Augmentation Strategies Research Articles

POLYCORE: Polygon-based contour refinement for improved Intravascular Ultrasound Segmentation

Segmentation of the coronary vessel wall in intravascular ultrasound is a fundamental step in guiding coronary intervention. However, it is an challenging task, even for highly skilled cardiologists, due to image artefacts and shadowed regions caused by calcified plaque, guide wires and vessel side branches. Recently, dense-based neural networks have been applied to this task, however, they often fail to predict anatomically plausible contours in these low-signal areas. We propose a novel methodology called Polygon-based Contour Refiner (POLYCORE) that addresses topological error in dense-based segmentation networks using a relational inductive bias through higher-order connections between vertices to learn anatomically rational contours. Our approach remedies the over-smoothing phenomena common in polygon networks by introducing a new vector field refinement module which enables pixel-level detail to be added in an iterative process. POLYCORE is enhanced with augmented polygon aggregation which we show is more effective than typical dense-based test-time augmentation strategies. We achieve state-of-the-art results on two diverse datasets, observing particular improvements when segmenting the lumen structure and in topologically-challenging regions containing shadow artefacts. Our source code is available here: http://orcid.org/https://github.com/kitbransby/POLYCORE.

Multi-vendor validation of automated left ventricular analysis from 3D echocardiography using artificial intelligence

Abstract Background Accurate echocardiographic assessment of left ventricular (LV) function is dependent on both image acquisition and analysis. Although 3D echocardiography (3DE) is increasingly recommended for volume quantification, manually derived volumes are limited by operator subjectivity, variable image quality, and the lengthy duration of analysis. Consequently, several automated approaches based on artificial intelligence (AI) have been proposed to segment the LV cavity from 3DE and have demonstrated better accuracy and reproducibility compared to human experts. While the utility of AI models in the clinic depends on their ability to generalise to different populations and ultrasound vendors, the external validation of developed models remains limited. Purpose This study evaluates the ability of an existing AI model trained using data from a single vendor to accurately estimate routine 3DE LV indices in an independent external dataset acquired using equipment from a different vendor. Methods A previously published and internally validated deep learning model for segmentation of the LV cavity and myocardium from 3DE was used to automatically derive LV indices including end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and ejection fraction (EF) [1]. A second independent dataset consisting of paired 3DE and CMR images from 65 marathon runners from a different centre (using equipment from a different vendor) was subsequently used for external validation. Images from both datasets were subjectively scored from 1 (poor) to 5 (excellent) as a measure of image quality. Agreement with CMR as the reference was assessed using a two-way mixed effects intraclass correlation coefficient (ICC), supplemented by Bland-Altman analysis, to determine the model bias (between 3DE and CMR) and 95% limits of agreement (LOA). To assess whether performance could be improved by inclusion of multi-vendor data during training, the initial model was fine-tuned using a small subset (n=9) of images from the second centre. Results The mean image quality score from the second dataset was 2.5 (± 1.1), which was significantly lower than the mean of 3.4 (± 1.0) from the first dataset. Despite vendor and image quality differences, the initial model produced good agreement with CMR in EDV, ESV, and LV mass but only poor agreement in EF (Fig 1). Fine tuning greatly improved agreement in EF with reduced bias and narrower 95% LOA (Fig 2). Conclusion With the addition of a small number of cases for fine-tuning, AI models for automated LV analysis can achieve good performance analysing 3DE images acquired from a different vendor and population. Advancing model generalisability through more sophisticated data augmentation strategies may enable truly vendor-agnostic automated LV analysis from 3DE.

Intelligent Manufacturing in Wine Barrel Production: Deep Learning-Based Wood Stave Classification

Innovative wood inspection technology is crucial in various industries, especially for determining wood quality by counting rings in each stave, a key factor in wine barrel production. (1) Background: Traditionally, human inspectors visually evaluate staves, compensating for natural variations and characteristics like dirt and saw-induced aberrations. These variations pose significant challenges for automatic inspection systems. Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. To enhance performance and accuracy, we employ a data augmentation strategy using cropping techniques to address intra-class variability in individual staves. (3) Results: Our approach significantly improves accuracy and reliability in classifying wood with irregular textures and heterogeneity. The use of explainable AI and model calibration offers a deeper understanding of the model’s decision-making process, ensuring robustness and transparency, and setting confidence thresholds for outputs. (4) Conclusions: The proposed system enhances the performance of automatic wood inspection technologies, providing a robust solution for industries requiring precise wood quality assessment, particularly in wine barrel production.

