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Lateral ankle sprain (LAS) is one of the most common types of injury in professional football (soccer) players with high risk of recurrence. The rehabilitation after LAS in professional football players is often still time-based and relies on anecdotal experience of clinicans. There is still a lack of utilization of criteria-based rehabilitation concepts after LAS in professional football. The aims of this clinical commentary are (1) to critically discuss the need for criteria-based rehabilitation concepts after LAS in professional football players, (2) to highlight the current lack of these approaches and (3) to present a novel clinical guideline-based rehabilitation algorithm. Short time-loss (15 days) and high recurrence rate (17%) raise the question of trivialization of LAS in professional football. Despite consequences for many stakeholders involved (players, teams, clubs, insurers), there is still a lack of of criteria-based, step-by-step approaches. The use of a criteria-based rehabilitation approach might reduce the high recurrence rate after LAS in professional football players and will lead, in turn, to increased long-term player availability. Practical experiences of he authors demonstrate the feasibility of such an approach. The effectiveness of this novel rehabilitation algorithm remains to be evaluated in future studies. Level of Evidence: 5 INTERNATIONAL JOURNAL OT SPOKR PHYSICAL TOEBAfY [ABSTRACT FROM AUTHOR]

In the application of interval type-2 (IT2) Takagi–Sugeno–Kang (TSK) fuzzy logic systems (FLSs), the center-of-sets (COS) type-reduction (TR) is more advantageous than the centroid TR. This paper proposes three types of discrete non-iterative algorithms to solve the problem of COS TR in IT2 TSK FLSs. Multiple simulation experiments are carried out for the IT2 TSK FLSs with different fuzzy rule numbers. Experimental results show that the computational efficiencies of the three discrete non-iterative algorithms are better than that of Karnik–Mendel (KM) algorithms, which provides latent value for the application of type-2 FLSs. [ABSTRACT FROM AUTHOR]

Concept-cognitive learning (CCL) has been an active topic in artificial intelligence and cognitive computing. Two-way concept-cognitive learning (TCCL), an efficient cognitive mechanism to discover knowledge from an arbitrary information granule, has attracted attention to the problems of concept learning. Although TCCL has been widely adopted for cognitive and learning concepts, the existing studies still have some issues: learning concepts can only from the standard formal context, i.e., extent with discrete information, and ignoring the fuzziness of object information that is also important to precise cognitive and learning concepts. Hence, this paper proposes a novel L-fuzzy concept learning method via two-way concept-cognitive learning and fuzzy residual implication, namely, L-fuzzy concept-cognitive learning (LF-CCL). Specifically, we give the theoretical underpinnings of the L-fuzzy CCL method and a pair of dual cognitive operators are presented based on residual implication. Then L-fuzzy two-way granule spaces are constructed to explore the transformation of information granules and the generation of knowledge granules from arbitrary cognitive clues. Meanwhile, we analyze the CCL progress using L-fuzzy operators based on fixed points. Moreover, the corresponding algorithms are designed, and the decision value of this method in financial investment applications is explored through experimental analysis. Finally, numerical experiments verify the effectiveness and flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative reconstruction. Methods This retrospective study included 16 patients (mean age: 54.2 ± 22.1 years; 8 men and 8 women) with acute hemorrhage who underwent contrast-enhanced CT. Unenhanced axial, arterial phase axial, arterial phase coronal, and delayed phase axial images were reconstructed with deep learning reconstruction, hybrid iterative reconstruction, and filtered back projection, which was used as a control in qualitative analyses. Circular and line regions of interest were placed on the aorta and superior mesenteric artery (SMA), respectively, in quantitative analyses. Using a blind process, 2 radiologists independently evaluated image noise, depiction of arteries, and suitability for the evaluation of hemorrhage in qualitative image analyses. Results Image noise in deep learning reconstruction was significantly reduced compared with hybrid iterative reconstruction in the quantitative (P < .001) and qualitative analyses (Reader 1, P ≤ .001 for all series; Reader 2, P = .002, .001, and < .001). The slope at the half maximum in deep learning reconstruction (123.8 ± 63.2 HU/mm) significantly improved compared with hybrid iterative reconstruction (105.3 ± 51.0 HU/mm) in the CT attenuation profile of the SMA (P < .001). Qualitative analyses revealed a significantly improved depiction of arteries (Reader 1, P < .001 for all series; Reader 2, P = .037, .008, and < .001) and suitability for evaluating acute hemorrhage in the arterial phase image (Reader 1, P < .001 for both series; Reader 2, P = .041 and .004) with deep learning reconstruction compared with hybrid iterative reconstruction. Discussion Deep learning reconstruction provided images with a significantly better depiction of arteries and more suitable quality arterial phase images for the evaluation of abdominopelvic hemorrhage compared with hybrid iterative reconstruction. Conclusion Deep learning reconstruction is better for reconstructing abdominopelvic contrast-enhanced CT images when evaluating hemorrhages; however, a prospective study including a large number of patients is needed to strengthen the findings of this study. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality for China, the transmission mechanism, path, diffusion effect,and allocation of Emission Reduction Targets (ERT) among industries must be investigated. Setting short-term ERT will create a reverse pressure mechanism for the industry's carbon reduction, ensuring the achievement of carbon emission control target. However, few studies have explored how to influence the entire system by controlling key industries from the perspective of network control. Therefore, this study investigates Inter-industry Carbon Emission Transfer (ICET) in China from 1997 to 2017,and found that ICET can achieve fair distribution of ERT. Inspired by the controllability theory of complex networks to integrate the driving factors, the transmission paths, diffusion effects, and allocation of ERT into a unified analytical framework. Then, breaking the traditional top-down hierarchical decomposition mechanism and transforming it into a bottom-up target forcing mechanism, a decomposition algorithm for the total control target was proposed.The results reveal that ICET accounts for the majority of carbon emission. The characteristics of network structure have a significant impact on the ICET.Different industries have varying control capabilities. Compared with industry-wide control, target control on the Minimum Control Industry Set (MCMCS) has a slightly worse emission reduction effect, but the economic loss is smaller, making it an effective policy choice. [ABSTRACT FROM AUTHOR]

