Uncovering the Invisible: The Role of High-density Catheters in Recognizing Fractionated Signals in Pulmonary Vein Isolation.

The HD Grid multipolar mapping catheter has emerged as an invaluable tool for greater effectiveness of pulmonary vein isolation (PVI). In the cases described here, fractionated signals seen with the HD Grid catheter at the left atrial appendage (LAA) and left superior pulmonary vein (LSPV) junction were ablated. These signals are not likely to be visualized with conventional catheters and may cause recurrences due to incomplete PVI. The directional sensitivity limitations of bipolar electrogram recordings and the unique anatomy of the LAA-LSPV ridge further contribute to the challenge of evaluating PVI. The HD Grid catheter's ability to record bipoles parallel and perpendicular to the catheter splines and its high-density mapping capabilities provide a superior means for identifying gaps in ablation and detecting the low-voltage isthmus. Furthermore, factors such as ablation quality, catheter stability, and thickness of the LAA-LSPV ridge influence the presence of fractionated signals and the success of PVI. Incorporating preprocedural imaging modalities, such as computed tomography or magnetic resonance imaging, and real-time intracardiac echocardiography could enhance the tailored approach to address these challenges. Future developments in the HD Grid technology, including the option for contact force measurement during mapping, may offer additional insights into the nature of these signals. This case series highlights the significance of using the HD Grid catheter for a detailed interrogation of the LAA-LSPV ridge, ultimately leading to more effective PVI and improved outcomes in patients with atrial fibrillation.

MetaV: A Pioneer in feature Augmented Meta-Learning Based Vision Transformer for Medical Image Classification.

Image classification, a fundamental task in computer vision, faces challenges concerning limited data handling, interpretability, improved feature representation, efficiency across diverse image types, and processing noisy data. Conventional architectural approaches have made insufficient progress in addressing these challenges, necessitating architectures capable of fine-grained classification, enhanced accuracy, and superior generalization. Among these, the vision transformer emerges as a noteworthy computer vision architecture. However, its reliance on substantial data for training poses a drawback due to its complexity and high data requirements. To surmount these challenges, this paper proposes an innovative approach, MetaV, integrating meta-learning into a vision transformer for medical image classification. N-way K-shot learning is employed to train the model, drawing inspiration from human learning mechanisms utilizing past knowledge. Additionally, deformational convolution and patch merging techniques are incorporated into the vision transformer model to mitigate complexity and overfitting while enhancing feature representation. Augmentation methods such as perturbation and Grid Mask are introduced to address the scarcity and noise in medical images, particularly for rare diseases. The proposed model is evaluated using diverse datasets including Break His, ISIC 2019, SIPaKMed, and STARE. The achieved performance accuracies of 89.89%, 87.33%, 94.55%, and 80.22% for Break His, ISIC 2019, SIPaKMed, and STARE, respectively, present evidence validating the superior performance of the proposed model in comparison to conventional models, setting a new benchmark for meta-vision image classification models.

A reduced vector model predictive controller for a three-level neutral point clamped inverter with common-mode voltage suppression.

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid.

Power Grids and Instrument Transformers up to 150 kHz: A Review of Literature and Standards.

The phenomenon of high-frequency distortion (HFD) in the electric grids, at both low-voltage (LV) and medium-voltage (MV) levels, is gaining increasing interest within the scientific and technical community due to its growing occurrence and the associated impact. These disturbances are mainly injected into the grid by new installed devices, essential for achieving decentralized generation based on renewable sources. In fact, these generation systems are connected to the grid through power converters, whose switching frequencies are significantly increasing, leading to a corresponding rise in the frequency of the injected disturbances. HFD represents a quite recent issue, but numerous scientific papers have been published in recent years on this topic. Furthermore, various international standards have also covered it, to provide guidance on instrumentation and related algorithms and indices for the measurement of these phenomena. When measuring HFD in MV grids, it is necessary to use instrument transformers (ITs) to scale voltages and currents to levels fitting with the input stages of power quality (PQ) instruments. In this respect, the recently released Edition 2 of the IEC 61869-1 standard extends the concept of the IT accuracy class up to 500 kHz; however, the IEC 61869 standard family provides guidelines on how to test ITs only at power frequency. This paper provides an extensive review of literature, standards, and the main outputs of European research projects focusing on HFD and ITs. This preliminary study of the state-of-the-art represents an essential starting point for defining significant waveforms to test ITs and, more generally, to achieve a comprehensive understanding of HFD. In this framework, this paper provides a summary of the most common ranges of amplitude and frequency variations of actual HFD found in real grids, the currently adopted measurement methods, and the normative open challenges to be addressed.

