bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between weight-adjusted-waist index and urge urinary incontinence: a cross-sectional study from NHANES 2013 to 2018
Authors: Sun Haohao , Huang Jingxi , Tang Hao , Wei Bingbing 
Published: January 2024

AbstractThis study aimed to investigate the association between urge urinary incontinence (UUI) and weight-adjusted waist circumference index (WWI), a newly developed measure of obesity. Data from the 2013–2018 National Health and Nutrition Examination Survey (NHANES) were included in the present cross-sectional study. Urge urinary incontinence was identified by self-reported urine leakage before reaching the toilet. Weighted multivariate logistic regression and generalized additive models were used to investigate the connection between WWI and UUI and its nonlinearity. The nonlinear relationship was explored using smoothed curve fitting. Additionally, further analyses were performed on subgroups and interaction tests were conducted. In the study, a total of 14,118 individuals were enrolled, with a UUI prevalence rate of 21.18%. Overall UUI was more prevalent with elevated WWI (OR 1.20, 95% CI 1.13–12.8, P < 0.0001), which similar results were observed in weekly (OR 1.32, 95% CI 1.18–1.48, P < 0.0001) and daily (OR 1.27, 95% CI 1.06–1.53, P = 0.0091) UUI. And this connection remained steady among all subgroups (P > 0.05 for all interactions). Smoothed curve fitting showed no nonlinear relationship between WWI and UUI. In addition, a stronger correlation was found between WWI and UUI risk than other obesity indicators such as waist circumference (WC) and body mass index (BMI). Among US adults, weight-adjusted waist circumference index values are positively associated with elevated odds of UUI and show stronger associations than WC and BMI. Further studies are required to elucidate the causal relationship between WWI and UUI.
Multiple ageing effects on testicular/epididymal germ cells lead to decreased male fertility in mice
Authors: Endo Tsutomu , Kobayashi Kiyonori , Matsumura Takafumi , Emori Chihiro , Ozawa Manabu , Kawamoto Shimpei , Okuzaki Daisuke , Shimada Keisuke , Miyata Haruhiko , Shimada Kentaro , Kodani Mayo , Ishikawa-Yamauchi Yu , Motooka Daisuke , Hara Eiji , Ikawa Masahito 
Published: January 2024

AbstractIn mammals, females undergo reproductive cessation with age, whereas male fertility gradually declines but persists almost throughout life. However, the detailed effects of ageing on germ cells during and after spermatogenesis, in the testis and epididymis, respectively, remain unclear. Here we comprehensively examined the in vivo male fertility and the overall organization of the testis and epididymis with age, focusing on spermatogenesis, and sperm function and fertility, in mice. We first found that in vivo male fertility decreased with age, which is independent of mating behaviors and testosterone levels. Second, overall sperm production in aged testes was decreased; about 20% of seminiferous tubules showed abnormalities such as germ cell depletion, sperm release failure, and perturbed germ cell associations, and the remaining 80% of tubules contained lower number of germ cells because of decreased proliferation of spermatogonia. Further, the spermatozoa in aged epididymides exhibited decreased total cell numbers, abnormal morphology/structure, decreased motility, and DNA damage, resulting in low fertilizing and developmental rates. We conclude that these multiple ageing effects on germ cells lead to decreased in vivo male fertility. Our present findings are useful to better understand the basic mechanism behind the ageing effect on male fertility in mammals including humans.
Stanford Hypnosis Integrated with Functional Connectivity-targeted Transcranial Stimulation (SHIFT): a preregistered randomized controlled trial
Authors: Faerman Afik , Bishop James H. , Stimpson Katy H. , Phillips Angela , Gülser Merve , Amin Heer , Nejad Romina , DeSouza Danielle D. , Geoly Andrew D. , Kallioniemi Elisa , Jo Booil , Williams Nolan R. , Spiegel David 
Published: January 2024