The predicted Bridge Condition Index (BCI) is a guiding indicator for formulating preventive maintenance strategies for bridges and is hardly available even if using the most advanced neural network prediction model, owing to the few measured BCIs and neural network model defects. In this paper, an improved Back-Propagation Neural Network (BPNN) model named CPSO-BP-MC is proposed to predict BCI, which has a BPNN as its core, combined with the Coordinated Particle Swarm Optimization (CPSO) algorithm aims to faster convergence and avoid local optimal solution even with less training sample, and the Markov Chain (MC) algorithm aims to further revise the fluctuation of CPSO-BP prediction output due to maintenance and reinforcement. A case study is presented to demonstrate the efficiency of CPSO-BP-MC predicting BCI with fewer training samples. Comparing the predicted BCI of CPSO-BP-MC with the measured BCI and other models, the results show that CPSO-BP-MC converges faster than other models, and can predict the BCI more stably and accurately, and the error between prediction and measured BCI is less than 3 %. [ABSTRACT FROM AUTHOR]

The spatial and spectral features of hyperspectral images exhibit complementarity, and neglecting them prevents the full exploitation of useful information for superresolution. This article proposes a spatialspectral middle cross-attention fusion network to explore the spatialspectral structure correlation. Initially, we learn spatial and spectral features through spatial and spectral branches instead of single ones to reduce information compression. Then, a novel middle-cross attention fusion block that includes middle features fusion strategy and crossattention is proposed to fuse spatial-spectral features to enhance their mutual effects, which aims to explore the spatial-spectral structural correlations. Finally, we propose a spectral feature compensation mechanism to provide complementary information for adjacent band groups. The experimental results show that the proposed method outperforms state-of-the-art algorithms in object values and visual quality. [ABSTRACT FROM AUTHOR]

In recent years, synthetic aperture radar (SAR) vehicle detection has become a research hotspot. However, algorithms using horizontal bounding boxes can lead to redundant detection areas due to the varying aspect ratio and arbitrary orientation of vehicle targets. This paper proposes a morphology-based feature extraction network (MFE-Net), which fully uses the prior shape knowledge of the vehicle targets. Specifically, we adopt rotatable bounding boxes to predict the targets, and a novel rectangular rotation-invariant coordinate convolution (RRICC) is proposed to extract the feature, which can determine more accurately the convolutional sampling location of the vehicles. The adaptive thresholding denoising module (ATDM) is designed to suppress background clutter. Furthermore, inspired by the convolutional neural networks (CNNs) and selfattention, we propose the hybrid representation enhancement module (HREM) to highlight the vehicle target features. The experiment results show that the proposed model obtains an average precision (AP) of 93.1% on the SAR vehicle detection data set (SVDD). [ABSTRACT FROM AUTHOR]

We introduce a data-driven framework to automatically identify interpretable and physically meaningful hyperelastic constitutive models from sparse data. Leveraging symbolic regression, our approach generates elegant hyperelastic models that achieve accurate data fitting with parsimonious mathematic formulas, while strictly adhering to hyperelasticity constraints such as polyconvexity/ellipticity. Our investigation spans three distinct hyperelastic models—invariant-based, principal stretch-based, and normal strain-based—and highlights the versatility of symbolic regression. We validate our new approach using synthetic data from five classic hyperelastic models and experimental data from the human brain cortex to demonstrate algorithmic efficacy. Our results suggest that our symbolic regression algorithms robustly discover accurate models with succinct mathematic expressions in invariant-based, stretch-based, and strain-based scenarios. Strikingly, the strain-based model exhibits superior accuracy, while both stretch-based and strain-based models effectively capture the nonlinearity and tension-compression asymmetry inherent to the human brain tissue. Polyconvexity/ellipticity assessment affirm the rigorous adherence to convexity requirements both within and beyond the training regime. However, the stretch-based models raise concerns regarding potential convexity loss under large deformations. The evaluation of predictive capabilities demonstrates remarkable interpolation capabilities for all three models and acceptable extrapolation performance for stretch-based and strain-based models. Finally, robustness tests on noise-embedded data underscore the reliability of our symbolic regression algorithms. Our study confirms the applicability and accuracy of symbolic regression in the automated discovery of isotropic hyperelastic models for the human brain and gives rise to a wide variety of applications in other soft matter systems. Our research introduces a pioneering data-driven framework that revolutionizes the automated identification of hyperelastic constitutive models, particularly in the context of soft matter systems such as the human brain. By harnessing the power of symbolic regression, we have unlocked the ability to distill intricate physical phenomena into elegant and interpretable mathematical expressions. Our approach not only ensures accurate fitting to sparse data but also upholds crucial hyperelasticity constraints, including polyconvexity, essential for maintaining physical relevance. [Display omitted] [ABSTRACT FROM AUTHOR]

Electric bicycle datasets for lift control have played a crucial role in the development of electric bicycle lift entry detection algorithms. However, existing datasets often encounter issues, including a small dataset size, a high false detection rate, and the absence of a publicly available benchmark dataset. This paper addresses these challenges by constructing a new electric bicycle detection dataset for lift control based on the research demands of the electric bicycle detection task for lift control. XHNet_EB, a car scene image dataset covering various types of electric bicycles, was constructed based on images taken by a camera from above. Segmented annotation was employed, framing the front and rear of the electric bicycle independently. This approach effectively addressed the problems of many irrelevant features and a high false detection rate caused by the use of whole-electric-bicycle annotations in mainstream datasets. AP_50, AP_75, the mAP, and the number of false detections in the images were used for quantitative analysis of the dataset. The experimental results indicated that the object detection accuracy of the XHNet_EB dataset was excellent. Quantitative analysis and evaluation of the number of false detections in images were conducted using four mainstream lightweight detection model algorithms. The results demonstrated that segmented annotation reduced the false detection rate more effectively than entire electric bicycle annotation. This study identified drawbacks in existing datasets. The dataset proposed in this paper overcomes the shortcomings of existing commercial data and solves problems such as the high false detection rate caused by the inclusion of many irrelevant features caused by the "whole-electric-bicycle annotation" method, which could help with the development of electric bicycle detection applications in lifts. [ABSTRACT FROM AUTHOR]

Dynamic obstacle avoidance is crucial in autonomous driving, ensuring vehicle safety by preventing collisions and enhancing driving efficiency. Dynamic obstacle avoidance algorithms have made significant progress due to deep learning. However, video-based target detection methods can suffer from missed or false detections when processing consecutive frames, especially for high-speed moving targets or in complex dynamic scenes. Multi-target tracking methods require intricate algorithm designs for target initialization and occluded object recovery, which can be compromised by tracker performance, leading to unstable tracking or target loss. To address the issue of target loss in multi-target tracking, we designed the novel YTCN model that infuses time series information through temporal convolution and enhances the sensitivity of the receptive field with Spatial Pyramid Pooling, Feature Concatenate and Spatial Convolution(SPPFCSPC), enhancing the model's feature extraction capability. Simultaneously, we developed the novel Global Attention Mechanism(GAM) and Double Attention(DA) mechanisms that merge channel and spatial features to enhance feature representation. Finally we design the Temporal Residual Block(TRB) to model the temporal obstacles. Experimental results show that our method achieves 82.4 % of mAP_0.5 on the BDD 100 K dataset, which gets 1.3 % higher than the previous methods. [ABSTRACT FROM AUTHOR]