Optimization scheduling of microgrid comprehensive demand response load considering user satisfaction.

The original load control model of microgrid based on demand response lacks the factors of incentive demand response, the overall satisfaction of users is low, the degree of demand response is low, the Time Of Use (TOU) price of peak-valley filling capacity is weak, and the peak-valley difference of load curve is large. Regarding the limitations of the current microgrid demand response model, this study further optimizes the flexible load control strategy and proposes a two-objective optimization model based on price and incentive. Meanwhile, the model is solved using an improved chaotic particle group algorithm. Finally, the microgrid load data were selected for simulation analysis. The simulation results showed that the comprehensive demand response of flexible control model proposed increased the overall satisfaction of users by 9.51%, the overall operating cost of microgrid suppliers decreased by 12.975/ten thousand yuan, the peak valley difference decreased by 4.61%, and the user demand response increased by 27.24%. The model effectively improves the overall profit of the supply side of the microgrid, improves the user satisfaction, and maximizes the linkage benefits of the supply and demand of the micro grid. In addition, the model effectively reduces the phenomenon of distributed power supply in the microgrid, and realizes the supply and demand matching of the whole load in the microgrid.

Dark triad traits, study and power motives among medical students-A cross-sectional study at a German medical faculty.

Background: A good physician should be empathic and altruistic, among other qualities. Therefore, the levels of socially undesirable personality traits (Dark Triad) as well as implicit motives of achievement, affiliation and power (Multi-Motive Grid) among medical students as future physicians were analyzed at two different points in their medical training. Methods: This study includes 380 medical students in their first year and 217 in their third year in Germany. All participants completed the Dirty Dozen (DD) and Multi-Motive Grid (MMG) questionnaires at the end of two different classes as paper-and-pencil tests. Relevant differences of the Dark Triad traits between the medical students and reference sample and the two different cohorts, as well as their implicit motives, the associations of Dark Triad traits and MMG components and gender differences of the Dark Triad traits were calculated. Results: There were no significant group differences between year one and year three medical students in narcissism, psychopathy and Machiavellianism (Dark Triad). There were no significant differences between the medical students and reference sample except in psychopathy. Male students scored significantly higher in the Dark Triad traits than female students. In the MMG, first-year students scored significantly higher levels in Fear of Rejection, and lower levels in Hope of Success and Hope of Power than the third-year students. Some associations were found between narcissism and Machiavelliansim with Hope of Success, Hope of Power and Fear of power. Conclusions: Dark Triad traits already appear to exist before the commencement of medical studies. These traits do not differ significantly between the medical students and reference sample; only a few MMG components seem to differ at different stages of their studies. This lack of differences between the medical students and validation cohort indicates that tests based on (undesirable) personality traits are not suitable criteria for the admission selection of medical students.

Comparative sanitation data from high-frequency phone surveys across 3 countries.

With less than half of the world's urban population having safely managed sanitation due to the high cost and difficulty of building sewers and treatment plants, many rely on off-grid options like pit latrines and septic tanks, which are hard to empty and often lead to illegal waste dumping; this research focuses on container-based sanitation (CBS) as an emerging off-grid solution. Off-grid sanitation refers to waste management systems that operate independently of centralized infrastructure and CBS is a service providing toilets that collect human waste in sealable containers, which are regularly emptied and safely disposed of. These data relate to a project investigating CBS in Kenya, Peru, and South Africa, focusing on how different user groups access and utilize sanitation - contrasting CBS with other types. Participants, acting as citizen scientists, collected confidential data through a dedicated smartphone app designed by the authors and external contractors. This project aimed to explore the effective scaling, management, and regulation of off-grid sanitation systems, relevant to academics in urban planning, water and sanitation services, institutional capability, policy and governance, and those addressing inequality and poverty reduction. The 12-month data collection period offered participants small incentives for weekly engagement, in a micro payment for micro tasks approach. Participants were randomly selected, attended a training workshop, and (where needed) were given a smartphone which they could keep at the end of the project. We conducted weekly smartphone surveys in over 300 households across informal settlements. These surveys aimed to understand human-environment interactions by capturing daily life, wellbeing, income, infrastructural service use, and socioeconomic variables at a weekly resolution, contributing to more informed analyses and decision-making. The smartphone-based approach offers efficient, cost-effective, and flexible data collection, enabling extensive geographical coverage, broad subject areas, and frequent engagement. The Open Data Kit (ODK) tools were used to support data collection in the resource-constrained environment with limited or intermittent connectivity.

A grid-wise approach for accurate computation of Standardized Runoff Index (SRI).

