Prevalence of peripheral artery disease in diabetes mellitus: research article

Background: Peripheral artery disease is a major macrovascular complication of diabetes mellitus. Patients with diabetes mellitus have an increased prevalence of PAD. The ankle brachial pressure index is an easy, non invasive and often under utilised tool for diagnosis of PAD.Methods: In the present study, 100 patients from Western Uttar Pradesh with diabetes mellitus were enrolled to find out prevalence of peripheral vascular disease using ankle brachial pressure index and study the associated risk factors.Results: 59 percent of the subjects were female and 41 percent were male. Ankle-brachial index (ABI) examination of patients indicated Abnormal (ABI= <0.9), in 40 cases.Conclusions: it can be concluded that peripheral vascular disease in Diabetes Mellitus is more commonly associated than is generally believed

Functional outcome following external fixator (JESS) application for proximal humeral fractures

Background: Proximal humeral fractures account for 5% of all fractures. Observed frequently in older osteoporotic patients but found in young patients with high-energy trauma.About 80% of these fractures are undisplaced or minimally displaced. Non-operative method requiring immobilization of shoulder often leads to a stiff shoulder, whereas surgical procedures such as plating need excessive soft tissue dissection. It was overcome in this study by less soft tissue dissection by use of external fixator application and early mobilization.Methods: Total of 18 patients mean age 40.5 years, predominantly male (16/18) treated with external fixator - JESS (Joshi’s external immobilization system) for Neer’s two, three and four part proximal humeral fractures. Vehicular accidents were the most common mode of injury followed by fall. There were 8 cases each of Neer's two and three part fractures. Shoulder mobilization started within a week as postoperativelyas pain allowed. Patients followed up at 3, 6, 12 and 18 weeks for pain, function, range of motion and anatomy with check X-ray. After radiological union at 8-10 weeks JESS was removed. Cases were evaluated for functional result by constant scoring system.Results: Average score on constant scoring system was 72 after a mean follow-up of 6 months. All fractures united in mean duration of 9.33 weeks. The complications included shoulder stiffness in one case and pin tract infection in two cases.Conclusions: Early shoulder mobilization a prerequisite for good results can be achieved without compromising fracture union. Less soft tissue dissection required and significant cost effective

Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments

A real-world application or setting involves interaction between different modalities (e.g., video, speech, text). In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times. MRL involves learning reliable and robust representations of information from heterogeneous sources and fusing them. However, in practice, the data acquired from different sources are typically noisy. In some extreme cases, a noise of large magnitude can completely alter the semantics of the data leading to inconsistencies in the parallel multimodal data. In this paper, we propose a novel method for multimodal representation learning in a noisy environment via the generalized product of experts technique. In the proposed method, we train a separate network for each modality to assess the credibility of information coming from that modality, and subsequently, the contribution from each modality is dynamically varied while estimating the joint distribution. We evaluate our method on two challenging benchmarks from two diverse domains: multimodal 3D hand-pose estimation and multimodal surgical video segmentation. We attain state-of-the-art performance on both benchmarks. Our extensive quantitative and qualitative evaluations show the advantages of our method compared to previous approaches.Comment: 11 Pages, Accepted at ICMI 2022 Ora

MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE

In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights

MICMAC Analysis of Industry 4.0 in Indian Automobile Industry

Industrial evolution has taken over as next generation technological breakthrough which is capable of achieving digitalization to ensure enhanced quality, safety and economics. This paper aims to highlight those factors that affect the functioning of the automobile industry. The objective is to find the effect of various factors contributed by Industry 4.0 in the Indian automobile sector through brainstorming with manufacturing experts and literature survey. Ten factors were found relevant. Generated responses were used to measure their influence with each other. Structural Equation Modeling (SEM) analysis was applied for this. Then MICMAC (Matriced'impactscroisés multiplication appliquée á un classment), a cross-impact matrix multiplication is applied to show the dependency of the variables thereby examining the strength of the relationship between Industry 4.0 factors based on their driving and dependence power.The outcome model categorizes the factors in such a way that their impact can be observed by the practitioner and make changes accordingly. Effective mitigation of challenges associated with these factors is envisaged to make it much simpler for the company to enhance their performance and competitive ability. The model reiterates the fact that advancement in technology drives the 4th revolution and other factors such as supply chain management, organizational structure, employment disruption, etc. affect each other at various stages of development

Constant-Round Private Decision Tree Evaluation for Secret Shared Data

Decision tree evaluation is extensively used in machine learning to construct accurate classification models. Often in the cloud-assisted communication paradigm cloud servers execute remote evaluations of classification models using clients’ data. In this setting, the need for private decision tree evaluation (PDTE) has emerged to guarantee no leakage of information for the client’s input nor the service provider’s trained model i.e., decision tree. In this paper, we propose a private decision tree evaluation protocol based on the three-party replicated secret sharing (RSS) scheme. This enables us to securely classify inputs without any leakage of the provided input or the trained decision tree model. Our protocol only requires constant rounds of communication among servers, which is useful in a network with longer delays. Ma et al. (NDSS 2021) presented a lightweight PDTE protocol with sublinear communication cost with linear round complexity in the size of the input data. This protocol works well in the low latency network such as LAN while its total execution time is unfavourably increased in the WAN setting. In contrast, Tsuchida et al. (ProvSec 2020) constructed a constant round PDTE protocol at the cost of communication complexity, which works well in the WAN setting. Although their construction still requires 25 rounds, it showed a possible direction on how to make constant round PDTE protocols. Ji et al. (IEEE Transactions on Dependable and Secure Computing) presented a simplified PDTE with constant rounds using the function secret sharing (FSS) at the cost of communication complexity. Our proposed protocol only requires five rounds among the employed three servers executing secret sharing schemes, which is comparable to previously proposed protocols that are based on garbled circuits and homomorphic encryption. To further demonstrate the efficiency of our protocol, we evaluated it using real-world classification datasets. The evaluation results indicate that our protocol provides better concrete performance in the WAN setting that has a large network delay