Achieving a balance between convergence and diversity and their mutual enhancement is a complex task in the process of algorithm improvement. This is crucial because it is directly related to the effectiveness of the algorithm in obtaining accurate and uniformly distributed Pareto frontiers. Although significant progress has been made in particle swarm algorithms, exploring new approaches is necessary. In this paper, we construct a projection plane (projection line in 2D) based on the extreme values of the non-dominated solutions, select a set of uniform reference points on the projection plane, and then project the non-dominated solutions onto the constructed projection plane to form projection points. The reference points and projection points on the projection plane are thus utilized to guide the updating of the population as well as the maintenance of the external archive, a strategy that enhances the algorithm's global exploration and local exploitation capabilities. Secondly, we aggregate the target values of particles into a single scalar value and combine the idea of particle fusion to design a scheme for the particle selection of individual optimal particles. This paper further improves the algorithm's overall performance by using the information between populations to select individual optimal particles. Lastly, it is evaluated against a number of multi-objective algorithms that are currently in use and perform well on 22 test problems. The findings demonstrate that the algorithm this paper proposes performs better when solving multi-objective problems. [ABSTRACT FROM AUTHOR]

Background: Online content can inform the personal risk of developing an eating disorder, and it can influence the time and motivation to seek treatment. Patients routinely seek information online, and access to information is crucial for both prevention and treatment. The primary aim of the current study was to quantify the readability scores of online content on eating disorders using natural language processing algorithms, across two languages: English and Italian. Methods: Unique terms related to single diagnoses were searched using Google®. The content available on Wikipedia was also assessed. Readability was defined according to the Flesch Readability Ease (FRE) and the Rate Readability Index (RIX). The scientific support of retrieved content and the authoritativeness of sources were measured through standardized variables. Results: In Italian, online content was more likely published by private psychotherapy institutes or by websites that promote diet-advice or weight-loss. In both languages, the most readable content was on Anorexia Nervosa (RIX 4.18, FRE-en 59.6, FRE-it 41.69), Bulimia Nervosa (RIX 3.99, FRE-en 66.27, FRE-it 39.66) or Binge Eating (RIX 4.01, FRE-en 68.10, FRE-it 38.62). English sources consistently had more references than Italian pages (range 35–182, vs 1–163, respectively). and had a higher percentage of citations available in the target language. The content of these references was mainly reflective of peer-reviewed or clinical manuals. Conclusion: Attention should be given to developing online content for Muscle Dysmorphia and Orthorexia Nervosa, as well as improving the overall readability of online content on eating disorders, especially for languages other than English. [ABSTRACT FROM AUTHOR]

This study proposes a facial expression recognition model based on anomaly features and multispectral imaging (AF-MSI) to extract spectral, spatial, anomalous, and temporal features for recognizing facial expressions. To ensure that the extracted anomaly is consistent with the characteristics of a human face and expression, the background of the face required by anomaly extraction is reconstructed using the spatial characteristics and facial spectral signal correlation. Then, improved spectral correlation is employed to reconstruct a sparse coding model based on spectral images, thereby constructing a sparse dictionary. A temporal anomaly feature signal is divided into short intervals with a fixed number of frames. Further, the descriptor and correlation of the short-term sequence features are obtained by the sparse dictionary's base vector. The characteristic signals with good correlation are selected as representative spectral-spatial-anomaly-temporal features for subsequent training and learning. The proposed model is verified experimentally on different datasets and compared with the state-of-the-art methods. The experimental results demonstrate that the proposed facial expression recognition model can achieve an accuracy of over 86% and has a significant advantage over comparable algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem of traditional methods which lead to low detection accuracy owing to limited sample size and imbalanced sample, this paper proposes a novel insulator self-explosion detection algorithm based on mixed samples and transfer learning. Firstly, the segmentation of insulator and background region is realized by using template matching and grabcut algorithm. Secondly, the segmented insulator image is merged with the new background image at pixel level to generate the insulator artificial sample with distinct texture. Thirdly, this paper introduces an improved Faster R-CNN model that utilizes ResNet50 for feature extraction, replacing the traditional VGG16 network. This modification significantly improves the ability of the network to extract insulator texture features. By introducing the focal loss function to address the imbalance between positive and negative samples, the detection accuracy of the network is further improved. Finally, the introduction of depthwise separable convolution helps reduce model complexity, resulting in a more lightweight architecture. The model is trained by a dataset that includes both synthetic and real samples. The real insulators are then employed for evaluation. The experimental results show that even with a reduced number of artificial samples, the algorithm proposed in this study achieves a mean average precision of 95.26%. Additionally, the average detection time is 2.31s. These results show that compared with the original Faster R-CNN method, the proposed method has improved the detection accuracy and speed. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) path planning algorithm often suffers from low population diversity and rapid early convergence, which can lead to the algorithm easily falling into local optima and affecting its stability. This paper proposes an improved PSO algorithm with two key enhancements: (1) the introduction of a particle reactivation module during the iteration process, and (2) the incorporation of the Simulated Annealing (SA) concept during the global optimal solution update phase. These improvements enhance the diversity of the particle population, slow down the early convergence rate of the algorithm, reduce the probability of the algorithm getting trapped in local optima, and increase its overall stability. Experiments were conducted to compare the proposed algorithm with the standard PSO algorithm, genetic algorithms, and other improved PSO path planning algorithms. The results indicate that the improved algorithm shows superior performance in both average path length and algorithm stability. [ABSTRACT FROM AUTHOR]

In order to have shorter detection time and higher detection accuracy for target detection, this article mainly improves the YOLOv5 algorithm from optimizing active function and the loss function. The results of comparative experiments shown that our improved YOLOv5 algorithm's performance for detecting object was greatly improved. [ABSTRACT FROM AUTHOR]

