Search Results (6,915)

Medical health records hold personal medical information and should only be accessed by authorized medical personnel or concerned patients. The importance of medical health records privacy is increasing as these records are shared in cloud environments. In this paper, we propose an enhanced system for securing patient data (Medical Health Records) embedded in multiple medical images in fragments for secure transmission and public sharing on the cloud or other environments. To protect the patient’s privacy, Medical Records are first encrypted, and then the ciphertext is broken into several fragments based on the number of multimodal medical images of a patient. A key generator randomly selects medical images from the multimodal image data to embed the encrypted patient health record segment using a modified least significant bit embedding process. The proposed technique enables an extra layer of security as even if files fall into the wrong hands and a fragment of the file is decrypted, it will not present any understandable information until all fragments from other medical images are extracted and combined in the correct order. The experiments are performed using multimodal 3255 MRI scans of 21 patients. The robustness of the proposed method was measured using different metrics such as PSNR, MSE, and SSIM. The results show that the proposed system is robust and that image quality is also maintained. To further study the stego image quality, a deep learning-based classification was applied to the images, and the results show that the diagnosis using stego medical images and performance remains unaffected even after embedding the encrypted data. Full article

Pediatric low-grade gliomas (PLGGs) comprise a heterogeneous set of low-grade glial and glioneuronal tumors, collectively representing the most frequent CNS tumors of childhood and adolescence. Despite excellent overall survival rates, the chronic nature of the disease bears a high risk of long-term disease- and therapy-related morbidity in affected patients. Recent in-depth molecular profiling and studies of the genetic landscape of PLGGs led to the discovery of the paramount role of frequent upregulation of RAS/MAPK and mTOR signaling in tumorigenesis and progression of these tumors. Beyond, the subsequent unveiling of RAS/MAPK-driven oncogene-induced senescence in these tumors may shape the understanding of the molecular mechanisms determining the versatile progression patterns of PLGGs, potentially providing a promising target for novel therapies. Recent in vitro and in vivo studies moreover indicate a strong dependence of PLGG formation and growth on the tumor microenvironment. In this work, we provide an overview of the current understanding of the multilayered cellular mechanisms and clinical factors determining the natural progression patterns and the characteristic biological behavior of these tumors, aiming to provide a foundation for advanced stratification for the management of these tumors within a multimodal treatment approach. Full article

(1) Background: Acute pain in hospitalized children remains under-recognized and under-treated. Our objective is to benchmark pain assessment, documentation, treatment, and patient experience in children admitted to a US children’s hospital. (2) Methods: A cross-sectional, mixed-method survey of pain for children hospitalized ≥24 h. Charts were reviewed for modalities of pain assessment and treatment for all inpatients. If pain was documented, patients/caregivers were surveyed regarding their experience with pain and its management. (3) Results: Chart review: All 107 patients had ≥1 pain score documented. A total of 47 patients had a pain score ≥0, 35 (74.5%) of whom had ≥1 moderate-severe score. Seventy (65.4%) patients received ≥1 intervention for pain, including medications from ≥1 class (e.g., opioids) (n = 55, 51.4%) and/or integrative/non-pharmacologic intervention(s) (n = 39, 36.4%). There were assessment and documentation gaps. Patient survey: A total of 39 (83.0%) interviews were attempted; 25 (53.2%) were completed. The worst pain was mostly caused by acute illness (n = 13, 52%) and painful procedures (n = 10, 40%). Suggestions for improvement included increasing the use of integrative modalities and optimizing patient–clinician communication. (4) Conclusions: All patients admitted ≥24 h had ≥1 pain score documented; however, gaps in documentation were common. Multimodal treatment and integrative modalities were underutilized. Procedures were a frequent cause of under-treated pain, prompting an institution-wide quality improvement project. Full article