Advancing ketamine in the treatment hierarchy for refractory depression.

Evidence indicates that ketamine is highly effective, has a lower side effect profile and is better tolerated compared to many augmentation strategies for refractory depression. This, combined with data on psychiatric treatment outcome mediators, suggests that earlier intervention with ketamine could improve outcomes for patients suffering from refractory depression.

Comorbidity, Treatment, and Service Utilization Patterns in Difficult-to-Treat Depression Patients: A Retrospective Study in a Portuguese Community Mental Health Team

Background and Objectives: Observational studies with data from real-world clinical practice with patients with difficult-to-treat depression (DTD) are rare. This study aims to collect observational data from the real-world clinical practice of a Portuguese community mental health team (CMHT) on the prevalence of DTD and to explore differences between DTD and non-DTD groups. Materials and Methods: We conducted a retrospective chart review study using data from Electronic Health Records (EHRs) of adult patients with psychiatric disorders followed by a CMHT from the Department of Psychiatry of the Coimbra Local Health Unit (between 1 December 2020–31 December 2022). The Dutch Measure for quantification of Treatment Resistance in Depression (DM-TRD) was used to assess the degree of treatment resistance and the Charlson Comorbidity Index (CCI) to measure medical comorbidity. Results: A quantity of 473 patients were referred to Cantanhede CMHT for a first assessment. Of these, 219 patients met the criteria for a primary diagnosis of any depressive disorder. Assistant psychiatrists identified 57 patients with DTD during follow-up (approximately 26%). The DTD group had higher rates of depressive episodes, greater depression severity, increased service use, higher DM-TRD scores, and a higher prevalence of comorbid anxiety symptoms, personality disorders, and severe medical comorbidities. The DTD group also had a higher prescription rate of antidepressants. Differences were observed in the use of antidepressant augmentation strategies and in the prescription of anticoagulant/antiplatelet drugs and analgesics, with higher prescription rates in the DTD group. We found correlations between DM-TRD and CCI scores, and between DM-TRD scores and all service use variables. Conclusions: Our results are consistent with a similar study in the United Kingdom, highlighting the need for a different approach to the management of DTD patients, who continue to live with a significant burden despite usual pharmacological and non-pharmacological treatments.

Deep learning-powered efficient characterization and quantification of microplastics

Characterizing and quantifying microplastics (MPs) are time-consuming and labor-intensive tasks traditionally. This paper presents an artificial intelligence (AI) framework aiming to automate these tasks by integrating computer vision and deep learning techniques. The approach leverages Fourier Transform Infrared (FTIR) spectra and visual images. Primary novelties of this research involve the development of: (1) an AI framework integrating efforts of data processing, analytics, visualization, and human-computer interaction; (2) a method for transforming FTIR data into contour images; (3) data augmentation strategies for resolving data scarcity and imbalance issues; (4) deep learning models for identifying MPs; (5) computer vision algorithms for quantifying MPs; and (6) an engineer-friendly graphic user interface (GUI) for enhancing data accessibility. The AI framework has been applied to polyethylene, polypropylene, polystyrene, polyamide, ethylene-vinyl acetate, and cellulose acetate. Results confirmed the efficacy of the framework, exhibiting high accuracy scores in classification (98 %), segmentation (99 %), and quantification (96 %) tasks. This research advances the capability of automatic assessment of MPs.

Child, family and professional views on valued communication outcomes for non-verbal children with neurodisability: A qualitative meta-synthesis.