Introduction: The novel Confirm Rx™ implantable cardiac monitor (ICM) with SharpSense™ technology incorporates a new P‐wave discriminator designed to improve AF detection. This study aimed to evaluate the diagnostic performance of the Confirm Rx™ ICM in detecting AF episodes of varying durations. Methods: We conducted a multicenter retrospective analysis of consecutive patients implanted with a Confirm Rx™ ICM (v1.2) across nine UK hospitals, all with documented AF lasting at least 6 min. Electrocardiograms (ECGs) were manually adjudicated by cardiologists. To account for intra‐ and inter‐reviewer variability, a random sample of 10% of ECGs underwent additional review. Disagreements were resolved by a third reviewer. Diagnostic performance was determined by calculating the gross and patient‐averaged positive predictive value (PPV) for AF episodes of different duration. The source of false positive (FP) detection was also categorized. Results: Overall, 16,230 individual ECGs from 232 patients were included. The median AF episode duration was 14 min. R‐wave amplitude remained stable during follow‐up (0.52 ± 0.27 mV [initial] vs. 0.54 ± 0.29 mV [end of follow‐up], p =.10). The gross and patient‐averaged PPV were 75.0% and 67.0%, respectively. Diagnostic performance (gross) increased with progressively longer AF episodes: 88.0% for ≥1 h, 97.3% for 6 h, and 100% for 24 h. The main source of FP during tachycardia was T‐wave oversensing (54.2%), while in non‐tachycardic episodes it was predominantly ectopy (71.2%). The AF burden precision was excellent (93.3%). Conclusion: The Confirm Rx™ ICM diagnostic performance was modest for all AF episodes (75%), with accuracy increasing for longer AF episodes. [ABSTRACT FROM AUTHOR]

We studied a variant of the vehicle routing problem (VRP) arising in last-mile distribution, called the multi-depot two-echelon vehicle routing problem with delivery options (MDTEVRP-DO). The MDTEVRP-DO involves two decision levels: (i) designing routes for a fleet of vehicles located in multiple depots to transport customer demands to a set of satellites and (ii) routing a fleet of vehicles from the satellites to serve the final customers. Nowadays, a relevant feature of the problem characterizing delivery services is that customers can collect their packages at pickup stations near their homes or workplaces. We designed an effectively simulated annealing (SA) heuristic for the problem. The new algorithm was extensively tested on benchmark instances from the literature, and its results were compared with those of start-of-the-art algorithms. The results show that the proposed SA obtains 30 out of the 36 best solutions for the MDTEVRP-DO benchmark instances. Moreover, its computation performance is superior to state-of-the-art algorithms for the MDTEVRP-DO. [ABSTRACT FROM AUTHOR]

Nursing has always been a cost for the institutions within which nurses work. This fact has influenced almost everything that affects how nurses are utilized and valued. As a cost, nurses are closely managed on the margin, always constrained by the resource machinations of organizations and systems with little determination or enumeration of the contributions nurses make to the service and financial value represented by algorithms and metrics that enumerate and demonstrate nurse's impact and contribution to service and financial value. This article further pushes the boundaries of this circumstance, challenging nurse and health leaders to reconceptualize nursing contribution and recalibrate the determination and calculation of nursing value as a sustainable baseline for nursing leadership for the future. [ABSTRACT FROM AUTHOR]

On 6 February 2023, a devastating earthquake doublet consisting of MW 7.8 and 7.6 events separated by about 9 hr struck the southeastern part of Türkiye. The developing aftershock sequence contained thousands of events during the first few days and overwhelmed the routine algorithms handling their detection and location. In addition, several stations temporarily lost real-time contact and came online again later. At the same time the Omori decay of the aftershock event rate reduced the event frequency and allowed for inclusion of progressively smaller-magnitude events with time. One possibility to help deal with such a complex situation is the use of machine learning (ML) methods to generate earthquake catalogs with a substantially higher number of events. Here, we present high-resolution earthquake catalogs derived with two ML association methods for the first five days of the aftershock sequence of this doublet. In terms of the number of reliably located events, the event catalog created from PhaseNet picks and the GENIE phase association method outperforms both the routine regional catalog and the second ML-derived catalog obtained from the GaMMA phase association method. Although both GaMMA and GENIE catalogs detect about 6 times more events than the routine catalog, GENIE associates on average about double the phases to a single event than GaMMA, which results in better constrained event locations. The spatiotemporal evolution of the event rates is sensitive to changes in the network geometry due to variable station availability. During the first few days, no decay of the event rate in the enhanced catalog is observed due to the inclusion of progressively smaller-magnitude events with time and increased station availability. This study indicates that ML-derived earthquake catalogs for challenging time periods like the early aftershock sequences of large earthquakes have the potential to significantly improve routine event catalogs. [ABSTRACT FROM AUTHOR]

The objective functions adopted by earthquake early warning (EEW) location algorithms are inadequate for out-of-network earthquakes. As a result, the real-time locations of these earthquakes are often erroneous. The consequences of mislocating out-of-network earthquakes are that their magnitudes are miscalculated, and the loci of their shaking predictions map are shifted. Given that the largest earthquakes occur in subduction settings, improving real-time out-of-network earthquake location is of great importance. In this study, the Virtual Dynamically Assembled Array Algorithm (ViDA³) is introduced, which addresses the location issue of offshore and off-network earthquakes. The guiding principle underlying the new EEW location algorithm is that standard seismic networks may be viewed as a collection of medium-sized seismic arrays, with each array consisting of three or more network stations. The potential of array seismology for EEW against out-of-network earthquakes stems primarily from the slowness vector, which points at the direction of the epicentral region. Thus, this region may be constrained merely by intersecting two or more such vectors. In addition, the length of the slowness vector depends on the hypocentral distance and depth and is thus vital for addressing an acute problem in a subduction setting--discriminating between upper crust and deep slab earthquakes. Furthermore, when the slowness of the P phase is known, the slowness of the S phase is deduced, and the S-phase arrival is searched for using the shift-and-sum practice. What makes ViDA³ so attractive is that, in locations where a real-time network is already in place, these added values may be achieved without extra hardware or substantial budget requirements. We present the result of ViDA³ real-time operation on a shallow earthquake offshore Vancouver Island and the result of its replay on a deep slab earthquake in northern Chile. performance is further assessed using a dataset of seismograms from the Mendocino Triple Junction area. It is concluded that ViDA³ location scheme outperforms currently available EEW location algorithms for out-of-network earthquakes. [ABSTRACT FROM AUTHOR]

Edge bundling is a technique used to improve the readability of large graph drawings by grouping edges to reduce visual complexity. This paper treats this task as a clustering problem, using compatibility metrics to evaluate solutions in an optimization pipeline combined with a clustering ensemble approach. The aim is to present the Clustering Ensemble-based Edge Bundling (CEBEB) method for solving the General-based Edge Bundling (GBEB) problem and report results for some given graphs. The CEBEB method proved very promising and generated better solutions than an existing evolutionary algorithm. Additionally, the paper introduces a new ensemble algorithm, specific for the GBEB, and reviews some previous results. [ABSTRACT FROM AUTHOR]

