delivery subscription (FDS) plans to complement their traditional pay-for-delivery (PFD) option, characterize the firm's optimal FDS subscription pricing strategy, and study the effects of product, consumer, and market characteristics on the subscription fees and firm's profits. We develop and analyze an economic model that incorporates salient features of product delivery settings that prior models of subscription pricing do not fully capture. The features include simultaneous availability of PFD and FDS options, lift in a consumer's demand when she switches from PFD to FDS, the possibility of attracting new consumers when the firm introduces FDS, and order batching by customers due to per-order delivery charges and free-delivery order allowances. Our utility-based approach to model consumers’ purchasing choices incorporates consumer heterogeneity both in terms of their utility for the product and their shopping preference under the PFD option. For homogeneous consumers, we show that introducing a FDS plan raises the firm's profit, establish the optimality of offering unlimited free-delivery allowance, and analyze comparative statics. With multiple customer types, our analysis of two subscription pricing strategies for FDS plans—a single “Universal” plan and multiple “Tiered” plans with varying subscription fees and free-delivery limits—leads to several interesting insights on delivery pricing strategies, as well as drivers and sensitivity of the optimal policy, profits, and number of subscribers. We supplement this analysis with extensive computational experiments that reveal a substantial increase in profits when a firm adds one or more FDS plans to its PFD option even though the optimal subscription fee(s) only covers part of the firm's actual shipping costs. These results also permit us to gauge the influence and understand the trends in the profitability of the Universal and Tiered plans as various parameters change.

The Role of Regulatory T cells (Tregs) in Tumorigenesis: A Comprehensive Literature Review

Introduction: Regulatory T cells (Tregs) are a subpopulation of CD4+ T lymphocytes that contribute to immune homeostasis by suppressing excessive immune activation. However, these immunosuppressive properties can lead to the suppression of anti-tumor immune responses. Depletion or blocking of Tregs through therapeutics has emerged as a possible method for enhancing anti-tumor immunity. However, the lack of selective targeting of Tregs in the tumor microenvironment is a significant limitation to the effectiveness of Treg therapies. Therefore, this investigation aims to review current literature on how Tregs suppress the antitumor immune response and how they can be targeted to promote anti-tumor immunity. Methods: This review examines recent literature on Tregs in the tumor microenvironment, focusing on both cell-contact dependent and independent mechanisms. Clinical trial studies were also included to assess therapeutic targeting of Tregs. The PubMed database was systematically searched for English articles from 2010 to present, supplemented by manual searches without date restrictions. Boolean expressions ensured comprehensive study retrieval. Results: The involvement of Tregs in the development of multiple cancer types is evident, and targeting these cells could potentially enhance the efficacy of antitumor immunity. In addition, we compiled a list of the novel approaches currently being used for Treg targeting in the context of cancer. Discussion: This review has identified the most promising targets for Treg-based therapies, opening avenues for accelerating the development of innovative cancer treatments. Conclusion: Our literature review offers insights into the complex interplay between the immune system and cancer. The understanding of this interaction is not just an endpoint but could potentially act as a steppingstone towards new scientific discoveries.

Reactive axillary lymph nodes after COVID-19 mRNA vaccination: comparison of mRNA vs. attenuated whole-virus vaccines.

To compare the incidence and natural course of reactive axillary lymph nodes (RAL) between mRNA and attenuated whole-virus vaccines using Deauville criteria. In this multi-institutional PET-CT study comprising multiple vaccine types (Pfizer-BioNTech/Comirnaty, Moderna/Spikevax, Sinovac/CoronaVac and Janssen vaccines), we evaluated the incidence and natural course of RAL in a large cohort of oncological patients utilizing a standardized Deauville scaling system (n=522; 293 Female, Deauville 3-5 positive for RAL). Univariate and multivariate analyses were conducted to evaluate the predictive value of clinical parameters (absolute neutrophil count [ANC], platelets, age, sex, tumor type, and vaccine-to-PET interval) for PET positivity. Pfizer-BioNTech/Comirnaty and Moderna vaccines revealed similar RAL incidences for the first 20 days after the second dose of vaccine administration (44% for the first 10 days for both groups, 26% vs. 20% for 10-20 days, respectively for Moderna and Pfizer). However, Moderna recipients revealed significantly higher incidences of RAL after 20 days compared to Pfizer-BioNTech/Comirnaty, with nodal reactivity spanning up to the 9th week post-vaccination (15% vs. 4%, respectively P < 0.001). No RAL was observed in patients who received either a single dose of J&J vaccine or two doses of CroronaVac. Younger patients showed increased likelihood of RAL, otherwise, clinical/demographic parameters were not predictive of RAL ( P = 0.014 for age, P > 0.05 for additional clinical/demographic parameters). RAL based on strict PET criteria was observed with mRNA but not with attenuated whole-virus vaccines, in line with higher immunogenicity and stronger protection offered by mRNA vaccines.