The Standardized Runoff Index (SRI) is a major indicator for evaluating hydrological drought conditions, accomplished by comparing the current runoff data with retrospective runoff conditions of an area for the same period. This hydrological drought indicator facilitates the characterisation of runoff variations across diverse regions. This study introduces a refined methodology for accurate computation of SRI by employing a grid-wise approach. Distinct probability distributions were fitted to each grid within the study area, diverging from the conventional practice of using a single probability distribution for the entire basin or sub-basin. The research endeavours to assess the efficacy of the grid-wise approach in improving the representation of drought characteristics when compared to the traditional areal approach. A comparative analysis between the performances of SRI computed through grid-wise fitting (where the probability distribution dynamically adapts to each grid) and the areal fitting approach (employing a uniform distribution across all grids) was conducted within the Godavari Basin, India. The findings in this study underscore that the misrepresentation of extreme events is inevitable for large heterogeneous basins like Godavari when the traditional areal approach was employed for SRI computation. Consequently, the grid-wise fitting emerges as a more accurate method for computing the SRI, particularly in characterising extreme dry or wet events.

Energy harvesting from fuel cell bicycles for home DC grids using soft switched DC-DC converter.

Fuel cell vehicles (FCVs) are gaining significance due to their potential to reduce greenhouse gas emissions and dependence on fossil fuels. Their efficient fuel cell cycle makes them ideal for last-mile transportation, offering zero emissions and longer range compared to battery electric vehicles. Additionally, the generation of electricity through fuel cell stacks is becoming increasingly popular, providing a clean energy source for various applications. This paper focuses on utilizing the energy from fuel cycle bicycles when it's not in use and feeding it into the home DC grid. To achieve this, a dual-phase DC to DC converter is proposed to boost stack voltage and integrate with the 24 V DC home grid system. The converter design is simulated using the PSIM platform and tested in a hardware-in-the-loop (HIL) environment with real-time simulation capabilities.

Correction of scatter in CBCT with a new grid design.

OBJECTIVE: This study aims at developing a simple and rapid Compton scatter correction approach for cone-beam CT (CBCT) imaging. Approach: In this work, a new Compton scatter estimation model is established based on two distinct CBCT scans: one measures the full primary and scatter signals without anti-scatter grid (ASG), and the other measures a portion of primary and scatter signals with ASG. To accelerate the entire data acquisition speed, a half anti-scatter grid (h-ASG) that covers half of the full detector surface is proposed. As a result, the distribution of scattered X-ray photons could be estimated from a single CBCT scan. Physical phantom experiments are conducted to validate the performance of the newly proposed scatter correction approach. Main Results: Results demonstrate that the proposed half grid approach can quickly and precisely estimate the distribution of scattered X-ray photons from only one single CBCT scan, resulting in a significant reduction of shading artifacts. In addition, it is found that the h-ASG approach is less sensitive to the grid transmission fractions, grid ratio and object size, indicating a robust performance of the new method. Significance: In the future, the Compton scatter artifacts can be quickly corrected using a half grid in CBCT imaging.

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid.

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

Projection-driven optimization framework for risk assessment of "dual carbon" audit in power grid enterprises with q-rung orthopair fuzzy MAGDM.

Power grid enterprises are the backbone of promoting clean and low-carbon energy transformation, playing an important role in achieving carbon peak and carbon neutrality. It is very necessary to audit the implementation of the "dual carbon" work of power grid enterprises, in order to better implement the national "dual carbon" policy and serve the development of the national economy. The risk assessment of "dual carbon" audit in power grid enterprises is multiple-attribute group decision-making (MAGDM). In this study, in light with projection measure technique and bidirectional projection measure technique, four forms of projection measure technique with q-rung orthopair fuzzy sets (q-ROFSs) are conducted. Then, two weighed projection techniques are conducted to manage the MAGDM. Finally, a numerical example for risk assessment of "dual carbon" audit in power grid enterprises and comparative analysis is utilized to verify the developed techniques. The major contribution of this research is constructed: (1) entropy technique is implemented to determine the weight values in line with score number (SN) and accuracy number (AN); (2) two weighed projection techniques are implemented to put forward MAGDM with q-ROFSs; (3) the numerical example for risk assessment of "dual carbon" audit in power grid enterprises is implemented to show the two weighed projection techniques under q-ROFSs; and (4) comparative studies are constructed with existing techniques.

Overvaluation of weight and shape in obesity: a comparative study between people with and without binge eating disorder.