There are many children with neurodisability who are unable to rely on speech to communicate and so use a range of augmentative and alternative communication (AAC) methods and strategies to get their message across. Current instruments designed to measure the outcomes of speech and language therapy interventions lack specific attention to communication outcomes that are valued by non-verbal children with neurodisability, their families and support networks. This qualitative meta-synthesis was conducted to identify valued communication outcomes to inform the next stage of developing a novel outcome measure. To systematically identify and synthesise the qualitative evidence about which communication outcomes non-verbal children with neurodisability, their family members, healthcare professionals and educators think are important to achieve, specifically which communication outcomes are most valued by: (1) non-verbal children with neurodisability; (2) parents or other family members of non-verbal children with neurodisability; and (3) professionals who work with non-verbal children with neurodisability. A systematic search of bibliographic databases and the grey literature was undertaken to identify qualitative studies that included evidence of views expressed by children, family members, healthcare professionals and educators on outcomes in relation to the communication of non-verbal children with neurodisability. All papers meeting the inclusion criteria were quality appraised using the Critical Appraisal Skills Programme Qualitative checklist, although none were excluded on this basis. The data synthesis involved organising coded data into descriptive themes which were then synthesised into analytical themes. We found 47 papers containing qualitative data meeting the inclusion criteria from research situated in 14 countries. The views of 35 children, 183 parents, six other family members, 42 healthcare professionals and 18 educators are represented in the review. The included studies contained very few data reported by children themselves; most data were provided by adults, especially parents. Three main analytical themes were identified: Experiences of communication and expectations; adapting to and acceptance of AAC; and becoming an autonomous communicator. This meta-synthesis brings together the limited qualitative research findings about what parents, professionals and children consider are important communication outcomes for non-verbal children with neurodisability. The synthesis identifies key gaps in our knowledge about the perspectives of children and their siblings. This synthesis will inform primary research to understand valued communication outcomes in this group, and ultimately the development of a patient-reported outcome measure (PROM) that can be used to demonstrate the effect of interventions, at both clinical and service levels. What is already known on the subject Studies of children with cerebral palsy and autism spectrum disorder indicate that at least 25% of children with these conditions are non-verbal. Studies on the health outcomes of children with neurodisability have identified that communication is rated as important by parents and health professionals. There is an evidence gap about which communication outcomes are important to non-verbal children, their families and the people who work with them. What this paper adds to the existing knowledge This is the first synthesis of data that relates to communication outcomes for non-verbal children with neurodisability. This qualitative meta-synthesis identifies from previous research studies the communication outcomes valued by children who are non-verbal, their parents or other family members, and the professionals who work with them. The findings will be used to shape further primary research and the development of a novel patient-reported communication outcome measure for non-verbal children with neurodisability. It is anticipated that this will be used by clinicians to measure the effect of their interventions. What are the practical and clinical implications of this work? Clinicians should reflect on parents' experiences of communication with their child before discussing potential outcomes with them. Gaining insight into the lived experience of communication for non-verbal children and their families will help healthcare professionals to understand which goals are important to them and why. Few studies have specifically asked which communication outcomes are important for non-verbal children with neurodisability. Further exploration is needed to determine which communication outcomes non-verbal children and their families would like to see included in outcome measures used by clinicians.

Assessment of Computer-Aided Translation Quality Based on Large-Scale Corpora

CAT technology, utilizing translation memory and quality control tools, boosts translation efficiency and consistency. Yet, it faces challenges with cultural nuances, context, and creativity, requiring human intervention. This study explores leveraging large-scale corpora to enhance machine translation quality assessment accuracy and efficiency. Automated evaluation models surpass manual limitations, enhancing translation system performance and decision-making. Supervised and unsupervised machine translation quality assessment methods, coupled with data augmentation strategies, reduce model bias by reinforcing the connection between source and target languages. The proposed model achieves 94.8% and 92.6% correctness on Google Translate and Wikipedia datasets, slightly surpassing manual rates. Data augmentation and cascade evaluation notably improve translation evaluation accuracy, especially with large-scale corpora. Future research targets higher-quality pseudo-labeled data, refined evaluation models, and advanced assessment methods for diverse translation needs.

