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Sound Source Localization (SSL) is a key task in audio signal processing, focusing on estimating the position of sound sources relative to a reference point, typically a microphone array. In this paper, we introduce a novel dataset named SoS, specifically designed for indoor SSL scenarios, containing real-life recordings augmented by background noises. Subsequently, we perform a comparative analysis of various features and models across three categories: traditional signal processing methods, classical machine learning approaches, and deep learning architectures. This analysis offers insights into the performance and limitations of each method under varying conditions. Our findings show that augmentation methods like Time Masking, when paired with the AdamW optimizer and Huber loss, typically result in performance improvements than alternative configurations. Our investigation into robustness to phase information discrepancies led us to the conclusion that magnitude feature might be more useful than traditional features like mel spectra or time-difference-of-arrival (TDoA). Additionally, our study emphasizes the difficulties in utilizing budget microphone receivers in a coplanar quadrilateral arrangement to achieve better sound source localization.

Cognitive Behavioral Therapy (CBT) for tinnitus alleviates psychological discomfort caused by severe tinnitus symptoms. During CBT, the patients will have various homework assignments, including writing daily diaries and self-monitoring. Most of these homework assignments are hand-written, textual data. This paper proposes that tinnitus therapeutics can utilize Large Language Models (LLMs) to analyze CBT and predict the outcomes of CBT treatments to manage high caseloads. We anonymized patient data and examined it with GPT-2-based-embedding, dimensionality reduction, and clustering process to observe how patients themselves changed their misconceptions and developed less unnecessary excessive emotional discomfort and how their Tinnitus Handicap Inventory (THI) scores were improved after the CBT treatment. We also discussed clustering results as a part of the demonstrations that LLMs can give us insights into the CBT. Then, we augmented textual patient data in three ways to minimize augmentation bias with a corresponding penalty to overcome the constraints of limitation of the number of datasets. We trained the Google T5 Transformer with the augmented data to predict the THI score outcomes at the end of the CBT sessions. We measured the performance using the ROUGE-L metric during the training and validation. The generated THI scores by Google T5 were converted from strings to floats to measure RMSE performance, which proved that the LLM could predict the outcome of CBT treatment with CBT data. Even though there is a risk of overfitting issues, this work demonstrated that tinnitus therapeutics experts can employ LLMs to manage caseloads.

Digital Human Resource Management transformation represent a reality that each organisation is dealing with these days, a situation that was accelerated by COVID-19 pandemic period. Organisations that are taking this step are determined by the benefits that the transformation could have for both employees and organisation’s performance. However, research in the field failed in offering the evidence on these benefits in relation to all type of performance. At the same time, despite the advantages brought, the process could still be a challenge for HR professionals to implement, as blending digital processes and human touch is not so easy to be done. Research on the way in which the digital HRM transformation should take place, focusing on factors that facilitate the transformation as well as on HR professionals competencies needed is recommended. Thereby, the purpose of our paper is to analyse the main concepts of augmenting HRM and to propose a future research methodology for this topic.

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ObjectiveThis paper aims to review the available evidence for the use of clozapine and electroconvulsive therapy (ECT) in combination.MethodologyElectronic searches were carried out to identify reports describing the combined use of clozapine and ECT.ResultsForty reports including 208 patients were identified. The majority of reports were in the form of case reports and case series, with few retrospective and open-label studies. The majority of patients were aged between 18 and 65 years and diagnosed with schizophrenia or schizoaffective disorder. Most of the patients refractory to clozapine were started on ECT as an augmentation therapy; however, in some reports, both ECT and clozapine were started concurrently, and in few cases clozapine was started after ECT. In terms of effectiveness, 37.5–100% patients improved in short-term, and sustained long-term improvement (3 weeks to 24 months) was described in few studies. In terms of the side-effect profile, five patients each had delirium and tachycardia and only four patients were described to have prolonged seizures. Overall, the combination was considered effective and safe.ConclusionThere is evidence for the effectiveness and safety of the clozapine–ECT combination and it should be used in patients with treatment-resistant schizophrenia who do not respond to clozapine. [ABSTRACT FROM PUBLISHER]

Introduction: Disruption of the knee extensor mechanism is a serious disorder that requires prompt treatment. It often occurs in the form of a patellar tendon rupture. It may occur in association with systemic disease or after administration of corticosteroids or fluoroquinolones. These conditions can cause tendon weakness, and consequent ruptures usually require both repair and augmentation. This paper reports on repair and augmentation for treating patellar tendon rupture in patients with Ehlers-Danlos syndrome (EDS). Case report: We report a patellar tendon rupture in a 27-year-old man with EDS, which occurred in the midsubstance of the patella. As the patient has tendon weakness, extensive repair will increase the risk of patella baja, and the use of end-to-end suturing technique alone will not be enough to prevent a rupture recurring; however, augmentation could be used to address the tendon weakness. Repair of the rupture and augmentation with hamstring tendon was performed. One year after the surgery, the patient was able to move his knee joint without pain and had an active range of motion of 0° (passive 20°)-145°. He was able to perform a straight leg raise without an extension lag. Conclusions: Repair and augmentation with hamstring tendon was an effective treatment option for patellar tendon rupture in a patient with EDS. [ABSTRACT FROM AUTHOR]