User identification is an essential technical support for downstream tasks such as recommendation systems, information retrieval, and collaborative filtering. Computing the similarity between user display names through classifiers is an effective solution for user identification across social networks. However, there are two problems with existing methods. Applying expert domain knowledge to extract handcrafted features of display names ignores the semantic information, resulting in poor performance of these methods. Selecting influential display names, and handcrafted features in user identification problems are also one of the difficulties. To solve these two problems, we propose a method based on the multi-feature fusion of display names using gated units. First, we extract the deep semantic features of display names through the BERT pre-trained multi-language model. Then, the gated mechanism is applied to select the handcrafted features we extracted to retain the essential features. Then, the adaptive factors are used to fuse handcrafted features and deep features to obtain user identification results across social networks. Finally, the efficiency of our model is verified on three constructed real-world multilingual display names datasets across multiple online social networks and compared with existing state-of-the-art methods. Experimental results show that the proposed algorithm outperforms the compared algorithms. [ABSTRACT FROM AUTHOR]

A fuzzy coefficient q -axis current increment flux weakening (FCCIFW) control method is proposed in interior permanent magnet synchronous motor (IPMSM) drives. The proposed FCCIFW not only simplifies the calculation of flux weakening coefficient according to the derived formula, but also avoids the saturation of current regulator. In FCCIFW, the theoretical calculation formula of conversion coefficient is derived firstly, and then the fuzzy rule base is established according to the formula. FCCIFW not only refrains the use of complex flux weakening calculation formula or fixed control parameters, but also refrains the problem of out-of-control in the process of deep flux weakening control, so as to improve the overall performance in the process of flux weakening. The results show that the proposed method has an effective and correct calculation process and results, Compared with different control methods in the operation process, it shows that the proposed method has more stable control effect, which has great application value in engineering. • This method adjusts the coefficients and simplifies the flux weakening process. [ABSTRACT FROM AUTHOR]

Purpose: Algorithmic and computational thinking are necessary skills for designers in an increasingly digital world. Parametric design, a method to construct designs based on algorithmic logic and rules, has become widely used in architecture practice and incorporated in the curricula of architecture schools. However, there are few studies proposing strategies for teaching parametric design into architecture students, tackling software literacy while promoting the development of algorithmic thinking. Design/methodology/approach: A descriptive study and a prescriptive study are conducted. The descriptive study reviews the literature on parametric design education. The prescriptive study is centered on proposing the incomplete recipe as instructional material and a new approach to teaching parametric design. Findings: The literature on parametric design education has mostly focused on curricular discussions, descriptions of case studies or studio-long approaches; day-to-day instructional methods, however, are rarely discussed. A pedagogical strategy to teach parametric design is introduced: the incomplete recipe. The instructional method proposed provides students with incomplete recipes for parametric scripts that are increasingly pared down as the students become expert users. Originality/value: The article contributes to the existing literature by proposing the incomplete recipe as a strategy for teaching parametric design. The recipe as a pedagogical tool provides a means for both software skill acquisition and the development of algorithmic thinking. [ABSTRACT FROM AUTHOR]

Background: The presence of lymphovascular invasion (LVI) in cases with breast cancer is considered a bad prognostic sign. The purpose of this study is to compare the efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) versus unenhanced magnetic resonance imaging (UE-MRI + DBT) in predicting LVI in women with pathologically confirmed breast cancer. Methods: This prospective self-controlled study enrolled a total of 70 cases of pathologically proven breast cancer. All the patients underwent tomosynthesis, non-contrast, and post-contrast MRI. Depending on the broken halo sign seen in tomosynthesis, peritumoral edema, dark rim diffusion at diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) values evaluated in MRI. Results: The accuracy of LVI detection by tomosynthesis was 58%; unenhanced and enhanced MRI had the same results at 60%. The accuracy of detecting LVI was raised to 64% by combining the tomosynthesis results with unenhanced MRI. Conclusions: Tomosynthesis parameters are promising tools in detecting LVI in breast cancer with better diagnostic accuracy in combination with unenhanced MRI. [ABSTRACT FROM AUTHOR]

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Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we propose a novel approach to enhance the security and efficiency of Vehicular Ad-Hoc Networks (VANETs) by integrating user authentication and cluster-based routing. The proposed method is divided into two segments. The first segment focuses on user authentication using a curve fitting technique, implemented via MATLAB simulation. Nodes are randomly deployed with geostationary coordinates and node keys. These nodes are clustered based on their geographical locations, and their legitimacy is verified using curve fitting. This ensures that only authenticated nodes participate in the network, thereby enhancing security and reliability. The second segment employs a modified Ad hoc On-Demand Distance Vector (AODV) protocol for routing, adapted to the clustered network structure. Route Requests (RREQs) are sent to Zone Heads (ZH) for validation and then forwarded to Cluster Heads (CH), where idle and execution costs are calculated based on buffer states and execution capacities. The proposed method also incorporates fog computing to enable localized data processing, reducing latency and improving scalability. The performance of the proposed method was evaluated through extensive simulations, measuring key metrics such as throughput, Packet Delivery Ratio (PDR), and latency. Results show that the proposed method achieves a throughput of 8367.141811 packets per second, a PDR of 0.83336875, and a latency of 6.606503751 seconds, outperforming state-of-the-art algorithms by significant margins. Specifically, the proposed method demonstrates a 7.5% improvement in throughput over Khudhair et al. and a 12.9% improvement over Ahmad et al. In terms of PDR, it shows an 8.8% increase over Khudhair et al. and a 7.1% increase over Ahmad et al. The latency reduction compared to these algorithms is 9.1% and 10.5%, respectively. These enhancements are attributed to the efficient clustering and authentication mechanisms, along with the integration of fog computing. The proposed method thus provides a comprehensive solution for secure and efficient VANET communication, paving the way for advanced intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Images taken in low lamination condition suffer from low contrast and loss of information. Low lumination image enhancement algorithms are required to improve the quality and broaden the applications of such images. In this study, we proposed a new Low lumination image enhancement architecture consisting of a transformer-based curve learning and an encoder-decoder-based texture enhancer. Considering the high effectiveness of curve matching, we constructed a transformer-based network to estimate the learnable curve for pixel mapping. Curve estimation requires global relationships that can be extracted through the transformer framework. To further improve the texture detail, we introduced an encoder-decoder network to extract local features and suppress the noise. Experiments on LOL and SID datasets showed that the proposed method not only has competitive performance compared to state-of-the-art techniques but also has great efficiency. [ABSTRACT FROM AUTHOR]