Improving the classification of rock samples with diffusion probabilistic machine learning

Machine learning is a powerful data-driven technology for regression and classification tasks in numerous branches of science and technology. With probabilistic machine learning, we can approximate the probability density function of a given digital data set, unlocking the sampling of new and realistic data. We implement probabilistic machine learning as a statistical-based data augmentation tool to effectively improve the performance of a classifier on scarce data sets. Limited data are the norm in most practical situations, as is the case with core samples retrieved from unconventional oil and gas reservoirs. Obtaining and properly labeling rock sample images is a manual and time-consuming task. This leads to scarce data sets that make the lithologic classification of rock samples challenging. We illustrate the technology on images taken from well cores from the Argentinean Neuquén and Austral basins, where by introducing generated samples in the training stage of a machine-learning classifier, we achieve a 5% higher accuracy with respect to the baseline obtained using the same number of samples from the original data set. We reach these results by proposing a lightweight workflow that can be run on very limited hardware. Our workflow is an alternative to classical data augmentation strategies for dealing with incomplete information, such as geometric transformations.

Exploring children's preferences for graphic symbols to represent pain‐related words

AbstractChildren who are hospitalized may sometimes not be able to communicate verbally to self‐report their pain or other symptoms due to medical conditions, medical interventions, or communication difficulties. As such, these children may need other means, such as augmentative and alternative communication (AAC) strategies, in this case, graphic symbols, to express their pain‐related experiences and receive applicable treatment. Choosing suitable graphic symbols to represent pain‐related words contributes to the effective use and implementation of visual support. This study explored the preferences of 6.0–9.11‐year‐old (years; months) children with typical development regarding graphic symbols to represent pain‐related words. These symbols were selected from two commonly used and widespread symbol resources: Picture Communication Symbols (PCS®) and Aragonese Portal of Augmentative and Alternative Communication (ARASAAC) symbols. A descriptive, quantitative study design was employed, including a total of 30 typically developed South African children. Data were collected by means of an electronic questionnaire and analyzed using descriptive and inferential statistics. Probability values were determined and predictions, as well as inferences, were implemented. The results showed that the children preferred ARASAAC symbols to represent most pain‐related words (p < 0.001). It is important to consider stakeholders' (in this case, children's) input on their preferences in designing communication support to enable participation during the clinical decision‐making process.

Augmentation strategy toward superior water absorption performance: An attempt utilizing [EMIm]Ac ionic liquid/water pair

Ionic liquids (ILs) offer significant advantages as alternative absorbents in absorption refrigeration cycles due to their superior physicochemical properties. Assessing the performance of new IL-based working pairs in absorption systems is essential for their rapid application in industrial applications. This study evaluates the water absorption performance of an aqueous solution of 1-ethyl-3-methylimidazolium acetate ([EMIM]Ac) under both saturated atmospheric and vacuum conditions. Additionally, the performance of an absorption chiller utilizing the [EMIM]Ac/H2O working pair was analysed using the Aspen Plus process simulator. A discrepancy was observed between the water vapor pressure in the air and the saturated vapor pressure of the aqueous solution at equilibrium. A theoretical criterion for the minimum relative humidity required for effective water vapor absorption by this absorbent at a given mass fraction is proposed. Under vacuum conditions, lower absolute pressure or higher mass fraction enhances absorption. Surfactants significantly improve absorption performance, with 2-octanol and 1-octanol demonstrating approximately double the absorption capacity compared to [EMIM]Ac. Higher mass fractions of the aqueous solution enhance the absorption rate but reduce the desorption rate. The coefficient of performance (COP) of the system decreases with increasing pressure and mass fraction, reaching a peak of 0.9 at a mass fraction of 20 % and a pressure of 3 kPa. Lower pressure and mass fraction enhance exergy efficiency.