In this paper we introduce the circuit diameter of polyhedra, which is always bounded from above by the combinatorial diameter. We consider dual transportation polyhedra defined on general bipartite graphs. For complete MxN bipartite graphs the Hirsch bound (M-1)(N-1) on the combinatorial diameter is a known tight bound [Math. Oper. Res., 9 (1984), pp. 629-633]. For the circuit diameter we show the much stronger bound M+N-2 for all dual transportation polyhedra defined on arbitrary bipartite graphs with M+N nodes. [ABSTRACT FROM AUTHOR]

• The greedy AutoAugment is proposed to find the best augmentation policies. • It reduces the exponential growth of the number of possible trials to linear growth. • The algorithm is computationally efficient and can concatenate many sub-policies. • The greedy approach helps the algorithm to increase the accuracy of the network. A major problem in data augmentation is to ensure that the generated new samples cover the search space. This is a challenging problem and requires exploration for data augmentation policies to ensure their effectiveness in covering the search space. In this paper, we propose Greedy AutoAugment as a highly efficient search algorithm to find the best augmentation policies. We use a greedy approach to reduce the exponential growth of the number of possible trials to linear growth. The Greedy Search also helps us to lead the search towards the sub-policies with better results, which eventually helps to increase the accuracy. The proposed method can be used as a reliable addition to the current artifitial neural networks. Our experiments on four datasets (Tiny ImageNet, CIFAR-10, CIFAR-100, and SVHN) show that Greedy AutoAugment provides better accuracy, while using 360 times fewer computational resources. [ABSTRACT FROM AUTHOR]

• The OccGAN structure is a semantic augmentation method on Cityscapes. • The Rationality Module utilizes prior knowledge to implant occluders. • The Authenticity Module ensures the plausibility by a generative adversarial network. • Our Method improves the performance of several SOTA algorithms. Difficult images with complicated environments and occlusion have significant impacts on the performance of algorithms. They obey the long-tail distribution in the widely used datasets, which results in rare samples being overwhelmed during training. This paper presents a new approach to generate plausible occluded images with annotation as a kind of data augmentation with scenes semantics. To achieve this task, we proposed the Occlusion-based Generative Adversarial Network (OccGAN) structure, which consists of a Rationality Module and an Authenticity Module. The Rationality Module generated preliminary occluded samples under the guidance of prior semantic knowledge. And the Authenticity Module is a generative adversarial structure to ensure the reality of the produced images. Qualitative results of the visualization process are given to verify the ablation study. Experiments on the semantic segmentation task indicate that several state-of-the-art algorithms combined with our OccGAN such as DRN, Deeplabv3+, PSPNet and ResNet-38, have boosts on IoU class scores and IoU category scores successfully. [ABSTRACT FROM AUTHOR]

Copyright of Psychiatrie Pro Praxi is the property of SOLEN sro 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.)

• The proposed TB-MFCC multifuse feature is comprised of data augmentation and feature extraction. • First, Pooled Multifuse Feature Augmentation uses the noise injection, stretching, shifting and pitching separately to increase the number of instances in the dataset and reduces overfitting. • Second, Triangular Bluestein Mel Frequency Cepstral Coefficients (TB-MFCC) are proposed and fused with Zero Crossing Rate (ZCR), Mel-frequency cepstral coefficients (MFCC) to extract the exact feature which increases the accuracy and reduces the Mean Squared Error (MSE). • Finally, the proposed MCNN with A-BiLSTM improves the nonlinear relationship among the features. • Thus the proposed augmentation, segmentation and classification is used to increase the accurate prediction of vehicle with reduced loss, less chance of overfitting and underfitting and avoids vanishing gradient problems. Vehicles equipped for emergencies like ambulances, fire engines, and police cruisers play a vital role in society by responding quickly to emergencies and helping to prevent loss of life and maintain order. Vehicle sound identification and classification are very important in the cities to identify emergency vehicles easily and to clear the traffic effectively. Convolutional Neural Network plays an important role in the accurate prediction of vehicles during an emergency. The main motive of this paper is to develop a suitable model and algorithms for data augmentation, feature extraction, and classification. The proposed TB-MFCC multifuse feature is comprised of data augmentation and feature extraction. First, in the proposed signal augmentation, each audio signal uses noise injection, stretching, shifting, and pitching separately and this process increases the number of instances in the dataset. The proposed augmentation reduces the overfitting problem in the network. Second, Triangular Bluestein Mel Frequency Cepstral Coefficients (TB-MFCC) are proposed and fused with Zero Crossing Rate (ZCR), Mel-frequency cepstral coefficients (MFCC), Root Mean Square (RMS), Chroma, and Tempogram to extract the exact feature which increases the accuracy and reduces the Mean Squared Error (MSE) of the model during classification. Finally, the proposed Multi-stacked Convolutional Neural Network (MCNN) with Attention-based Bidirectional Long Short Term Memory (A-BiLSTM) improves the nonlinear relationship among the features. The proposed Pooled Multifuse Feature Augmentation (PMFA) with MCNN & A-BiLSTM increases the accuracy (98.66 %), reduces the False Positive Rate (FPR) by 1.01 %, and loss (0 %). Thus the model predicts the sound without overfitting, underfitting, and vanishing gradient problems. [ABSTRACT FROM AUTHOR]