Siamese-based segmentation and tracking algorithms improve accuracy and stability for video object segmentation and tracking tasks simultaneously. Although effective, variability in target appearance and background clutter can still affect segmentation accuracy and further influence the performance of tracking. In this paper, we present a memory propagation-based target- aware and mask-attention decision network for robust object segmentation and tracking. Firstly, a mask propagation-based attention module (MPAM) is constructed to explore the inherent correlation among image frames, which can mine mask information of the historical frames. By retrieving a memory bank (MB) that stores features and binary masks of historical frames, target attention maps are generated to highlight the target region on backbone features, thus suppressing the adverse effects of background clutter. Secondly, an attention refinement pathway (ARP) is designed to further refine the segmentation profile in the process of mask generation. A lightweight attention mechanism is introduced to calculate the weight of low- level features, paying more attention to low-level features sensitive to edge detail so as to obtain segmentation results. Finally, a mask fusion mechanism (MFM) is proposed to enhance the accuracy of the mask. By utilizing a mask quality assessment decision network, the corresponding quality scores of the “initial mask” and the “previous mask” can be obtained adaptively, thus achieving the assignment of weights and the fusion of masks. Therefore, the final mask enjoys higher accuracy and stability. Experimental results on multiple benchmarks demonstrate that our algorithm performs outstanding performance in a variety of challenging tracking tasks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is facing growing security challenges due to its vulnerability. It is imperative to address the security issues using lightweight and efficient encryption schemes in resource-limited IoT. In this paper, we propose an enhanced message authentication encryption (MAE) scheme based on physical-layer key generation (PKG), which uses the random nature of wireless channels to generate and negotiate keys, and simultaneously encrypts the messages and authenticates the source. The proposed enhanced MAE scheme can greatly improve the security performance via dynamic keyed primitives construction while consuming very few resources. The enhanced MAE scheme is an efficient and lightweight secure communication solution, which is very suitable for resource-limited IoT. Theoretical analysis and simulations are carried out to confirm the security of the enhanced MAE scheme and evaluate its performance. A one-bit flipping in the session key or plain texts will result in a 50%-bit change in the ciphertext or message authentication code. The numerical results demonstrate the good performance of the proposed scheme in terms of diffusion and confusion. With respect to the typical advanced encryption standard (AES)- based scheme, the performance of the proposed scheme improves by 80.5% in terms of algorithm execution efficiency. [ABSTRACT FROM AUTHOR]

This two-part series provides a comprehensive guide to fabricating definitive metal partial dentures, addressing the challenges dental practitioners face in mastering prosthesis design. It aims to equip dental professionals with a thorough understanding of the theoretical foundations and clinical methods for effective metal partial denture fabrication. Part 2 covers removable partial denture (RPD) design philosophies, tooth preparation, secondary impressions, laboratory procedures, trial and final insertions, as well as post-insertion complications and their management. Additionally, four case scenarios are included to help readers apply the knowledge gained from both parts to design a metal partial denture. CPD/Clinical Relevance: This series provides essential knowledge and practical techniques for designing and fabricating effective metal partial dentures. [ABSTRACT FROM AUTHOR]

Haematuria and proteinuria are common findings on urinalysis in childhood. They usually occur in either children with specific symptoms e.g. macroscopic haematuria or nephrotic syndrome, or in those who have non-specific symptoms (usually when looking to exclude urinary tract infection) and sometimes in asymptomatic children. The majority of children fall into the latter two groups, the finding is temporary and not associated with long term renal disease. If the finding is persistent or patients have specific clinical features then renal abnormalities are more frequent and appropriate investigation is required. This review provides a rationale for an approach that allows reassurance to be given to children and their families in whom the finding is transient and benign, whilst identifying those in whom renal abnormalities are present and treatment required. It describes algorithms for macroscopic haematuria (MaH), asymptomatic microscopic haematuria (MiH) and proteinuria. [ABSTRACT FROM AUTHOR]

Existing sea surface small target detection methods typically rely on intricate feature extraction techniques on transformed radar returns. However, these approaches suffer from issues of high computational complexity and low real-time performance. Temporal Convolutional Network (TCN) can enable direct processing of radar time-series echo data without the need for elaborate feature extraction, thus substantially improving computational efficiency. Building upon this, this paper presents a novel target detection algorithm based on Multi-layer Attention Temporal Convolutional Network (MA-TCN). The proposed algorithm processes the amplitude information in the original echo signals, and comprehensively extracts sequence feature information through the construction of stacked residual modules. Additionally, it integrates multi-layer attention mechanisms to adaptively adjust the output weights of each residual module, thereby further enhancing detection accuracy. Experimental results demonstrate that the proposed approach achieves significant improvements in both detection performance and efficiency compared to existing methods. [ABSTRACT FROM AUTHOR]

As deep learning advances, neural network technologies are increasingly penetrating the field of steel surface defect detection. To tackle the challenges of low accuracy and inadequate quality, we introduce CMS-YOLOv8s, a defect detection algorithm engineered for superior precision and efficiency. Initially, integrate the CBAM to enhance the network's focus on relevant information, allowing it to leverage multi-dimensional data like spatial and channel dimensions fully. Next, introduce the SPPFCSPC module to fuse feature information from different scales, expanding the model's receptive field and enhancing its feature extraction abilities. Finally, incorporate a small object detection head to boost the model's capability in identifying tiny targets, strengthening its ability to detect targets across different scales. Experimental findings show that CMS-YOLOv8s attains a detection accuracy of 70.4%, registering a significant 3.3% enhancement compared to the original YOLOv8s. This advancement distinctly improves defect detection accuracy across all categories. [ABSTRACT FROM AUTHOR]