Epithelial ovarian cancer (EOC) is the deadliest gynecological malignancy worldwide. Brain metastasis (BM) is quite an uncommon presentation. However, the likelihood of central nervous system (CNS) metastasization should be considered in the context of disseminated disease. The therapeutic management of BMs is an unmet clinical need, to date. We identified, across different cancer centers, six cases of both BRCA wild-type and BRCA-mutated EOCs spreading to the CNS. They presented either with a single brain lesion or with multiple lesions and most of them had intracranial-only disease. All cases received Poly-ADP ribose polymerase inhibitor (PARPi) maintenance, as per clinical practice, for a long time within a multimodal treatment approach. We also provide an insight into the available body of work regarding the management of this intriguing disease setting, with a glimpse of future therapeutic challenges. Despite the lack of unanimous guidelines, multimodal care pathways should be encouraged for the optimal disease control of this unfortunate patient subset. Albeit not being directly investigated in BM patients, PARPi maintenance is deemed to have a valuable role in this setting. Prospective research, aimed to implement worthwhile strategies in the multimodal patient journey of BMs from EOC, is eagerly awaited. Full article

Multimodal Sentiment Analysis (MSA) plays a critical role in many applications, including customer service, personal assistants, and video understanding. Currently, the majority of research on MSA is focused on the development of multimodal representations, largely owing to the scarcity of unimodal annotations in MSA benchmark datasets. However, the sole reliance on multimodal representations to train models results in suboptimal performance due to the insufficient learning of each unimodal representation. To this end, we propose Self-HCL, which initially optimizes the unimodal features extracted from a pretrained model through the Unimodal Feature Enhancement Module (UFEM), and then uses these optimized features to jointly train multimodal and unimodal tasks. Furthermore, we employ a Hybrid Contrastive Learning (HCL) strategy to facilitate the learned representation of multimodal data, enhance the representation ability of multimodal fusion through unsupervised contrastive learning, and improve the model’s performance in the absence of unimodal annotations through supervised contrastive learning. Finally, based on the characteristics of unsupervised contrastive learning, we propose a new Unimodal Label Generation Module (ULGM) that can stably generate unimodal labels in a short training period. Extensive experiments on the benchmark datasets CMU-MOSI and CMU-MOSEI demonstrate that our model outperforms state-of-the-art methods. Full article

We address the problem of systematizing the authoring of digital dictionaries for craft education from ethnographic studies and recordings. First, we present guidelines for the collection of ethnographic data using digital audio and video and identify terms that are central in the description of crafting actions, products, tools, and materials. Second, we present a classification scheme for craft terms and a way to semantically annotate them, using a multilingual and hierarchical thesaurus, which provides term definitions and a semantic hierarchy of these terms. Third, we link ethnographic resources and open-access data to the identified terms using an online platform for the representation of traditional crafts, associating their definition with illustrations, examples of use, and 3D models. We validate the efficacy of the approach by creating multimedia vocabularies for an online eLearning platform for introductory courses to nine traditional crafts. Full article

With the popularity of smartphones, a large number of “phubbers” have emerged who are engrossed in their phones regardless of the situation. In response to the potential dangers that phubbers face while traveling, this paper proposes a multimodal danger perception network model and early warning system for phubbers, designed for mobile devices. This proposed model consists of surrounding environment feature extraction, user behavior feature extraction, and multimodal feature fusion and recognition modules. The environmental feature module utilizes MobileNet as the backbone network to extract environmental description features from the rear-view image of the mobile phone. The behavior feature module uses acceleration time series as observation data, maps the acceleration observation data to a two-dimensional image space through GADFs (Gramian Angular Difference Fields), and extracts behavior description features through MobileNet, while utilizing statistical feature vectors to enhance the representation capability of behavioral features. Finally, in the recognition module, the environmental and behavioral characteristics are fused to output the type of hazardous state. Experiments indicate that the accuracy of the proposed model surpasses existing methods, and it possesses the advantages of compact model size (28.36 Mb) and fast execution speed (0.08 s), making it more suitable for deployment on mobile devices. Moreover, the developed image-acceleration multimodal phubber hazard recognition network combines the behavior of mobile phone users with surrounding environmental information, effectively identifying potential hazards for phubbers. Full article