• In this paper, we explore how firms implement AI within R&D. • We make use of an international news database spanning 956 articles from 122 different newspapers publications in 2020. • AI is presently primarily adopted in order to augment human activities (55%) within R&D rather than to automate matters (11%). • Automation is more common in government, ICT and Tech & Software. • AI is primarily adopted for exploration R&D (64%) rather than exploitation (5%). Digitalization has altered many assumptions underpinning research on innovation management. At the early innings of exploring how digital innovation management stands out, there is a need for further studies in this area. Previous research on how firms use artificial intelligence has distinguished between automation and augmentation of human activities. In this paper, we explore how firms implement artificial intelligence within research and development. Utilizing an international news database spanning 956 articles from 122 newspapers published in 2020, we find that artificial intelligence is primarily adopted to augment human activities (55%) within research and development, rather than to automate matters (11%). We observe differences across sectors where automation is more common in government, information and communication technology (ICT), and technology and software. Our systematic coding shows that artificial intelligence is primarily adopted for exploration research and development (64%), rather than exploitation (5%). Based on these findings, we conclude that research and development from artificial intelligence primarily focuses on novel markets and areas of operations, rather than enhancing existing product markets and activities. Moreover, it augments human labor rather than replaces it; hence, job losses related to artificial intelligence do not seem to be taking place within research and development. [ABSTRACT FROM AUTHOR]

As an alternative approach, viseme-based lipreading systems have demonstrated promising performance results in decoding videos of people uttering entire sentences. However, the overall performance of such systems has been significantly affected by the efficiency of the conversion of visemes to words during the lipreading process. As shown in the literature, the issue has become a bottleneck of such systems where the system’s performance can decrease dramatically from a high classification accuracy of visemes (e.g., over 90%) to a comparatively very low classification accuracy of words (e.g., only just over 60%). The underlying cause of this phenomenon is that roughly half of the words in the English language are homophemes, i.e., a set of visemes can map to multiple words, e.g., “time” and “some”. In this paper, aiming to tackle this issue, a deep learning network model with an Attention based Gated Recurrent Unit is proposed for efficient viseme-to-word conversion and compared against three other approaches. The proposed approach features strong robustness, high efficiency, and short execution time. The approach has been verified with analysis and practical experiments of predicting sentences from benchmark LRS2 and LRS3 datasets. The main contributions of the paper are as follows: (1) A model is developed, which is effective in converting visemes to words, discriminating between homopheme words, and is robust to incorrectly classified visemes; (2) the model proposed uses a few parameters and, therefore, little overhead and time are required to train and execute; and (3) an improved performance in predicting spoken sentences from the LRS2 dataset with an attained word accuracy rate of 79.6%—an improvement of 15.0% compared with the state-of-the-art approaches.

In recent years, computer vision tasks have gained a lot of popularity, accompanied by the development of numerous powerful architectures consistently delivering outstanding results when applied to well-annotated datasets. However, acquiring a high-quality dataset remains a challenge, particularly in sensitive domains like medical imaging, where expense and ethical concerns represent a challenge. Generative adversarial networks (GANs) offer a possible solution to artificially expand datasets, providing a basic resource for applications requiring large and diverse data. This work presents a thorough review and comparative analysis of the most promising GAN architectures. This review is intended to serve as a valuable reference for selecting the most suitable architecture for diverse projects, diminishing the challenges posed by limited and constrained datasets. Furthermore, we developed practical experimentation, focusing on the augmentation of a medical dataset derived from a colonoscopy video. We also applied one of the GAN architectures outlined in our work to a dataset consisting of histopathology images. The goal was to illustrate how GANs can enhance and augment datasets, showcasing their potential to improve overall data quality. Through this research, we aim to contribute to the broader understanding and application of GANs in scenarios where dataset scarcity poses a significant obstacle, particularly in medical imaging applications. [ABSTRACT FROM AUTHOR]

Deep-learning models play a significant role in modern software solutions, with the capabilities of handling complex tasks, improving accuracy, automating processes, and adapting to diverse domains, eventually contributing to advancements in various industries. This study provides a comparative study on deep-learning techniques that can also be deployed on resource-constrained edge devices. As a novel contribution, we analyze the performance of seven Convolutional Neural Network models in the context of data augmentation, feature extraction, and model compression using acoustic data. The results show that the best performers can achieve an optimal trade-off between model accuracy and size when compressed with weight and filter pruning followed by 8-bit quantization. In adherence to the study workflow utilizing the forest sound dataset, MobileNet-v3-small and ACDNet achieved accuracies of 87.95% and 85.64%, respectively, while maintaining compact sizes of 243 KB and 484 KB, respectively. Henceforth, this study concludes that CNNs can be optimized and compressed to be deployed in resource-constrained edge devices for classifying forest environment sounds. [ABSTRACT FROM AUTHOR]

Computer Vision (CV) domain is widely used in the current era of automation and visual surveillance for the detection and classification of different objects in a diverse environment. The automatic machine inspection of different objects in the scenes is based on internal and external parameters like features that provide a huge amount of information related to the nature of an object in the scene. In this work, we propose a new automated method based on classical and deep learning feature selection. The proposed object classification method follows three steps. The data augmentation is performed in the first step to make the balance database. Later, Pyramid HOG (PHOG) and Central Symmetric LBP (CS-LBP) features are serially fused along with deep learning-based extracted features. The deep learning features are extracted from the pre-trained CNN model name Inception V3. In the third step, a new technique name Joint Entropy along with KNN (JEKNN) is employed to select the best features. The best-selected features are finally classified by well-known supervised learning methods and choose the best one based on higher accuracy. The proposed method is evaluated on Caltech101 balanced dataset and achieved maximum accuracy of 90.4% on Ensemble classifier which outperforms as compare to existing techniques. [ABSTRACT FROM AUTHOR]

Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for the validation process, such as apple, grape, and Citrus. In the first step, a noisy dataset is prepared by employing the original images to learn the designed framework better. The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data. After that, features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm (PFA). This algorithm aims to select the best features based on the fitness score and ignore redundant information. The selected features are finally classified usingmachine learning classifiers such asMediumNeuralNetwork, WideNeuralNetwork, and Support Vector Machine. The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%, 99.7%, 99.7%, and 93.4% for apple, grape, Citrus fruit, and citrus plant leaves, respectively. A detailed analysis is conducted and also compared with the recent techniques, and the proposed framework shows improved accuracy. [ABSTRACT FROM AUTHOR]

To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and the scarcity of representative training data. To tackle these limitations, we propose a deep learning-based breast tumor gigapixel histological image multi-classifier integrated with a high-resolution data augmentation model to process the entire slide by exploring its local and global information and generating its different synthetic versions. The key idea is to perform the classification and augmentation in feature latent space, reducing the computational cost while preserving the class label of the input. We adopt a deep learning-based multi-classification method and evaluate the contribution given by a conditional generative adversarial network-based data augmentation model on the classifier's performance for three tumor classes in the BRIGHT Challenge dataset. The proposed method has allowed us to achieve an average F1 equal to 69.5, considering only the WSI dataset of the Challenge. The results are comparable to those obtained by the Challenge winning method (71.6), also trained on the annotated tumor region dataset of the Challenge. [ABSTRACT FROM AUTHOR]

Cancer is one of the most dangerous diseaseswith highmortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography (CT) images of cancer patients. Medical image data hasmore complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem. We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation. In strategies, we trained our custom convolutional neural network model which has a structure inspired by the U-net, and residual blocks were also applied to the architecture. The output of the network is added with a rectified linear unit (Re-Lu) function for each pixel to ensure there are no negative values, which are absurd with radiation doses. Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy. The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score (DVH-score). In the best training cases, the Dose-score is 3.08 and the DVH-score is 1.78. In addition, we also conducted a comparison with the results of another study in the same context of using the loss function. [ABSTRACT FROM AUTHOR]

Background: Anterior cruciate ligament (ACL) reconstruction has been the gold standard for primary ACL rupture since the 1990s. In the past decade, ACL repair has received renewed attention and increased research. Purpose: To compare the clinical outcomes of modern augmented ACL repair versus autograft reconstruction for ACL ruptures. Study design: Systematic review; Level of evidence, 3. Methods: A search of the PubMed, EMBASE, Scopus, Web of Science, and the Cochrane Library databases was conducted for prospective or retrospective comparative studies published between January 1, 2010, and January 3, 2023, with a minimum 2-year follow-up. Two independent reviewers performed data extraction and methodological quality assessment. Sensitivity analysis was performed to maintain the stability of results. Results: Nine studies were included (minimum follow-up period, 24-60 months). The total sample size was 833 patients (augmented repair group: 358 patients; autograft ACL reconstruction group: 475 patients). There were 4 randomized controlled trials (level 1), 1 prospective comparative study (level 2), 2 retrospective comparative studies (level 3), and 2 case-control studies (level 3). The augmented ACL repair group attained significantly higher Lysholm score (weighted mean difference [WMD] = 1.57; 95% confidence interval [CI], 0.14-3.01; P = .03) and hamstring strength (WMD = 36.69; 95% CI, 29.07-44.31; P < .01) but had higher rates of hardware removal (odds ratio [OR] = 6.30; 95% CI, 2.44-16.23; P = .0001), reoperation (OR = 1.87; 95% CI, 1.33-2.62; P = .0003), and failure (OR = 1.58; 95% CI, 1.03-2.43; P = .0003) compared with the autograft ACL reconstruction group. No significant differences were observed between the repair and reconstruction groups regarding postoperative International Knee Documentation Committee scores, Tegner scores, knee laxity, satisfaction, ACL revisions, complications, and reoperation rather than revision. Conclusion: Augmented ACL repair was associated with higher rates of reoperation, hardware removal, and failure compared with autograft ACL reconstruction in studies with minimum 2-year follow-up data. However, augmented ACL repair had higher Lysholm scores and hamstring strength versus autograft ACL reconstruction. [ABSTRACT FROM AUTHOR]