The detection algorithms of small objects in remote sensing images is often challenging due to the complex background and limited pixels. This can lead to reduced accuracy in detection and an increased number of missed small objects. So this paper introduces YOLOFNC, an enhanced network based on YOLOv7. To improve the model's ability to capture features of small objects, an enhanced C3-Faster module based on the C3 module is designed and integrated into the YOLOv7 network. This module helps extract more features related to small objects. Additionally, we employ Normalized Wasserstein Distance (NWD) fusion GIoU as a novel loss function to refine the accuracy of network optimization weights and the small object regression framework. Furthermore, a coordinated attention (CA) mechanism is incorporated at strategic locations in the model to reduce redundant information in the feature layer and prevent the loss of important small object features. we conduct comparison experiments between YOLO-FNC and other commonly used object detection algorithms on DIOR, AITOD, and VisDrone datasets. The experimental results show that YOLO-FNC achieves 84.4% mAP on the DIOR dataset, 35.9% mAP on the AI-TOD dataset, and 52.6% mAP on the VisDrone dataset. Compared to YOLOv7 and other remote sensing object detection models, YOLO-FNC demonstrates better performance in object detection. [ABSTRACT FROM AUTHOR]

Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones. [Display omitted] • Encryption-based data consolidation strategy for 5G in fog computing is presented. • Data consolidation and data extraction algorithm at fog devices and cloud servers. • A comparative analysis based on storage, communication and transmission cost is done. [ABSTRACT FROM AUTHOR]

We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in real-time, enhancing the operational efficiency of urban freeways with less investment. To simplify the implementation and training of the algorithm, we developed a simulation platform based on SUMO microscopic traffic simulator. We conducted a series of simulation experiments, including local and coordinated ramp metering scenarios with various traffic demands profiles. The simulation results indicate that the proposed DRL-based algorithm outperforms the state-of-the-practice ramp metering methods, considering a comprehensive evaluation index encompassing mainstream speed at the bottleneck and queue length on ramp. Additionally, the method exhibits robustness, scalability, and the potential for further improvement through online learning during implementation. [ABSTRACT FROM AUTHOR]

Radiographic evaluation of the implant configuration after reverse shoulder arthroplasty (RSA) is time-consuming and subject to interobserver disagreement. The final configuration is a combination of implant features and surgical execution. Artificial intelligence (AI) algorithms have been shown to perform accurate and efficient analysis of images. The purpose of this study was to develop an AI algorithm to automatically measure glenosphere inclination, humeral component inclination, and the lateralization and distalization shoulder angles (DSAs) on postoperative anteroposterior radiographs after RSA. The Digital Imaging and Communications in Medicine files corresponding to postoperative anteroposterior radiographs obtained after implantation of 143 RSAs were retrieved and used in this study. Four angles were analyzed: (1) glenoid inclination angle (GIA, between the central fixation feature of the glenoid and the floor of the supraspinatus fossa), (2) humeral alignment angle (HAA, between the long axis of the humeral shaft and a perpendicular to the metallic bearing of the prosthesis), (3) DSA, and (4) lateralization shoulder angle (LSA). A UNet segmentation model was trained to segment bony and implant elements using manually segmented training (n = 89) and validation (n = 22) images. Then, an image-processing–based pipeline was developed to measure all 4 angles using AI-segmented images. Measures performed by 3 physician observers and the AI algorithm were then completed in 32 additional images. The agreements among human observers and between observers and the AI algorithm were evaluated using intraclass correlation coefficients (ICCs) and absolute differences in degree. The ICCs (95% confidence interval) for manual measurements of LSA, DSA, GIA, and HAA were 0.79 (0.55, 0.90), 0.90 (0.80, 0.95), 0.96 (0.93, 0.98), and 0.99 (0.97, 0.99), respectively. The AI algorithm measured the 32 images in the test set in less than 2 minutes. The agreement between observers and the AI algorithm was lowest when measuring the LSA for observer 2, with an ICC of 0.77 (0.52, 0.89), and an absolute difference in degrees (median [interquartile range]) of 5 (4). Better agreements were found between the AI measurements and the average manual measurements: absolute differences in degree for LSA, DSA, GIA, and HAA were 3 (5), 2 (3), 2 (2), and 2 (1), respectively; ICCs for LSA, DSA, GIA, and HAA were 0.89 (0.79, 0.95), 0.96 (0.93, 0.98), 0.85 (0.68, 0.93), and 0.98 (0.95, 0.99), respectively. The AI algorithm developed in this study can automatically measure the GIA, HAA, LSA, and DSA on postoperative anteroposterior radiographs obtained after implantation on RSA. [ABSTRACT FROM AUTHOR]

Preoperative planning has gained popularity in the management of reverse shoulder arthroplasty (RSA). Commercially available software provides 3-dimensional segmentation of scapula and humerus, as well as providing arc of motion for the implanted articulation and identifying potential areas of bony impingement. However, these software algorithms use a fixed scapula model, disregarding the preoperative clinical range of motion (C-ROM) of the patient, be it glenohumeral or scapulothoracic, as well as any soft tissue parameters. This study aims to compare the ROM based on preoperative planning software by using the implant position from postoperative computed tomography (CT) images (predicted ROM using preoperative planning software [P-ROM]), with the C-ROM assessed at minimum of 2 years of follow-up. Preoperative and postoperative CT scans of 46 patients who underwent primary RSA between 2017 and 2021 were analyzed. At the postoperative 2-year review, each patient was assessed for active ROM. Implant size and position based on operative notes and postoperative CT scans were used to replicate the performed surgery in the planning software. Abduction, flexion, and external rotation motion were simulated and recorded. The relationship between C-ROM and P-ROM was investigated using linear regression analysis, Pearson correlation coefficient, and paired t -test. P-ROM was significantly lower than C-ROM at 2 years postoperatively (P <.001), with an average discrepancy of 78° in abduction, 47° in flexion, and 37° in external rotation (C-ROM: abduction 155° ± 21° [80°-180°]; flexion 160° ± 17° [90°-180°]; external rotation 52° ± 14° [10°-80°] vs. P-ROM: abduction 77° ± 13° [53°-107°]; flexion 112° ± 25° [67°-180°]; external rotation 15° ± 21° [0°-79°]). The linear regression analysis indicated weak agreement between C-ROM and P-ROM (abduction R2 = 0.03; flexion R2 = 0.01; external rotation R2 = 0.04). Pearson's correlation coefficients revealed weak correlations of −0.18, 0.03, and 0.21 for abduction, flexion, and external rotation, respectively. P-ROM based on preoperative software in its current form does not allow the prediction of the C-ROM at 2 years of follow-up for patients undergoing RSA. [ABSTRACT FROM AUTHOR]