Linoleic acid exhibits anti-proliferative and anti-invasive activities in endometrial cancer cells and a transgenic model of endometrial cancer

ABSTRACT Emerging evidence has provided considerable insights into the integral function of reprogramming fatty acid metabolism in the carcinogenesis and progression of endometrial cancer. Linoleic acid, an essential fatty acid with the highest consumption in the Western diet regimen, has shown pro-tumorigenic or anti-tumorigenic effects on tumor cell growth and invasion in multiple types of cancer. However, the biological role of linoleic acid in endometrial cancer remains unclear. In the present study, we aimed to investigate the functional impact of linoleic acid on cell proliferation, invasion, and tumor growth in endometrial cancer cells and in a transgenic mouse model of endometrial cancer. The results showed that Linoleic acid significantly inhibited the proliferation of endometrial cancer cells in a dose-dependent manner. The treatment of HEC-1A and KLE cells with linoleic acid effectively increased intracellular reactive oxygen species (ROS) production, decreased mitochondrial membrane potential, caused cell cycle G1 arrest, and induced intrinsic and extrinsic apoptosis pathways. The anti-invasive ability of linoleic acid was found to be associated with the epithelial-mesenchymal transition process in both cell lines, including the decreased expression of N-cadherin, snail, and vimentin. Furthermore, treatment of Lkb1 fl/fl p53 fl/fl transgenic mice with linoleic acid for four weeks significantly reduced the growth of endometrial tumors and decreased the expression of VEGF, vimentin, Ki67, and cyclin D1 in tumor tissues. Our findings demonstrate that linoleic acid exhibits anti-proliferative and anti-invasive activities in endometrial cancer cell lines and the Lkb1 fl/fl p53 fl/fl mouse model of endometrial cancer, thus providing a pre-clinical basis for future dietary interventions with linoleic acid in endometrial cancer.

A Lightning Classification Method Based on Convolutional Encoding Features

At present, for business lightning positioning systems, the classification of lightning discharge types is mostly based on lightning pulse signal features, and there is still a lot of room for improvement. We propose a lightning discharge classification method based on convolutional encoding features. This method utilizes convolutional neural networks to extract encoding features, and uses random forests to classify the extracted encoding features, achieving high accuracy discrimination for various lightning discharge events. Compared with traditional multi-parameter-based methods, the new method proposed in this paper has the ability to identify multiple lightning discharge events and does not require precise detailed feature engineering to extract individual pulse parameters. The accuracy of this method for identifying lightning discharge types in intra-cloud flash (IC), cloud-to-ground flash (CG), and narrow bipolar events (NBEs) is 97%, which is higher than that of multi-parameter methods. Moreover, our method can complete the classification task of lightning signals at a faster speed. Under the same conditions, the new method only requires 28.2 µs to identify one pulse, while deep learning-based methods require 300 µs. This method has faster recognition speed and higher accuracy in identifying multiple discharge types, which can better meet the needs of real-time business positioning.

Substituting Poly(ethylene glycol) Lipids with Poly(2-ethyl-2-oxazoline) Lipids Improves Lipid Nanoparticle Repeat Dosing.

Poly(ethylene glycol) (PEG)-lipids are used in Food-and-Drug-Administration-approved lipid nanoparticle (LNP)-RNAdrugs, which are safe and effective. However, it is reported that PEG-lipids may also contribute to accelerated blood clearance and rare cases of hypersensitivity; this highlights the utility of exploring PEG-lipid alternatives. Here, it is shown that LNPs containing poly(2-ethyl-2-oxazoline) (PEOZ)-lipids can deliver messenger RNA (mRNA) to multiple cell types in mice inside and outside the liver. In addition, it is reported that LNPs formulated with PEOZ-lipids show reduced clearance from the bloodstream and lower levels of antistealth lipid immunoglobulin Ms than LNPs formulated with PEG-lipids. These data justify further exploration of PEOZ-lipids as alternatives to PEG-lipids in LNP-RNA formulations.