Recent advancements in simultaneous localization and mapping (SLAM) have significantly improved the handling of dynamic objects. Traditionally, SLAM systems mitigate the impact of dynamic objects by extracting, matching, and tracking features. However, in real-world scenarios, dynamic object information critically influences decision-making processes in autonomous navigation. To address this, we present a novel approach for incorporating dynamic object information into map representations, providing valuable insights for understanding movement context and estimating collision risks. Our method leverages on-site mobile robots and multiple object tracking (MOT) to gather activation levels. We propose a multimodal map framework that integrates occupancy maps obtained through SLAM with Gaussian process (GP) modeling to quantify the activation levels of dynamic objects. The Gaussian process method utilizes a map-based grid cell algorithm that distinguishes regions with varying activation levels while providing confidence measures. To validate the practical effectiveness of our approach, we also propose a method to calculate additional costs from the generated maps for global path planning. This results in path generation through less congested areas, enabling more informative navigation compared to traditional methods. Our approach is validated using a diverse dataset collected from crowded environments such as a library and public square and is demonstrated to be intuitive and to accurately provide activation levels. Full article

Human activity recognition (HAR) is pivotal in advancing applications ranging from healthcare monitoring to interactive gaming. Traditional HAR systems, primarily relying on single data sources, face limitations in capturing the full spectrum of human activities. This study introduces a comprehensive approach to HAR by integrating two critical modalities: RGB imaging and advanced pose estimation features. Our methodology leverages the strengths of each modality to overcome the drawbacks of unimodal systems, providing a richer and more accurate representation of activities. We propose a two-stream network that processes skeletal and RGB data in parallel, enhanced by pose estimation techniques for refined feature extraction. The integration of these modalities is facilitated through advanced fusion algorithms, significantly improving recognition accuracy. Extensive experiments conducted on the UTD multimodal human action dataset (UTD MHAD) demonstrate that the proposed approach exceeds the performance of existing state-of-the-art algorithms, yielding improved outcomes. This study not only sets a new benchmark for HAR systems but also highlights the importance of feature engineering in capturing the complexity of human movements and the integration of optimal features. Our findings pave the way for more sophisticated, reliable, and applicable HAR systems in real-world scenarios. Full article

Vertical-cavity surface-emitting laser (VCSEL)-based transmission over multimode fiber (MMF) has achieved data rates of 100G per lane and is progressing towards 200G per lane. Recently, high-data-rate MMFs derived from OM3 and OM4 have been proposed. These fibers exhibit higher effective modal bandwidths at 910 nm, leading to a different wavelength dependence compared to conventional OM3 and OM4 MMFs. Understanding the wavelength dependence of these fibers is crucial to address their utilization in a broader range of applications. Through Monte Carlo simulations, we have obtained the low-end boundary of the effective modal bandwidths (EMBs) for these fibers, revealing capability improvements over the existing OM3 and OM4. The high-data-rate OM4 performs the same as or better than OM5 from 840 nm to 920 nm, while also showing a high bandwidth for the 850–870 nm wavelength window, favoring VCSELs with center wavelengths shifted toward 860 nm. We also obtained the link bandwidth, which includes both modal bandwidth and chromatic dispersion contributions, and the transmission reaches for various types of transceivers. We find that for both high-data-rate OM3 and high-data-rate OM4, the link bandwidth stays above the value at 850 nm until around 910 nm, delivering a similar transmission performance from 850 to 910 nm without declining towards longer wavelengths, unlike the standard OM3 and OM4. This characteristic favors a wider range of wavelength choices for VCSELs and enables optimal deployments for various applications. Full article