This study is concerned with a realization of horizontal federated Gustafson–Kessel clustering algorithm and the ensuing construction of ellipsoidal information granules. As a fundamental component of Granular Computing, information granules play an important role in human-centric computing, such as human cognition and decision-making. Driven by the concerns of data privacy and confidentiality, it is of interest to investigate how to construct information granules on the basis of horizontally partitioned numeric data distributed across different sites using a privacy-preserving approach. To meet this challenge, federated learning has become an appealing solution to the problem of forming meaningful clusters (information granules) while ensuring data privacy and confidentiality. A two-development strategy is applied in the proposed algorithm: first, a collection of numeric representatives (prototypes) is obtained with the use of federated Gustafson–Kessel algorithm, which is able to reveal ellipsoidal shapes in the datasets and second, information granules are built through engaging the principle of justifiable granularity. A series of experimental studies demonstrate the effectiveness of the proposed federated Gustafson-Kessel algorithm in revealing the structure of the entire dataset. The formed ellipsoidal information granules help us gain a better insight into the topology of the overall dataset. [ABSTRACT FROM AUTHOR]

Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an efficient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance. [ABSTRACT FROM AUTHOR]

We present a rigorous mathematical analysis of the modeling of inviscid water waves. The free-surface is described as a parametrized curve. We introduce a numerically stable algorithm which accounts for its evolution with time. The method is shown to converge using approximate solutions, such as Stokes waves and Green-Naghdi solitary waves. It is finally tested on a wave breaking problem, for which an odd-even coupling suffices to achieve numerical convergence up to the splash without the need for additional filtering. [ABSTRACT FROM AUTHOR]

The utility of atrioventricular (AV) optimization (AVO) algorithms remains in question. A substudy of the SMART-AV trial found that patients with prolonged interventricular delays ≥70 ms were more likely to benefit from cardiac resynchronization therapy (CRT) with AVO. The SMART-CRT trial evaluated AVO on the basis of these results, but the study was underpowered. To increase statistical power, data from SMART-AV patients meeting the inclusion criterion of interventricular delay ≥70 ms were pooled with data from SMART-CRT to reassess AVO. SMART-CRT and SMART-AV were prospective, randomized, multicenter clinical trials. Patients in both studies were randomized to be programmed with an AVO algorithm (SmartDelay) or fixed AV delay (120 ms). Paired echocardiograms obtained at baseline and 6 months were compared, with CRT response defined as ≥15% reduction in left ventricular end-systolic volume. A total of 451 complete patient data sets were pooled and analyzed. The baseline demographics between studies did not differ statistically in terms of age, sex, left ventricular ejection fraction, or left ventricular end-systolic volume. The AVO group had a greater proportion of CRT responders (SmartDelay, 73.9%; fixed, 63.1%; P =.014) and greater changes in measures of reverse remodeling. SmartDelay patients with a recommended sensed AV delay outside the nominal range (100–120 ms) had 2.3 greater odds of CRT response than fixed AV delay patients. Greater CRT response and measures of reverse remodeling were observed in patients with SmartDelay enabled vs a fixed AV delay. This study supports the use of SmartDelay in patients with a CRT indication and interventricular delay ≥70 ms. [Display omitted] [ABSTRACT FROM AUTHOR]

This study proposes a controller design for the fixed-time stabilisation of a double integrator system (DIS), taking into account both output and state constraints. The controller combines Barrier Lyapunov Function (BLF)-based control method with homogeneous fixed-time controllers to provide faster convergence, eliminate the singularity of BLF-based fixed-time control algorithms, and guarantee precise convergence to the origin while maintaining the constraints. Three different cases including output constraint, output and state constraints, and fixed-time extended-state observer-based control for the disturbed DIS are studied. Several sets of simulations are carried out and comparisons are made with several finite-time and fixed-time controllers. In addition, the proposed control system is applied to the lateral control of an autonomous vehicle. Simulation results outline faster convergence, faster response, less sensitivity to sensor noise, more capability in disturbance rejection, and more accurate positioning compared to previous control systems. • This study provides an innovative framework for combining homogeneous control methods with BLF-based control algorithms. • The combined algorithm eliminates the singularity of conventional fixed-time BLF-based controllers. • The proposed technique guarantees precise convergence of system states to the origin while maintaining the output constraints. • A novel BLF is proposed which satisfies the conditions of bi-limit homogeneous approximation. • The proposed BLF provides superior performance in terms of faster response and tunability. [ABSTRACT FROM AUTHOR]

Despite carrying an excess risk of cardiovascular events, primary aldosteronism (PA) is a commonly overlooked secondary form of arterial hypertension. An increased awareness of its high prevalence and broader screening strategies are urgently needed to improve its detection rate and allow early diagnosis and targeted treatment. For patients with unilateral PA, these measures can correct hyperaldosteronism and ensure cure of hypertension, even when resistant to drug treatment, thus preventing adverse cardiovascular events. Among these, atrial fibrillation is the most common, but left ventricular hypertrophy, stroke, chronic kidney disease, and myocardial infarction also occur more often than in patients with hypertension and no PA. Young patients, who have higher chances of being cured long term, and high-risk patients, such as those with stage III or resistant hypertension, are those who will benefit most from an early diagnosis of PA. Therefore, the implementation of strategies to detect PA by a simplified diagnostic algorithm is necessary. In the patients who seek for surgical cure, adrenal vein sampling is key for the identification of unilateral PA and the achievement of optimal outcomes. Unfortunately, being technically demanding and poorly available, adrenal vein sampling represents the bottleneck in the workup of PA. Considering the novel knowledge generated in the past 5 years in many studies, particularly in the AVIS-2 study (Adrenal Vein Sampling International Study-2), based on 4 decades of experience at our center and on the last guidelines, we herein provide an update on the management of PA with recommendations for drug treatment and strategies to avoid adrenal vein sampling wherever it is poorly, or not, available. [ABSTRACT FROM AUTHOR]

Wildfire science needs new methods to evaluate and predict wildfire behavior and impacts. In this study, we present a new algorithm that calculates the maximum fire rate of spread resulting from a fire progression map made of isochrones, according to 1) the shape of the isochrones, and/or 2) prevailing atmospheric wind direction. We have applied the algorithm to a set of 30 fires, differentiated according to the way the isochrone map was surveyed: by firefighters' GPS positioning (in Europe, ~ 1-hour frequency), or by satellite data (North and South America, and Australia, ~ 12-hour frequency). The rate of spread was also related to the growth rate in hectares, and with the morphology of the fire scar. Our results reveal maximum speeds of 8.62 and 6.79 km/h for the GPS and satellite datasets respectively. In general, the GPS dataset shows faster runs that burn smaller extensions than the satellite dataset. Runs following wind direction are more elongated than the ones driven by other factors, which show more expansive runs. The algorithm presented here can be useful for the study of the factors influencing fire propagation. [ABSTRACT FROM AUTHOR]