Background: Posterior cruciate ligament (PCL) injuries to the knee are uncommon, and ideal surgical management of these injuries is unclear. Current surgical techniques include PCL reconstruction with remnant debridement, remnant-preserving techniques, and primary PCL repair. Augmentation of PCL repairs and reconstructions has been proposed to protect repairs or grafts in the postoperative period. Purpose: To describe PCL repair with the hamstring autograft augmentation technique and examine our preliminary midterm outcomes from a sequential cohort of patients. Study design: Case series; Level of evidence, 4. Methods: The authors identified patients at their institution who underwent remnant-preserving primary PCL repair with hamstring autograft augmentation for both isolated tears and tears associated with multiligament knee injury (MLKI). Patient-reported outcomes were evaluated at a minimum 2-year follow-up using the International Knee Documentation Committee (IKDC) subjective knee form, the 12-item Short Form Survey, and a custom return-to-play questionnaire. Patient-reported outcomes data were summarized, and the predictors of outcomes from the descriptive data and clinical measures were further examined. Results: A total of 23 patients with a mean follow-up of 5.3 years met the inclusion criteria. Of these patients, 87% were associated with MLKI. The mean IKDC score was 87.7. Approximately 83% of patients were able to successfully return to their sport or occupation. Among 19 athletes, only 2 reported being unable to return to their preinjury level of sport because of limitations from their PCL surgery. Patient-reported outcome scores and return to sport or occupation did not have a statistically significant association with age, sex, body mass index, time from injury to surgery, or follow-up time. Conclusion: Outcomes of our cohort with remnant-preserving primary PCL repairs with hamstring autograft augmentation demonstrated comparable clinical outcomes to previously published PCL data. The advantages of remnant preservation, primary repair, and augmentation with an independent hamstring autograft reconstruction are combined within this technique. [ABSTRACT FROM AUTHOR]

Acetabular augmentation is a challenge for complex primary and revision hip surgery. Several methods exist to recreate a supportive, hemispherical acetabulum where a defect exists, however, these can be complex, expensive, time consuming, and limited by available size and shape. We present a simple, inexpensive, quick and customisable method of acetabular augmentation using cementless polyethylene with titanium coating. The first case is presented with follow-up up to 8 years. [ABSTRACT FROM AUTHOR]

The aim of this research is to enhance the quality of prenatal ultrasound images by addressing common artifacts such as missing or damaged areas, speckle noise, and other types of distortions that can impede accurate diagnosis. The proposed approach involves a novel preprocessing pipeline for prenatal 5th-month ultrasound scan images, which includes three main steps. First, Multiscale Self Attention convolutional neural network (CNN) is used for image inpainting and augmentation to fill missing or damaged areas and generate augmented images for training DL models. Second, Anisotropic Diffusion Filtering is used for speckle noise reduction, and the filter parameters are adapted to local noise characteristics using memory-based speckle statistics. Third, the CNN is trained to estimate local statistics of the speckle noise and adapt filtering parameters accordingly to capture local and global image features. The effectiveness of the proposed approach is evaluated on a prenatal 5th-month ultrasound scan dataset. The results demonstrate that the proposed preprocessing steps significantly improve the quality of ultrasound images and lead to better performance of DL models. The proposed preprocessing pipeline using Multiscale Self Attention CNN for image inpainting and augmentation, followed by Anisotropic Diffusion Filtering and memory-based speckle statistics for speckle noise reduction, can significantly enhance the quality of prenatal ultrasound images and enhance the accuracy of diagnostic models. The approach has potential for broader use in medical imaging applications. [ABSTRACT FROM AUTHOR]

The Chekanov-Eliashberg differential graded algebra of a Legendrian knot L is a rich source of Legendrian knot invariants, as is the theory of generating families. The set P(L) of homology groups of augmentations of the Chekanov-Eliashberg algebra is an invariant, as is a count of objects from the theory of generating families called graded normal rulings. This paper gives two results demonstrating the usefulness of computing the homology group of an augmentation using a combinatorial interpretation of a generating family called a Morse complex sequence (MCS). First, we show that if the projection of L to the xz-plane has exactly 4 cusps, then |P(L)| ≤ 1. Second, we show that two augmentations associated to the same graded normal ruling by the many-to-one map between augmentations and graded normal rulings defined by Ng and Sabloff [The correspondence between augmentations and rulings for Legendrian knots, Pacific J. Math. 224(1) (2006) 141-150] need not have isomorphic homology groups. [ABSTRACT FROM AUTHOR]

The number of breast augmentation surgeries (BAS) has increased. Therefore, the number of breast cancer patients with history of BAS has also increased. In this paper, we present two cases of sentinel lymph node biopsy (SLNB) in patients with previous BAS who were diagnosed with breast cancer. The patients were augmented using different approach; the first case was augmented through transaxillary incision, whereas the second case was augmented through periareolar incision. Lymphoscintigraphy (LPG) was performed on the patients 1 day prior to operation, enabling confirmation of lymphatic flow and SLN in both patients. SLNB was successfully performed in both cases. In one patient, SLNB was performed using indocyanine green (ICG) fluorescence and the Photodynamic Eye (PDE) system. Regardless of history of BAS, ICG and PDE system showed lymphatic flow and SLN in real time. LPG and ICG fluorescence were useful methods for SLN detection in patients with previous BAS, being able to confirm lymph flow before operation. Biopsy methods using LPG and PDE system were considered useful for difficult confirmation of lymph flow after breast augmentation. This is the first report of SLNB using ICG and PDE system for patients with previous BAS. [ABSTRACT FROM AUTHOR]