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder

Radiation source signal sorting in complex environments is currently a hot issue in the field of electronic countermeasures. The pulse repetition interval (PRI) can provide stable and obvious parametric features in radiation source identification, which is an important parameter relying on the signal sorting problem. To solve the problem linked to the difficulties in sorting the PRI in complex environments using the traditional method, a signal sorting method based on a parallel denoising autoencoder is proposed. This method implements the binarized preprocessing of known time-of-arrival (TOA) sequences and then constructs multiple parallel denoising autoencoder models using fully connected layers to achieve the simultaneous sorting of multiple signal types in the overlapping signals. The experimental results show that this method maintains high precision in scenarios prone to large error and can efficiently filter out noise and highlight the original features of the signal. In addition, the present model maintains its performance and some robustness in the sorting of different signal types. Compared with the traditional algorithm, this method improves the precision of sorting. The algorithm presented in this study still maintains above 90% precision when the pulse loss rate reaches 50%.

Mendelian randomization analysis using multiple biomarkers of an underlying common exposure.

Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are co-regulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.

Ocean warming shapes embryonic developmental prospects of the next generation in Atlantic cod

Abstract Although early embryonic protein production relies exclusively on maternal molecules such as messenger RNAs (mRNAs) incorporated into ovarian follicles, knowledge about any thermally induced, intergenerational effects is scarce in ectotherms. Here, we investigated how elevated temperatures (9°–12°C vs. 6°C) during oogenesis influenced the next generation by targeting maternal mRNAs in Atlantic cod (Gadus morhua) embryos, in view of up- and down-regulated genes in ovarian follicles of pre-spawning adults. Overall, the spawning female liver showed significantly higher levels of free amino acids and N-metabolites at 9°C than at 6°C. Higher-than-optimal temperatures induced adjustments in embryo transcriptome proportional to the temperature increase relative to the control group. The adjustments included alterations in maternal-effect genes, which are developmentally conserved among vertebrates. The transcriptomic differences for a selection of genes in embryos were reflected in ovarian follicles (containing multiple cell types) several months ahead of the spawning season, implying that environmental conditions of the adults are key for adjusting the genetic instructions for offspring development. This programming of fundamental traits from mother to offspring appears part of a sophisticated process to adapt the offspring to a changing ocean, though within life stage-specific, physiological thermal tolerance windows.

Efficient Access to Elusive 1D 13C NMR Spectra through Highly Resolved 1H, 13C-Long-Range Correlation Spectroscopy.

A method for obtaining 1D 13C NMR spectra from natural products or metabolites using proton detection is described. The approach delivers singlets for every 13C signal without conducting any broadband 1H decoupling (CPD) and is based on calculating 13C projections from constant-time HMBC and conventional HSQC experiments, recorded at high digital resolution and processed to pure phases. Paramount to the proposed method is the implication of nonuniform sampling and echo processing. The echo processing produces phase-sensitive 2D CT-HMBC spectra with narrow 13C signal line shapes. Two simple HMBC pulse sequences are utilized with the suppression of homo- and heteronuclear couplings. Due to the removal of the 1H multiplet structure in F1 (no tilt at higher digital resolution), 13C singlets arise. An overall increase in 13C signal-to-noise (SINO) for all types of carbon multiplicities is observed, making the proposed technique superior compared to direct 13C excitation. For otherwise difficult-to-measure quaternary carbon atoms, a SINO enhancement of up to 6 and 12 depending on F1 resolution (3 and 6 Hz/point) is reported. Echo/anti-Echo signal detection cleans up the spectrum. Nonuniform sampling (NUS) lays the groundwork to significantly reduce the total acquisition time. Final 1D 13C projections are obtained by combining the 13C projection from CT HMBC and conventional HSQC. This orthogonal concept of combining the 13C projections from different spectra inherently minimizes the risk of missing 13C cross-peaks by inappropriate setting of long-range nJHC coupling delays and the shortcoming of T2 relaxations. The advantages and some limitations of the concept are discussed.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Does the duration of diabetes increase the risk of cancer? A nationwide population-based cohort of patients with new-onset diabetes and a matched reference cohort.

Diabetes mellitus and cancer are both common health issues, but the correlation between these two diseases remains unclear. We investigated the association of cumulative exposure of diabetes mellitus as an indication of hyperglycemia in terms of disease duration on multiple cancer types. We hypothesized that the risk of cancer would increase over time after the onset of diabetes. The study population consisted of a population-based cohort of 398,708 people and it was constructed from the Finnish CARING project. The Diabetes group consisted of 185,258 individuals, and the non-diabetic reference group comprised 187,921 individuals. Over 4.1 million person-years were accumulated, and the median follow-up time was 10.55 years. In the diabetes group, 25,899 cancer cases were observed compared with 23,900 cancers in the non-diabetic group. We did not find a clear relationship between the duration of diabetes mellitus and most cancer types examined. However, for cancers of the pancreas, prostate gland, bronchus, and lungs, a temporal relationship was found. Furthermore, even within the cancer types where the relationship was detected, it did not change over time. These findings indicate that diabetes does not independently increase the risk of cancer. Instead, the development of diabetes may be attributed to shared risk factors with cancer, such as obesity and/or insulin resistance accompanied by hyperinsulinemia. Thus, it is likely that the clock for increased cancer risk starts ticking already before onset of diabetes and hyperglycemia.