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 m6A reader protein

Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitors for a target is limited, data augmentation strategies are often preferred to enhance model performance. In this study, we developed predictive machine learning models for structure-based drug discovery leveraging multiple traditional machine learning algorithms trained with target and ligand dynamics-aware datasets.To illustrate our approach, we present a composite model that combines classification and regression to predict YTHDF1 inhibitors, utilizing PLEC features. YTHDF1, a key m6A reader protein involved in mRNA translation, is implicated in various cancers, making it a promising therapeutic target. Traditional structure-based virtual screening (SBVS) using generic scoring functions has struggled to identify potent YTHDF1 inhibitors due to the protein's unique binding characteristics. To overcome this, we developed YTHDF1-specific machine learning scoring functions (MLSFs) to enhance SBVS efficacy.We employed various data augmentation techniques to generate a comprehensive dataset, incorporating multiple conformations of ligands and the YTHDF1 protein. We have trained 64 YTHDF1-specific MLSFs using four machine learning algorithms and evaluated them on ten test sets, focusing on their predictive and ranking power. Our results demonstrate that the artificial neural network with protein-ligand extended connectivity fingerprints (ANN-PLEC) outperforms other MLSFs, consistently achieving high area under the precision-recall curve (PR-AUC) of 0.87. This method shows promise for targets with limited quantities of active molecules, providing a viable path forward for drug discovery research. The ANN-PLEC scoring function is made freely available on GitHub for other researchers to access and utilize https://github.com/JuniML/SBVS-YTHDF1/.

LLM-enhanced Composed Image Retrieval: An Intent Uncertainty-aware Linguistic-Visual Dual Channel Matching Model

Composed image retrieval (CoIR) involves a multi-modal query of the reference image and modification text describing the desired changes, allowing users to express image retrieval intents flexibly and effectively. The key of CoIR lies in how to properly reason the search intent from the multi-modal query. Existing work either aligns the composite embedding of the multi-modal query and the target image embedding in the visual domain through late-fusion or converts all images into text descriptions and leverage large language models (LLM) for text semantic reasoning. However, this single-modality reasoning approach fails to comprehensively and interpretably capture the users’ ambiguous and uncertain intents in the multi-modal queries, incurring the inconsistency between retrieved results and ground truth. Besides, the expensive manually-annotated datasets limit the further performance improvement of CoIR. To this end, this paper proposes an LLM-enhanced Intent Uncertainty-aware Linguistic-Visual Dual Channel Matching Model (IUDC), which combines the strengths of multi-modal late-fusion and LLMs for composed image retrieval. We first construct an LLM-based triplet augmentation strategy to generate more synthetic training triplets. Based on this, the core of IUDC consists of two matching channels: the semantic matching channel is responsible for intent reasoning on the aspect-level attributes extracted by an LLM, and the visual matching channel accounts for the fine-grained visual matching between multi-modal fusion embedding and target images. Considering the intent uncertainty presented in the multi-modal queries, we introduce Probability Distribution Encoder (PDE) to project the intents as probabilistic distributions in the two matching channels. Consequently, a mutually enhanced module is designed to share knowledge between the visual and semantic representations for better representation learning. Finally, the matching scores of two channels are added to retrieve the target image. Extensive experiments conducted on two real datasets demonstrate the effectiveness and superiority of our model. Notably, with the help of the proposed LLM-based triplet augmentation strategy, our model achieves a new record of state-of-the-art performance among all datasets.

Enabling personalized smart tourism with location-based social networks

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes. However, applying LBSNs to smart tourism is a challenge due to the need to deal with complex multi-source information modeling and tourism data sparsity. In this article, to fully harness the potential of LBSNs using deep learning technologies, we propose an knowledge-driven personalized recommendation method for smart tourism. Representation learning techniques can effectively modeling the contextual information (e.g., time, space, and semantics) in LBSNs, while the data augmentation strategy of contrastive learning techniques can explore user personalized travel behaviors and alleviate data sparsity. To demonstrate the effectiveness of the proposed approach, we conducted a case study on trip recommendation. Furthermore, the patterns of human mobility are revealed by exploring the effect of contextual data and tourist potential preferences.