ABSTRACT Traditionally, wind turbine dynamics are analyzed using computationally efficient but geometrically coarse aeroelastic models. With ever larger offshore turbines being installed in deeper waters, the wind industry is gradually moving toward more complex foundation types such as jackets and tripods. Even the simplest models of such structures have many more degrees of freedom (DoFs) than the complete wind turbine model, leading to excessive computation times. To cope with this, we can employ reduced 'superelement' modeling of the support structure. However, since these structures are subjected to hydrodynamic loading at a large portion of their DoFs, traditional reduction methods fail to properly describe the response to this excitation. In this paper, we therefore propose to combine superelement modeling with the concept of modal truncation augmentation, which consists in extending the reduction basis by adding 'residual vectors'. Furthermore, we use principal component analysis to find the predominant hydrodynamic loading on the support structure. A case study is performed on a reference wind turbine model on a jacket structure, revealing both the need for coupled dynamic analysis and the shortcomings of traditional superelement models for offshore support structures. Most importantly, this case study shows that the proposed augmented superelement approach allows to create very compact yet accurate models of the complex support structure, thereby enabling efficient integrated simulation of offshore wind turbines.Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Previous comparative reviews of add-on therapies for patients with major depressive disorder (MDD) with an inadequate response to antidepressants have not used meta-analytic techniques to compare different drug classes and have included non-licensed therapies. This meta-analysis reviewed all published peer-reviewed evidence for the efficacy of EU-licensed therapies in patients with MDD and an inadequate response to antidepressant monotherapy. Papers concerning randomized clinical trials (RCTs) were identified using criteria from the Cochrane Handbook for Systematic Reviews of Interventions. Add-on therapies reviewed were antidepressants, quetiapine XR, lithium, and S-adenosyl-l-methionine (SAMe). Seven RCTs that reported response and remission in a way that allowed quantitative analysis were included in this meta-analysis. Comparison of the different drug classes indicated that most interventions had similar efficacy. The likelihood of response was significantly greater with SAMe versus placebo and lithium and with quetiapine XR versus placebo. Most add-on interventions demonstrated comparable efficacy in patients with MDD and an inadequate response to initial antidepressants. However, there is currently a paucity of high-quality data regarding the use of add-on treatments in patients with MDD who are inadequate responders to antidepressants, with quetiapine XR presenting the most comprehensive evidence base to date. [ABSTRACT FROM PUBLISHER]

The rapid increase of technologically enhanced listening platforms gives listeners access to music with ever-increasing ease and ubiquity, giving rise to the suggestion that we should now conceptualize music as a resource similar to water; something that is utilized to achieve everyday goals. This paper proposes that music is a utilitarian resource employed by listeners to augment cognitive, emotional, behavioral, and physiological aspects of the self. To better explore these notions this paper examines the potential role of the “functions of music,” first espoused by Alan P. Merriam in 1964. Merriam suggested music has a situational use and an underlying function (music’s ability to alter the self through listening). The research presented here asserts that listeners interact with specific musical materials to achieve or orientate themselves towards contextually-rooted goals. Reinforcing Tia DeNora’s suggestion that music is a “technology of the self” this research presents the results of a 41 publication meta-analysis exploring the possible functions of music. The resultant Aggregate Thematic Functions Framework (ATF framework) identifies 45 possible utilitarian functions of music, spread across five domains of action. The framework also proposes a meta-domain and an emotional sub-domain.

Copyright of Der MKG-Chirurg is the property of Springer Nature 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.)

Copyright of Czech Dental Journal / Ceská Stomatologie a Praktické Zubni Lékarstvi is the property of Czech Dental Chamber 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.)

Technological innovation in digital communications, epitomised in the shift from the informational web (Web1.0) to the interactional web (Web2.0), provokes new opportunities and challenges for social research. Web2.0 technologies, particularly the new social media (e.g. social networking, blogging and micro-blogging) as well as the increased accessibility of theWorld Wide Webthrough highly portable and prevalent devices like smart phones, tablets and netbooks generates new forms of data which are of significance for social research as well as new methods and techniques for analysing this kind of data. Even though we are in the midst of this rapid innovation, it is nonetheless possible to distinguish three basic lines of argument about its current and prospective impact on social research. Some commentators suggest this innovation generates methods and data that can act as asurrogatefor more traditional quantitative and qualitative research designs such as experiments, sample surveys and in-depth interviews. Others argue that digital communication technologiesre-orientatesocial research around new objects, populations and techniques of analysis. It can also be argued that digital social researchaugments, but needs to be used in conjunction with, more traditional methods. C. WrightMills’ classic statement ofThe Sociological Imaginationis used to clarify the distinctive contribution of digital social research; what can it do that traditional methods cannot in understanding how social relations are constituted, how they can change and how they generate social identities. It is argued that digital social research, particularly the analysis of new social media, is distinctive in capturing naturally occurring or ‘user-generated’ data at the level of populations in real or near-real-time. Consequently, it offers the hitherto unrealisable possibility of studying social processes as they unfold at the level of populations as contrasted with their official construction through the use of ‘terrestrial’ research instruments and curated data-sets. Realising this research potential entails the development of digital ‘observatories’ such as theCardiff Online Social Media ObServatory. The paper concludes with a discussion of the political and ethical, as well as the technological, implications of observatories, focusing in particular on tensions between the ‘panoptic’ and ‘synoptic’ powers of digital observatories and the allied possibilities of a ‘signature science’. [ABSTRACT FROM AUTHOR]