A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.

SMOKING AND FLAP SURVIVAL: A COMPREHENSIVE SYSTEMATIC REVIEW

Background: Free flap reconstruction of complex and/or large wounds, whether traumatic, following cancer resection, or for other reconstructive needs has become a commonly accepted practice. Multiple different types of free flaps are utilized for varying needs based on the individual patient and the defect that requires coverage. active smokers who underwent a nonelective traumatic reconstruction with a perforator-based fasciocutaneous flap (anterolateral thigh [ALT] flap) will have a higher incidence of smoking-related complications compared to the use of a muscle-only flap. The aim: The aim of this study to show about smoking and flap survival. Methods: By the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. This search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed, SagePub, and Google Scholar were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search get 9 articles, whereas the results of our search on SagePub get 441 articles, on Google Scholar 4830 articles. Records remove before screening are 2771, so we get 2509 articles fos screening. After we screened based on record exclude, we compiled a total of 10 papers. We included five research that met the criteria. Conclusion: Smoking decreases the alveolar oxygen pressure and subcutaneous wound tissue oxygen, and nicotine causes vasoconstriction, smokers are more likely to experience flap loss, hematoma, or fat necrosis than non-smokers. Preoperative and post-operative abstinence period of at least 1 week is necessary for smokers who undergo flap operations.

An Adaptive Reclosing Scheme for Cross-Line Faults on Double-Circuit Wind Power Outgoing Lines with Shunt Reactors

Wind turbines are vulnerable to negative sequence current injection, the conventional automatic reclosing scheme for wind power outgoing lines, since we may inject negative sequence components into the system and reclose it without distinguishing the nature of the fault through rectification. Moreover, reclosing in permanent faults could induce a secondary impact on the system. To solve the above problems, an adaptive reclosing scheme for cross-line faults on double-circuit wind power outgoing lines with shunt reactors is proposed. Firstly, a new tripping strategy and a single-side partial-phase reclosing method are proposed for multiple types of outgoing line faults, while, simultaneously, phase-to-phase coupling loops are established. Secondly, the criteria of fault nature are established based on the fault phase shunt reactor current and fault phase voltage characteristics, and transient faults are rapidly and accurately distinguished from permanent ones according to the criteria. Finally, the theoretical derivation and simulation experiments are conducted on the PSCAD/EMTDC platform to demonstrate that the proposed adaptive reclosing method is applicable to avoid the injection of negative sequence currents into wind turbines. Meanwhile, the success rate of reclosing for wind power outgoing line is significantly improved and the continuity of power transmission on the wind farm(s) is ensured.

Joint trajectories of bullying victimization and sexual harassment victimization as predictors of adolescent alcohol use.

The present study examined the independent and joint effects of bullying victimization and sexual harassment victimization on adolescent alcohol use over time within a community sample of adolescents. Adolescents aged 13-15 years old at baseline (N = 800, Mage = 14.42, SD = 0.83; 57.5% female) recruited from Western New York State made five online survey reports of peer victimization and alcohol use over a 2-year period. Latent class growth analysis was used to identify trajectory classes of victimization from bullying and sexual harassment over time, and regression modeling was used to examine the associations with later alcohol use. Three developmental courses were identified for bullying victimization (moderate/decreasing, high/decreasing, never or low) and for sexual harassment victimization (moderate/decreasing, moderate/increasing, never or low). Adolescents in the moderate/decreasing group of bullying victimization subsequently consumed more alcoholic drinks when they drank. Belonging to the moderate/increasing group of sexual harassment was associated with increased later alcohol intoxication and number of drinks. Bullying victimization and sexual harassment victimization were concurrently correlated over time. Adolescents who followed the joint trajectory group of moderately decreasing bullying and increasing sexual harassment were more likely to report increased later alcohol intoxication and number of drinks. Moderate levels of bullying victimization along with increasing sexual harassment victimization are associated prospectively with greater alcohol use in adolescence. Findings highlight the importance of considering the cumulative, joint effects of multiple types of peer victimization on adolescent health outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).