Empowering Brain Tumor Diagnosis through Explainable Deep Learning

Brain tumors are among the most lethal diseases, and early detection is crucial for improving patient outcomes. Currently, magnetic resonance imaging (MRI) is the most effective method for early brain tumor detection due to its superior imaging quality for soft tissues. However, manual analysis of brain MRI scans is prone to errors, largely influenced by the radiologists’ experience and fatigue. To address these challenges, computer-aided diagnosis (CAD) systems are more significant. These advanced computer vision techniques such as deep learning provide accurate predictions based on medical images, enhancing diagnostic precision and reliability. This paper presents a novel CAD framework for multi-class brain tumor classification. The framework employs six pre-trained deep learning models as the base and incorporates comprehensive data preprocessing and augmentation strategies to enhance computational efficiency. To address issues related to transparency and interpretability in deep learning models, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize the decision-making processes involved in tumor classification from MRI scans. Additionally, a user-friendly Brain Tumor Detection System has been developed using Streamlit, demonstrating its practical applicability in real-world settings and providing a valuable tool for clinicians. All simulation results are derived from a public benchmark dataset, showing that the proposed framework achieves state-of-the-art performance, with accuracy approaching 99% in ResNet-50, Xception, and InceptionV3 models.

Multi-Scale Frequency-Adaptive-Network-Based Underwater Target Recognition

Due to the complexity of underwater environments, underwater target recognition based on radiated noise has always been challenging. This paper proposes a multi-scale frequency-adaptive network for underwater target recognition. Based on the different distribution densities of Mel filters in the low-frequency band, a three-channel improved Mel energy spectrum feature is designed first. Second, by combining a frequency-adaptive module, an attention mechanism, and a multi-scale fusion module, a multi-scale frequency-adaptive network is proposed to enhance the model’s learning ability. Then, the model training is optimized by introducing a time–frequency mask, a data augmentation strategy involving data confounding, and a focal loss function. Finally, systematic experiments were conducted based on the ShipsEar dataset. The results showed that the recognition accuracy for five categories reached 98.4%, and the accuracy for nine categories in fine-grained recognition was 88.6%. Compared with existing methods, the proposed multi-scale frequency-adaptive network for underwater target recognition has achieved significant performance improvement.

Disentangled contrastive learning for fair graph representations

Graph Neural Networks (GNNs) play a key role in efficiently learning node representations of graph-structured data through message passing, but their predictions are often correlated with sensitive attributes and thus lead to potential discrimination against some groups. Given the increasingly widespread applications of GNNs, solutions are required urgently to prevent algorithmic discrimination associated with GNNs, to protect the rights of vulnerable groups and to build trustworthy artificial intelligence. To learn the fair node representations of graphs, we propose a novel framework, the Fair Disentangled Graph Neural Network (FDGNN). With the proposed FDGNN framework, we enhance data diversity by generating instances that have identical sensitivity values but different adjacency matrices through data augmentation. Additionally, we design a counterfactual augmentation strategy for constructing instances with varying sensitive values while preserving the same adjacency matrices, thereby balancing the distribution of sensitive values across different groups. Subsequently, we employ a disentangled contrastive learning strategy to acquire disentangled representations of non-sensitive attributes such that sensitive information does not affect the prediction of node information. Finally, the learned fair representations of non-sensitive attributes are employed for building a fair predictive model. Extensive experiments on three real-world datasets demonstrate that FDGNN yields the best fairness predictions compared to the baseline methods. Additionally, the results demonstrate the potential of disentanglement in learning fair representations.

Inertial amplification as a performance enhancement method for snap-through vibration energy harvester