Rotator cuff tears are a common cause of debilitating pain, reduced shoulder function, and weakness. Despite improvements in the understanding of the disease process and advances in surgical treatment, recent studies have reported that healing of massive rotator cuff tears is not as predictable as that of smaller rotator cuff tears. These high failure rates are a result of both mechanical and biologic factors that may affect the patients' intrinsic capacity to heal. Most research studies have concentrated on tissue engineering as a means for improvement of healing in rotator cuff repair, including the use of scaffolds, growth factors, and mesenchymal stem cells. There has been much interest in the development of various scaffolds that provide adequate strength as well as stimulate and enhance healing potential. The purpose of this paper is to review the current basic science and clinical application of extracellular matrix scaffolds, which are currently the most widely used scaffolds for repair of rotator cuff tears. [ABSTRACT FROM AUTHOR]

Objective: Obsessive-compulsive disorder (OCD) is a common psychiatric illness with a lifetime prevalence in the general population of approximately 2-3%. Serotonin reuptake inhibitors (SRIs) and cognitive-behavioral therapy (CBT) in the form of exposure and response prevention (ERP) both represent first-line treatments for OCD. However, unsatisfactory response to these treatments is very common and the evaluation of next-step treatment strategies is highly relevant. The purpose of this paper is to review available data on treatment-resistant OCD and to build a treatment algorithm for those patients who fail to respond to a first SRI trial. Method: We carried out a search on MEDLINE/PUBMED database, selecting meta-analyses, systematic reviews and randomized controlled studies written in English on treatment-resistant OCD. We also considered open-label studies and case series and/or reports, written in English. We reviewed the available evidence for different strategies and tried to delineate an evidence-based treatment algorithm for clinicians. Results: Antipsychotic addition to SRIs and CBT augmentation of drug treatment both are supported by a number of double-blind studies, although differences between antipsychotics seem to exist and the effectiveness of routinely delivered CBT as an adjunct to medication in real world OCD patients with incomplete response to medication need to be replicated. The switch to IV administration of clomipramine may be clinically useful in some cases, although the return to oral formulation often is associated with a relapse. Switching to other first-line agents or to other compounds (such as venlafaxine) is supported by open-label studies or by double-blind studies without a placebo arm. Conclusions: Several evidence-based effective strategies are available to clinicians in case of treatment-resistant OCD. Strengths and limitations of each of the effective strategies are still under study and will be the focus of future comparative trials. There is also a strong need for alternative therapeutic options for OCD patients. [ABSTRACT FROM AUTHOR]

2-Edge connectivity is an important fault tolerance property of a network because it maintains network communication despite the deletion of a single arbitrary edge. Planar spanning subgraphs have been shown to play a significant role for achieving local decentralized routing in wireless networks. Existing algorithmic constructions of spanning planar subgraphs of unit disk graphs (UDGs) such as Minimum Spanning Tree, Gabriel Graph, Nearest Neighborhood Graph, etc. do not always ensure connectivity of the resulting graph under single edge deletion. Furthermore, adding edges to the network so as to improve its edge connectivity not only may create edge crossings (at points which are not vertices) but it may also require edges of unbounded length. Thus we are faced with the problem of constructing 2-edge connected geometric planar spanning graphs by adding edges of bounded length without creating edge crossings (at points which are not vertices). To overcome this difficulty, in this paper we address the problem of augmenting the edge set (i.e., adding new edges) of planar geometric graphs with straight line edges of bounded length so that the resulting graph is planar and 2-edge connected. We provide bounds on the number of newly added straight-line edges, prove that such edges can be of length at most 3 times the max length of an edge of the original graph, and also show that the factor 3 is optimal. It is shown to be NP-Complete to augment a geometric planar graph to a 2-edge connected geometric planar graph with the minimum number of new edges of a given bounded length. We also provide a constant time algorithm that works in location-aware settings to augment a planar graph into a 2-edge connected planar graph with straight-line edges of length bounded by 3 times the longest edge of the original graph. It turns out that knowledge of vertex coordinates is crucial to our construction and in fact we prove that this problem cannot be solved locally if the vertices do not know their coordinates. Moreover, we provide a family of k-connected UDGs which does not have 2-edge connected spanning planar subgraphs, for any . [ABSTRACT FROM AUTHOR]

This paper deals with development of a new aerobic-anaerobic bioremediation technology for impassable bogs polluted with oil in the North part of the Western Siberia, because traditional remediation technologies are impossible technically and economically not favorable there. [ABSTRACT FROM AUTHOR]

This article discusses one of several reasons for the loss of augmentation in English absolute constructions (ACs): the emergence of the verbal gerund (VG) in - ing. The VG was from the start strongly associated with prepositional contexts (de Smet, 2008) and has been shown to have a history connected to the AC (Fanego, 2004). More specifically, the strong association between VGs and prepositions probably caused a reinterpretation of most prepositionally augmented ACs as VGs, resulting in a functional split (Fonteyn & van de Pol, 2016). Second, this paper illustrates how the what with -construction underwent a very similar development, albeit in the opposite direction. Indeed, it appears that the grammaticalization of with into an AC-marker (van de Pol & Hoffmann, 2016), and the resulting strong association between with and ACs, may have caused the formally similar what with -construction to be reanalyzed as an AC. As a result, this study serves as an example of how category borders are often gradient (see also Malchukov, 2004, 2006) and how such gradient boundaries may cause the (bidirectional) reanalysis of constructions close to the border, which in turn may impact the larger overarching constructions involved. • More augmentors were historically available, but with is the only common one in PDE. • The rise of the verbal gerund has influenced the development of augmented absolutes. • What with gerunds with overt subjects came to be reanalyzed as absolutes in PDE. • Additional PDE augmentors might develop to help process concessional absolutes. [ABSTRACT FROM AUTHOR]