Introduction: The overvaluation of weight and shape is a diagnostic criterion in eating disorders, except in binge eating disorder (BED), where it has received less attention. This aspect is also not usually analyzed in people with overweight or obesity without an eating disorder. This research aims to identify the indicators of symptomatology, as well as those of self-construction and cognitive structure, that are associated with overvaluation in obesity, either alone or in conjunction with BED. Method: A sample of 102 overweight or obese participants was accessed. The sample was divided into four groups: one without overvaluation or BED (n = 33); a second with overvaluation and without BED (n = 21); a third with BED, but without overvaluation (n = 15), and a fourth with BED and overvaluation (n = 33). The groups completed instruments regarding eating symptomatology, anxiety, depression, and stress. In addition, they were administered the Repertory Grid Technique, a semi-structured interview to evaluate the cognitive structure involved in the construal of the self and others. Results: The factors of overvaluation and the presence of BED independently explained eating symptomatology, and the latter also showed a tendency to influence anxiety, depression, and stress. In terms of cognitive structure, weight polarization was explained by overvaluation, while BED was associated with a high presence of cognitive conflicts. In self-construction, BED was the factor that explained the differences, particularly in Self-Ideal discrepancy. Discussion: The results highlight the importance of overvaluation in obesity, even in the absence of BED. Its evaluation and treatment are recommended. Furthermore, in the case of BED, it is also advisable to evaluate the overvaluation of weight and shape since it can be a severity specifier.

"Mail-slot" Technique for Minimally Invasive Placement of Subdural Grid Electrodes: A Single-institution Experience.

BACKGROUND: In the management of multi-drug-resistant focal epilepsies, intracranial electrode implantation is used for precise localization of the ictal onset zone. In select patients, subdural grid electrode implantation is utilized. Subdural grid placement traditionally requires large craniotomies to visualize the cortex prior to mapping. However, smaller craniotomies may enable shorter operations and reduced risks. We aimed to compare surgical outcomes between patients undergoing traditional large craniotomies with those undergoing tailored "mini" craniotomies (the "mail-slot" technique) for subdural grid placement. METHODS: This retrospective cohort study included 23 patients who underwent subdural electrode implantation for epilepsy monitoring between 2014 and 2020. Patients were categorized into mini-craniotomies (n = 9) and traditional large craniotomies (n = 14) groups. Demographics, operative details, and outcomes were reviewed. Craniotomy size and number of electrodes were determined via post hoc radiographs. RESULTS: Of the 23 patients studied, the mini group had smaller craniotomy sizes (mean: 22.71 cm2 vs. 65.17 cm2, P < 0.001) and higher electrode-to-size ratios (mean: 4.25 vs. 1.71, P < 0.0001). The mini group had slightly fewer total electrodes (mean: 88.67 vs. 107.43, P = 0.047). No significant differences were found in operative duration, blood loss, invasive electroencephalography duration, complications, or Engel scores between the groups. One patient per group required further invasive epilepsy monitoring for localization; all patients underwent therapeutic surgery. CONCLUSIONS: Our findings suggest that mini-craniotomies for subdural grid placement in epilepsy monitoring offer significant advantages, including smaller craniotomy sizes and shorter operation durations, without compromising safety or efficacy. These results support the trend towards minimally invasive, patient-tailored surgical approaches in epilepsy treatment.

Deep learning-driven hybrid model for short-term load forecasting and smart grid information management.

Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load demand. The study introduces a hybrid method that combines multiple deep learning models, the Gated Recurrent Unit (GRU) is employed to capture long-term dependencies in time series data, while the Temporal Convolutional Network (TCN) efficiently learns patterns and features in load data. Additionally, the attention mechanism is incorporated to automatically focus on the input components most relevant to the load prediction task, further enhancing model performance. According to the experimental evaluation conducted on four public datasets, including GEFCom2014, the proposed algorithm outperforms the baseline models on various metrics such as prediction accuracy, efficiency, and stability. Notably, on the GEFCom2014 dataset, FLOP is reduced by over 48.8%, inference time is shortened by more than 46.7%, and MAPE is improved by 39%. The proposed method significantly enhances the reliability, stability, and cost-effectiveness of smart grids, which facilitates risk assessment optimization and operational planning under the context of information management for smart grid systems.

A grid-forming approach utilizing DC bus dynamics for low inertia power systems with HVDC applications.