In recent years, there has been a lot of interest in exploiting the bistable behavior of snap-through systems to harvest energy from vibration sources. The efficient operation of any bistable VEH depends on its ability to exhibit large-amplitude interwell motion. Under weak ambient excitation, bistable VEH performs marginally because of the confinement of motion to a single well. Frequency up-conversion, multi-stability, and adaptive techniques are some of the performance enhancement strategies suggested for bistable-VEH. Considering the VEH's space constraints, the above designs are hard to implement in practical cases. This study introduces an inertial amplification mechanism (IAM) as a simple passive strategy to enhance the performance of a snap-through VEH, a concept not explored in previous studies. The addition of IAM increases the effective mass without increasing the physical mass and thereby enhances energy harvesting, especially from weak ambient excitation sources. The dynamics and performance of the enhanced snap-through VEH are investigated analytically and numerically under harmonic and random excitations. The harmonic balance method (HBM) derives the frequency-amplitude relationship, which shows a hardening behavior and an increase in bandwidth. The effective potential method provides a closed-form expression for the joint probability density function (Joint PDF), which is governed by the Fokker-Planck equation. The joint PDF shows a transition from bimodal to unimodal with an increase in the value of the geometrical parameter. The stochastic averaging method is employed to obtain the stationary probability density function, which defines the long-term dynamics of the VEH. The effects of noise intensity, mass ratio, and inertial amplifier angle on the dynamics are investigated. Finally, the performance of the proposed VEH is compared with a conventional snap-through VEH, an equivalent linear VEH, and a multistable VEH under harmonic and random excitation conditions. The findings suggest that the snap-through VEH with the IAM has advantages over the linear and multistable nonlinear VEH in terms of extracting energy from low-intensity harmonic and random excitation sources. This simple augmentation strategy preserves the original system's bistability, eliminating the need for the complex design of a multistable VEH.

Multi-level discriminator based contrastive learning for multiplex networks

Graph embedding is a technique for obtaining low-dimensional representations of nodes across diverse networks, which may then be used for various downstream tasks and applications. When it applies to heterogeneous networks, it is hard to handle heterogeneous networks because they usually contain different types of nodes and edges with more semantic and structural information. Recently, contrastive learning has developed as the preferred strategy for dealing with unsupervised heterogeneous graph embedding to reduce the cost of human label annotation. However, most multi-view contrastive learning approaches calculate the model’s loss only based on the mutual dependence between the node representation and graph representation. These approaches ignore that both node attributes and node clustering contain discriminative content. To solve this issue, we propose a model called Multi-Level Discriminator-based Contrastive Learning for Multiplex Networks (MLDCL). This model adopts a multi-level multi-discriminator-based approach that can simultaneously learn the global-level structural information, node-level attribute information, and local-level clustering information. Moreover, an augmentation strategy in the contrast learning process from the spectral domain is proposed to improve the representation and discriminative ability of MLDCL. Numerous tests with node clustering and classification tasks on widely used datasets demonstrate the efficacy of the proposed approach.

Management of Rotator Cuff Tears in Severely Osteoporotic Patients

Management of rotator cuff tears (RCT) in osteoporotic patients presents significant challenges for treating clinicians because of compromised bone quality. We present a case series of 2 patients with RCT and severe osteoporosis highlighting the complexities of operative and nonoperative management and underscoring the importance of preoperative optimization and careful surgical planning in this vulnerable population. Patient 1 experienced initial improvement postarthroscopic RCT repair but required revision surgery because of anchor pullout, with eventual conversion to reverse total shoulder arthroplasty. Patient 2 faced intraoperative challenges because of previously undiagnosed osteoporosis, leading to difficulties in securely placing suture anchors, and was referred to the fracture liaison service (FLS) for osteoporosis management postoperatively. A multifaceted approach integrating preoperative optimization, advanced surgical techniques, and postoperative management is advised to improve operative success. Extensive perioperative bone quality assessments and augmentation strategies are warranted, including medication comanagement through an FLS if available. Detailed surgical planning is crucial, focusing on arthroscopic repair technique while considering various anchor factors to mitigate failure including proper placement, design, and pullout analysis. Intraoperative bone augmentation with materials such as calcium phosphate, magnesium phosphate, and polymethyl methacrylate cements significantly improves anchor fixation strength in osteoporotic bone. Consideration of a transosseous approach and potential for conversion to an open procedure is also an option when necessary. This review highlights the unique challenges and potential options available to treating clinicians when managing RCT in osteoporotic patients. Continued research and refinement of emerging treatment strategies are essential to improve outcomes and quality of life in this patient population.