AbstractHypnotizability, one’s ability to experience cognitive, emotional, behavioral and physical changes in response to suggestions in the context of hypnosis, is a stable neurobehavioral trait associated with improved treatment outcomes from hypnosis-based therapy. Increasing hypnotizability in people who are low-to-medium hypnotizable individuals could improve both the efficacy and effectiveness of therapeutic hypnosis as a clinical intervention. Hypnotizability is associated with dorsolateral prefrontal cortex (DLPFC) functions and connectivity with the salience network, yet there is conflicting evidence as to whether unilateral inhibition of the DLPFC changes hypnotizability. We hypothesized that using personalized neuroimaging-guided targeting to non-invasively stimulate the left DLPFC with transcranial magnetic stimulation (TMS) would temporarily increase hypnotizability. In a preregistered, double-blinded, randomized controlled trial, we recruited a sample of 80 patients with fibromyalgia syndrome, a functional pain disorder for which hypnosis has been a demonstrated beneficial non-pharmacological treatment option. All participants were TMS-naive. Participants were randomly assigned to active or sham continuous theta-burst stimulation over a personalized neuroimaging-derived left-DLPFC target, a technique termed SHIFT (Stanford Hypnosis Integrated with Functional Connectivity-targeted Transcranial Stimulation). We tested our hypothesis using the hypnotic induction profile scores, a standardized measure of hypnotizability. Pre-to-post SHIFT change in the hypnotic induction profile scores was significantly greater in the active versus sham group after 92 s of stimulation (P = 0.046). Only the active SHIFT group showed a significant increase in hypnotizability following stimulation (active: P < 0.001; sham: P = 0.607). As such, modulation of trait hypnotizability is possible in humans using non-invasive neuromodulation. Our findings support a relationship between the inhibition of the left DLPFC and an increase in hypnotizability. Dose–response optimization of spaced SHIFT should be explored to understand the optimal dose–response relationship. ClinicalTrials.gov registration: NCT02969707.
A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images
Authors: Zebari Nechirvan Asaad , Mohammed Chira Nadheef , Zebari Dilovan Asaad , Mohammed Mazin Abed , Zeebaree Diyar Qader , Marhoon Haydar Abdulameer , Abdulkareem Karrar Hameed , Kadry Seifedine , Viriyasitavat Wattana , Nedoma Jan , Martinek Radek 
Published: January 2024

AbstractDetecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.
Disturbance observer‐based model predictive control of a coaxial octorotor with variable centre of gravity
Authors: Derakhshan Reza Ebrahimpour , Danesh Mohammad , Moosavi Hassan 
Published: January 2024

AbstractThis paper presents a model predictive control (MPC) approach based on the extended disturbance observer (EDOB) for trajectory tracking of a coaxial octorotor unmanned aerial vehicle (UAV). First, the system dynamic model is derived using Newton–Euler relations in the presence of time‐varying centre of gravity (COG); then, a two‐loop cascade structure is presented to perform the trajectory tracking task. Both loops are controlled using MPC with feedforward compensation based on the EDOB to improve disturbance rejection abilities. When the mass changes, the moment of inertia and COG are affected. The EDOB simultaneously estimates the effects of time‐varying mass, external disturbances, and parametric uncertainties in six degrees of freedom. After obtaining virtual control inputs using designed controllers, constrained control allocation is used to obtain rotors speed in a valid range. The proposed control scheme is evaluated using simulation. The simulation results show the ability of the developed control strategy in accurate trajectory tracking and stable flight in different conditions and being robust to uncertainty and disturbance.
Ultra‐high frequency sensors positioning on the power transformer to mitigate the negative effects on the partial discharge localization accuracy
Authors: Jangjoo Mohammad Amin , Allahbakhshi Mehdi , Mirzaei Hassan Reza 
Published: January 2024

AbstractThis paper proposes a new method for improving the partial discharge (PD) localization accuracy by detecting the best places of ultra‐high frequency (UHF) sensors on the power transformer tank. In this way, firstly, the critical sites inside the power transformer that can produce PD are determined. Then the sensors’ positioning is done in such a way that if the PD happens at each point of these critical sites, the distance between the estimated PD location obtained by solving the time difference of arrival (TDOA) equations and the actual PD location is laid in an acceptable range. For this purpose, the TDOA equations are linearized around the critical PD coordinates as a matrix form A.X = B. The configuration of the sensor positions that minimizes the condition number of the matrix A is the best configuration. To evaluate the effectiveness of the proposed method, several simulations and evaluations are performed on a power transformer in the CST software. The obtained results and comparing them with the previous method and also conventional arbitrary sensors positioning confirm the effectiveness of this method.
Research of simulation and experiment on arc characteristics of gas mechanical switch
Authors: Jiang Cheng , Qu Lu , Yu Zhangqing , Gan Zhizheng , yan Xin , Zeng Rong , Huang Yulong , Li Shaofu 
Published: January 2024