The use of exotic (=alien) arthropods in classical and augmentative biological control programs has yielded huge economic and ecological benefits. Exotic species of arthropods have contributed to the suppression of key pests in agriculture and forestry or have aided in restoring natural systems affected by adventive species. However, adverse non-target effects of exotic biological control agents have been observed in a number of projects. Non-target effects range from very small effects, e.g. 2% parasitization on a non-target insect on a local level, to massive effects on a large scale. Until now, no consensus on how to judge the magnitude of non-target effects and whether these effects can be tolerated or are unacceptable has emerged. In this paper, we briefly review both the benefits of biological control as well as the associated risks including to human and animal health, plant health and particularly the environment. We also make an attempt at identifying the major challenges for assessing risks and for balancing benefits and risks. There is general agreement that sound risk assessment procedures should precede the release of exotic invertebrate biological control agents and a recent shift-especially for arthropod biological control-from introductions done without meaningful risk assessment studies to projects conducting thorough host range testing can be observed. However, overly stringent regulations that would preclude promising agents from being developed must be avoided. [ABSTRACT FROM AUTHOR]

This paper extends the scedasticity comparison among several groups of observations, usually complying with the homoscedastic and the heteroscedastic cases, in order to deal with data sets laying in an intermediate situation. As is well known, homoscedasticity corresponds to equality in orientation, shape and size of the group scatters. Here our attention is focused on two weaker requirements: scatters with the same orientation, but with different shape and size, or scatters with the same shape and size but different orientation. We introduce a multiple testing procedure that takes into account each of the above conditions. This approach discloses a richer information on the data underlying structure than the classical method only based on homo/heteroscedasticity. At the same time, it allows a more parsimonious parametrization, whenever the patterned model is appropriate to describe the real data. The new inferential methodology is then applied to some well-known data sets, chosen in the multivariate literature, to show the real gain in using this more informative approach. Finally, a wide simulation study illustrates and compares the performance of the proposal using data sets with gradual departure from homoscedasticity. [ABSTRACT FROM AUTHOR]

Abstract Background Suture-button (SB) fixation has been widely performed for syndesmosis injuries, but it has been reported unstable in some biomechanical studies. The purpose of this study was to evaluate the stability of the syndesmosis using SB fixation with anterior inferior tibiofibular ligament augmentation using suture-tape (ST). Methods Eight normal fresh-frozen cadaveric legs were used. After initial tests of intact and injured models, SB fixation, SB fixation with ST augmentation, ST augmentation alone, and screw fixation were performed sequentially for each specimen. Loading tests stimulating dorsiflexion, inversion, and external rotation of the ankle joint were performed for each model. The tibiofibular diastasis (TFD) and the fibular rotational angle related to the tibia (FRA) were measured using a magnetic tracking system. Findings In the injured model, both TFD and FRA increased significantly compared with the intact model in all directions (P <.05). In the SB fixation model, TFD and FRA generally showed significant increases (P <.05, except for TFD in external rotation). In the SB fixation with ST augmentation model and ST augmentation alone, TFD and FRA were not significantly different compared with the intact model (P >.05). In the screw fixation model, FRA with inversion force at the ankle was significantly decreased compared with the intact model (P =.027). Interpretation SB fixation alone did not provide stability of the syndesmosis, and screw fixation became too rigid compared with the intact model. Using ST augmentation achieved dynamic stability similar to the intact model for syndesmotic injuries. Highlights • The stability in various surgical methods for syndesmosis injuries was evaluated. • Suture-button fixation alone did not provide stability of the syndesmosis. • Screw fixation became too rigid. • Suture-tape augmentation achieved dynamic stability for syndesmosis injuries. [ABSTRACT FROM AUTHOR]

This paper summarizes substantive new results derived by a student team (the first three authors) under the direction of the fourth author at the 2008 session of the KSU REU "Brainstorming and Barnstorming". The main results show that the construction of the inner automorphism group of a quandle gives rise to a functor from the category of quandles and surjective quandle homomorphisms to the category of groups, characterize quotient maps of quandles which do not change the group of inner automorphims, and characterize those normal subgroups of the inner automorphism group which arise as kernels of homomorphisms induced by quandle surjections. [ABSTRACT FROM AUTHOR]

The anatomic approach is gaining popularity in anterior cruciate ligament (ACL) reconstruction. It is predominantly applied during primary ACL reconstruction. However, following the same principles as during primary surgery, the anatomic approach can also be applied during revision and augmentation surgery. This paper discusses the surgical technique for anatomic single- and double-bundle ACL reconstruction, for primary, revision and augmentation surgery. During primary reconstruction, the choice for single- or double-bundle reconstruction and graft size should be based on ACL insertion site and femoral intercondylar notch dimensions. When there is an isolated anteromedial (AM) or posterolateral (PL) bundle rupture, augmentation of a single-bundle can be performed while protecting the integrity of the intact bundle. Especially during revision surgery, there are many potential situations the surgeon may encounter when entering the knee. There are multiple possible solutions for all of these different situations leading to an anatomic end result. Three-dimensional computed tomography (CT) scanning should be used to evaluate the current tunnel positions and determine the operative strategy. [ABSTRACT FROM AUTHOR]