Multi-modal object re-identification (ReID) is a challenging task that seeks to identify objects across different image modalities by leveraging their complementary information. Traditional CNN-based methods are constrained by limited receptive fields, whereas Transformer-based approaches are hindered by high computational demands and a lack of convolutional biases. To overcome these limitations, we propose a novel fusion framework named MambaReID, integrating the strengths of both architectures with the effective VMamba. Specifically, our MambaReID consists of three components: Three-Stage VMamba (TSV), Dense Mamba (DM), and Consistent VMamba Fusion (CVF). TSV efficiently captures global context information and local details with low computational complexity. DM enhances feature discriminability by fully integrating inter-modality information with shallow and deep features through dense connections. Additionally, with well-aligned multi-modal images, CVF provides more granular modal aggregation, thereby improving feature robustness. The MambaReID framework, with its innovative components, not only achieves superior performance in multi-modal object ReID tasks, but also does so with fewer parameters and lower computational costs. Our proposed MambaReID’s effectiveness is validated by extensive experiments conducted on three multi-modal object ReID benchmarks. Full article

Background: Esophageal cancer (EC) comprises 1% of all diagnosed cancers in the USA. It is more common in other parts of the world. If there is distant metastasis, the relative survival rate is 6%. There are no standardized screening methods for EC. Case Presentation: We reported a four-year case of esophageal cancer, a P53-positive mutation with atypical distant metastasis to the cardiac and skeletal muscles. The patient was managed with multimodal therapy, including immunotherapy, which could have been a factor in prolonged survival. Conclusions: Distant metastases are typically seen postmortem, and with prolonged survival, we are able to find such unique metastases antemortem. Despite a history of negative scans, the patient’s ctDNA (circulating tumor DNA) remained positive, which was a better predictor of recurrence in this case. Future research is required to establish cost-effective screening methods and standardized treatments. Full article

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows’ food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method’s performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system’s generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO₂, SO₂, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust. Full article

In autonomous driving, the fusion of multiple sensors is considered essential to improve the accuracy and safety of 3D object detection. Currently, a fusion scheme combining low-cost cameras with highly robust radars can counteract the performance degradation caused by harsh environments. In this paper, we propose the IRBEVF-Q model, which mainly consists of BEV (Bird’s Eye View) fusion coding module and an object decoder module.The BEV fusion coding module solves the problem of unified representation of different modal information by fusing the image and radar features through 3D spatial reference points as a medium. The query in the object decoder, as a core component, plays an important role in detection. In this paper, Heat Map-Guided Query Initialization (HGQI) and Dynamic Position Encoding (DPE) are proposed in query construction to increase the a priori information of the query. The Auxiliary Noise Query (ANQ) then helps to stabilize the matching. The experimental results demonstrate that the proposed fusion model IRBEVF-Q achieves an NDS of 0.575 and a mAP of 0.476 on the nuScenes test set. Compared to recent state-of-the-art methods, our model shows significant advantages, thus indicating that our approach contributes to improving detection accuracy. Full article

Background: Musculoskeletal pain is a non-negligible multifaceted condition affecting more than 30% of the global population. Superficial heat therapy (HT), through increasing tissue temperatures, plays a role in increasing local metabolism and function and relieving pain. Knee (KP) and sports pain represent two relevant fields of superficial HT application. Methods: In the present paper, a panel of experts performed a narrative review of the literature regarding the role of superficial HT in the management of knee and sports activity-related pain. Results: According to the reviewed literature, HT represents a therapeutic option in the management of musculoskeletal pain due to three main effects: pain relief, promotion of healing, and return to normal function and activity. Moreover, HT plays a role in sport activities both before and after exercise. Before performing sports, HT helps in preparing muscles for performance. After performing sports, it is capable to promote recovery and healing pathways. Combining and sequencing superficial heat and cold therapy represent an interesting topic of study. Overall, the application of heat wraps for superficial HT can be considered safe. Conclusions: HT has been shown to be a potentially beneficial and safe option in the management of several conditions including KP and sports. The key in the application of superficial HT is a multimodal and multidisciplinary approach. Full article