AbstractAn arc is the high‐temperature discharge plasma produced in the opening process of mechanical switches, which directly affects the breaking capability of a hybrid DC circuit breaker. According to the physical mechanism of an electric arc, the construction of an arc model for simulation analysis is an important technical means in the electrical field. In this study, based on the theory of magneto hydrodynamics (MHD), a gas mechanical switch model of a natural commutation DC circuit breaker with a compound gap is established. The arc motion process under different conditions is simulated and calculated. The influence of different initial pressures, different opening speeds, and different striking currents on the arc voltage characteristics is analysed. The results show that the larger the gas pressure, the smaller the arc volume and the higher the arc voltage. The faster the opening speed, the longer the arc and the higher the arc voltage; with the increase of the current, the arc voltage increases rapidly at a low current, while the arc voltage increases slowly at a high current.
Temperature drift suppression for micro‐electro‐mechanical system gyroscope based on vibrational‐displacement control with harmonic amplitude ratio
Authors: Bu Feng , Feng Rui , Guo Shuwen , Zhou Ming , Wang Yiwang , Wang Feng 
Published: January 2024

AbstractMicro‐electro‐mechanical system gyroscope detection output is susceptible to drift due to ambient temperature variations. Furthermore, the drive‐mode vibration amplitude is affected by the signal pickup circuit and structural parameters, which significantly influence the temperature drift of the bias and scale factor (SF). This study proposes a vibration amplitude control method based on harmonic amplitude sideband‐ratio (SBR), namely SBR‐AGC, which characterizes the vibration amplitude using the harmonic amplitude ratio of the vibrational electrical signal. The constancy of vibration amplitude is maintained via closed‐loop control to suppress bias and SF temperature drift. The experimental results on cobweb‐like disk resonator gyroscope reveal that the temperature coefficient of bias in the SBR‐AGC mode lies within −20 to 60°C and decreases by 36%, and the temperature coefficient of the SF lies within −10 to 50°C and decreases by 49.7%. Therefore, the environmental adaptability of the gyroscope is effectively improved.
A family of Z‐source‐based half‐bridge inverters: Topology derivation
Authors: Aalami Mohmmadamin , Babaei Ebrahim , Zadeh Saied Ghassem 
Published: January 2024

AbstractZ‐source‐based half‐bridge inverters are a type of Z‐source inverters whose outer stage is a half‐bridge inverter. This paper presents a generalized structure from which 16 configurations can be derived. Once this topology has been introduced, one of the derived topologies, out of 16, is analyzed in detail based on the appropriate control method. According to these analysis results, this topology's boost‐factor and some equations to design passive and active devices are obtained. Next, it will be tried to derive the equations for each topology based on the obtained results for one of the configurations. In the next step, the topology that has been analyzed is considered to evaluate the efficiency of this topology using the power losses study. Hereafter, the generalized configuration's merits and downsides are studied by comparing it with other similar topologies. Finally, the experiment in the final section checks the operation and validity of the obtained results.
Dual‐input single‐output high step‐up DC–DC converter for renewable energy applications
Authors: Mohammadi Farid , Khorsandi Amir 
Published: January 2024

AbstractThis paper introduces a dual‐input single‐output (DISO) non‐isolated DC–DC converter with the capability to accommodate multiple input ports. It supports both bidirectional and unidirectional power flows. The proposed converter employs the switched‐capacitors (SC) technique, resulting in a high‐ voltage transfer gain. Although the use of the SC technique leads to discontinuous input current, this issue has been addressed by incorporating an inductor at the input side. The DISO high‐voltage gain converter offers flexible control options, reduces current and voltage stress on semiconductors, and imposes no duty cycle limitations due to the proposed switching methods. Furthermore, when Port 2 fails, the proposed converter can transfer energy to the load by sources in Port 1 without interruption. The steady‐state model and small‐signal analysis (SSA) of the proposed converter are examined at continuous conduction mode (CCM). To evaluate accurately, the proposed converter is compared with the recently presented converters based on important characteristics. Additionally, a comprehensive power loss analysis is performed. Following the design specifications, an evaluation and testing of a prototype are conducted to verify the feasibility and performance of the converter.
Machine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid
Authors: Zafar Ahsan , Che Yanbo , Faheem Muhammad , Abubakar Muhammad , Ali Shujaat , Bhutta Muhammad Shoaib 
Published: January 2024