While power electronic converters, such as voltage source converters (VSCs), are crucial for the operation of converter-dominated renewables and their integration with the electricity grid, their reliance on VSCs can pose a challenge. The limited inertia of these sources can lead to a deterioration of the rate of change of frequency, potentially causing power system stability issues. A grid-forming approach utilizing dc-link dynamics is one of the attractive alternatives to achieve grid synchronization and support grid frequency. Existing grid-forming control schemes, which assume a constant or virtually constant dc source, rely on a fixed physical dc-link capacitor. Nonetheless, the inertia support from such a capacitor is brief, owing to its limited energy storage capability. Consequently, enhancing inertia becomes imperative; otherwise, it may result in an increased rate of change of voltage on the dc side, potentially leading to issues with protection, undesirable interactions, and system instability. This paper proposes a new grid-forming control strategy that considers a virtual capacitor to achieve grid synchronization while simultaneously providing the network with inertia response services during power imbalances. Moreover, including a virtual resistor in the controller effectively attenuates power and dc voltage oscillations. Simulations using Simulink and small signal stability analysis are conducted to validate the efficacy of the proposed controller.

A Novel Approach for Temperature-Induced Ball Grid Array Collapse Observation.

This study presents a new approach to investigating the impact of repeated reflow on the failure of ball grid array (BGA) packages. The issue with the BGA package collapse is that the repeated reflow can lead to short circuits, particularly for BGAs with a very fine pitch between leads. A novel approach was developed to measure the collapse of BGA solder balls during the melting and solidification process, enabling in situ measurements. The study focused on two types of solders: Sn63Pb37 as a reference, and the commonly used SAC305, with measurements taken at various temperatures. The BGA samples were subjected to three different heating/cooling cycles in a thermomechanical analyzer (TMA) at temperatures of 250 °C, 280 °C, and 300 °C, with a subsequent cooling down to 100 °C. The results obtained from the TMA indicated differences in the collapse behavior of both BGA solder alloys at various temperatures. Short circuits between neighboring leads (later confirmed by an X-ray analysis) were also recognizable on the TMA. The novel approach was successfully developed and applied, yielding clear insights into the behavior of solder balls during repeated reflow.

Depth-Guided Bilateral Grid Feature Fusion Network for Dehazing.

In adverse foggy weather conditions, images captured are adversely affected by natural environmental factors, resulting in reduced image contrast and diminished visibility. Traditional image dehazing methods typically rely on prior knowledge, but their efficacy diminishes in practical, complex environments. Deep learning methods have shown promise in single-image dehazing tasks, but often struggle to fully leverage depth and edge information, leading to blurred edges and incomplete dehazing effects. To address these challenges, this paper proposes a deep-guided bilateral grid feature fusion dehazing network. This network extracts depth information through a dedicated module, derives bilateral grid features via Unet, employs depth information to guide the sampling of bilateral grid features, reconstructs features using a dedicated module, and finally estimates dehazed images through two layers of convolutional layers and residual connections with the original images. The experimental results demonstrate the effectiveness of the proposed method on public datasets, successfully removing fog while preserving image details.

Nonlinear robust sliding mode - Backstepping hybrid control for WECS -theoretical design and experimental evaluation.

This paper proposes a new contribution in the field of optimizing control techniques for wind systems to enhance the quality of the energy produced in the grid. Although the Sliding Mode control technique, whether classical or involving the use of artificial intelligence, has shown interesting results, its main drawback lies in the oscillation phenomenon commonly referred to as "chattering." This phenomenon affects the accuracy and robustness of the system, as well as the parametric variation of the system. In this work, we propose a solution that combines two nonlinear techniques based on the Lyapunov theorem to eliminate the chattering phenomenon. It is a hybrid approach between the Backstepping strategy and the Sliding Mode, aiming to control the active and reactive powers of the doubly fed induction generator (DFIG) connected to the electrical grid by two converters (grid side and machine side). This hybrid technique aims to improve the performance of the wind system in terms of precision errors, stability, as well as active and reactive power. The proposed solution has been validated in the Matlab & Simulink environment to assess the performance and robustness of the proposed model, as well as experimentally validated on a test bench using the DSPACE 1104 card. The obtained results are then compared with other techniques, demonstrating a significant improvement in performance.

Modeling the grid cell activity based on cognitive space transformation.

The grid cells in the medial entorhinal cortex are widely recognized as a critical component of spatial cognition within the entorhinal-hippocampal neuronal circuits. To account for the hexagonal patterns, several computational models have been proposed. However, there is still considerable debate regarding the interaction between grid cells and place cells. In response, we have developed a novel grid-cell computational model based on cognitive space transformation, which established a theoretical framework of the interaction between place cells and grid cells for encoding and transforming positions between the local frame and global frame. Our model not only can generate the firing patterns of the grid cells but also reproduces the biological experiment results about the grid-cell global representation of connected environments and supports the conjecture about the underlying reason. Moreover, our model provides new insights into how grid cells and place cells integrate external and self-motion cues.