AbstractDuring the fourth energy revolution, artificial intelligence implementation is necessary in all fields of technology to meet the increasing energy demands and address the diminishing fossil fuel reserves, necessitating the shift towards smart grids. The authors focus on predicting parameters accurately to minimise loss and improve power generation capacity in smart grids, given that accurate parameter prediction is essential for traditional power grid stations converting to smart grids. The authors employ an artificial intelligence‐based machine learning model, namely the long short‐term memory, to predict parameters of a solar power plant. After analysing the results obtained from the long short‐term memory model in graphical visualisation, the model is further improved using two different techniques namely, a convolutional neural network‐long short‐term memory and the authors proposed an autoencoder long short‐term memory. Comparing the results of these models, the study finds that autoencoder long short‐term memory outperforms the convolutional neural network‐long short‐term memory as well as simple long short‐term memory. Thus, the use of artificial intelligence in this study substantially enhances the precision of parameter prediction by augmenting the performance of rudimentary machine learning models, thereby facilitating the attainment of a resilient and resourceful power system that overcomes power losses and ameliorates production capacity in the context of Smart Grids.
An operation site security detection method based on point cloud data and improved YOLO algorithm under the architecture of the power internet of things
Authors: Yang Shibo , Wang Yu , Guo Shuai , Feng Shijie 
Published: January 2024

AbstractAn operation site safety detection method based on point cloud data and improved YOLO algorithm under the power Internet of Things architecture is proposed to address the complex environment of power construction sites and the poor effectiveness of most existing object detection methods. Firstly, an operation site safety supervision system was designed based on the power Internet of Things architecture, and efficient image processing was achieved through cloud edge collaboration. Then, point cloud data and on‐site monitoring information are used on the edge side to extract the accessible area, ensuring that the target is located in a safe area. Finally, the YOLO algorithm is improved in the cloud by using clustering algorithms, network structure optimization, and other methods, and used to detect targets and determine whether their behaviour meets the safety requirements of the operation site. Based on the PyTorch deep learning framework, the proposed method was experimentally demonstrated, and the results showed that its average detection accuracy and time were 94.53% and 68 ms, respectively, providing technical support for achieving remote monitoring of power operation sites.
How coaches design small-sided games in rugby union: a practice-based review
Authors: Wintershoven Koen , Beaven Martyn , Gill Nicholas , McMaster Daniel Travis 
Published: January 2024

Background: Game-based training (GBT) is a holistic method for improving performance in team sports. Task constraints and design parameters can affect training effectiveness. The evidence on how small-sided game (SSG) design in rugby union (RU) influences outcome variables is lagging. No study has investigated the contemporary SSG practices of RU practitioners in different performance contexts.
Methods An anonymous online survey with twenty-four questions was distributed to RU training staff six times worldwide in one year through multiple e-platforms. A frequency analysis was used to assess responses and contrast it with the literature.
Results: 115 surveys were recorded. The dominant characteristics were >5 y experience (66% of respondents), head coach position (53%), New Zealand-based (77%), coaching male players (69%) on the local/school level (68%). Higher-level practitioners applied larger SSG formats with shorter duration, using more extreme pitch sizes and less tackling, for conditioning purposes primarily. Lower-level practitioners targeted fun and technique. Smaller game formats and tackling were used more in male and youth players, and on the lower levels. Further observed differences relate to practitioner role, experience, and geographical location.
Conclusion: Practitioners design SSG differently depending on their coaching background, target population, competition level, and geographical location. Practitioner education on GBT would optimise RU training practice. This study provides performance-contextual insights.
Management of Neurologic Complications in Patients with Brain and Spine Tumors
Authors: V. Bonm Alipi , Taylor Lynne P. 
Published: January 2024

AbstractPatients with brain and spine tumors represent a distinct population with unique needs. We provide a practical review of neurologic care in this group with an emphasis on familiarizing the general neurologist to the nuances of neuro-oncologic supportive care. We review the management of cerebral edema, steroid dosing, and pertinent side effects. We discuss seizure management, including choice of anticonvulsants, putative antitumor effects, and important seizure mimics like drop attacks. We review the presentation and symptomatology of stroke-like migraine attack after radiation therapy (SMART syndrome). We describe the signs and symptoms that should prompt concern for metastatic spinal cord compression, as well as both acute and definitive treatment options. Finally, we discuss the underappreciated incidence of venous thromboembolic events, particularly in patients with gliomas, and review the data on management.