Unsupervised learning-based approach for detecting 3D edges in depth maps

3D edge features, which represent the boundaries between different objects or surfaces in a 3D scene, are crucial for many computer vision tasks, including object recognition, tracking, and segmentation. They also have numerous real-world applications in the field of robotics, such as vision-guided grasping and manipulation of objects. To extract these features in the noisy real-world depth data, reliable 3D edge detectors are indispensable. However, currently available 3D edge detection methods are either highly parameterized or require ground truth labelling, which makes them challenging to use for practical applications. To this extent, we present a new 3D edge detection approach using unsupervised classification. Our method learns features from depth maps at three different scales using an encoder-decoder network, from which edge-specific features are extracted. These edge features are then clustered using learning to classify each point as an edge or not. The proposed method has two key benefits. First, it eliminates the need for manual fine-tuning of data-specific hyper-parameters and automatically selects threshold values for edge classification. Second, the method does not require any labelled training data, unlike many state-of-the-art methods that require supervised training with extensive hand-labelled datasets. The proposed method is evaluated on five benchmark datasets with single and multi-object scenes, and compared with four state-of-the-art edge detection methods from the literature. Results demonstrate that the proposed method achieves competitive performance, despite not using any labelled data or relying on hand-tuning of key parameters.</p

Impact of Principal Component Analysis on the Performance of Machine Learning Models for the Prediction of Length of Stay of Patients

Patient inflow, limited resources, criticality of diseases and service quality factors have made it essential for the hospital administration to predict the length of stay (LOS) for inpatients as well as outpatients. An efficient and effective LOS prediction tool can improve the patient care and minimize the cost of service by increasing the efficiency of the system through optimal allocation of available resources in the hospital. For predicting patient’s LOS, machine learning (ML) models can have encouraging results. In this paper, five ML algorithms, namely linear regression, k- nearest neighbours, decision trees, random forest, and gradient boosting regression, have been used to predict the LOS for the patients admitted to the hospital with some medical history, laboratory measurements, and vital signs collected before admission. Additionally, the impact of principal component analysis (PCA) has been analyzed on the predictive performance of all ML algorithms. A five-fold cross-validation technique has been used to validate the results of proposed ML model. The results concluded that the RF and GB model performs better with &nbsp;score of 0.856 and 0.855 respectively among all the ML models without using PCA. However, the accuracy of all the models increased with the PCA except KNN and LR. The GB model when used with principal components has &nbsp;score and MSE approximate to 0.908 and 0.49 respectively compared to the model that incorporates with the original data. Additionally, PCA has an advantageous effect on the DT, RF and GB models. Therefore, LOS for new patients can be predicted effectively using the proposed tree-based RF and GB model with using PCA

Effect of teriparatide in fracture healing of intertrochanteric fracture: a prospective study

Background: Intertrochanteric fracture is a relatively common and serious medical issue in geriatric trauma result in serious health problems and decrease health related quality of life. Faster time-to-union is important for early return to daily activities and reduction of complications. Teriparatide has been shown to accelerate fracture-healing. The purpose of the present prospective, randomized, controlled study was to evaluate the effect of teriparatide on the course of intertrochanteric fracture-healing.Methods: Forty patients of intertrochanteric fractures who underwent surgical intervention between June 2016 and May 2017 were enrolled in this prospective study and followed for minimum of six months. Group A included patients who received only calcium supplementation; patients in Group B received teriparatide along with calcium supplementation postoperatively.Results: The mean time to fracture healing was between 8-12 weeks for the treatment group, compared with 12-16 weeks for the control group. There was also significant effectiveness with regards to Parker and Palmer mobility score at 6 months.Conclusions: Postoperative use of teriparatide for 6 months appears to be an effective adjunct therapy in the treatment of patients with intertrochanteric fractures. However, because of the limited power of the study a large-scale cohort study is still required for determining the efficacy of teriparatide

Pulmonary Lymphangiomyomatosis: A Rare Disease responsive to Progesterone

Lymphangiomyomatosis (LAM) is a rare multisystem disorder in women of child-bearing age. We present a case of a 28 year old lady who presented with cough and breathlessness. She had been diagnosed as a case of lymphangiomyomatosis by computer tomography of chest. She showed dramatic clinical improvement with hormonal therapy

New Oral Anticoagulants: An Overview

Oral anticoagulant therapy is used in the prevention and treatment of venous thromboembolism (VTE), prevention of stroke and other systemic emboli in patients with atrial fibrillation (AF) and artificial heart valves

Best Practices for Reliable and Robust Spacecraft Structures

A study was undertaken to capture the best practices for the development of reliable and robust spacecraft structures for NASA s next generation cargo and crewed launch vehicles. In this study, the NASA heritage programs such as Mercury, Gemini, Apollo, and the Space Shuttle program were examined. A series of lessons learned during the NASA and DoD heritage programs are captured. The processes that "make the right structural system" are examined along with the processes to "make the structural system right". The impact of technology advancements in materials and analysis and testing methods on reliability and robustness of spacecraft structures is studied. The best practices and lessons learned are extracted from these studies. Since the first human space flight, the best practices for reliable and robust spacecraft structures appear to be well established, understood, and articulated by each generation of designers and engineers. However, these best practices apparently have not always been followed. When the best practices are ignored or short cuts are taken, risks accumulate, and reliability suffers. Thus program managers need to be vigilant of circumstances and situations that tend to violate best practices. Adherence to the best practices may help develop spacecraft systems with high reliability and robustness against certain anomalies and unforeseen events

COVID-19 market disruptions and food security: Evidence from households in rural Liberia and Malawi

We use data collected from panel phone surveys to document the changes in food security of households in rural Liberia and Malawi during the market disruptions associated with the COVID-19 lockdowns in 2020. We use two distinct empirical approaches in our analysis: (a) an event study around the date of the lockdowns (March to July 2020), and (b) a difference-in-differences analysis comparing the lockdown period in 2020 to the same months in 2021, in order to attempt to control for seasonality. In both countries, market activity was severely disrupted and we observe declines in expenditures. However, we find no evidence of declines in food security.1

Optimization methods for electric power systems: An overview

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article

Metformin extended-release versus immediate-release:An international, randomized, double-blind, head-to-head trial in pharmacotherapy-naïve patients with type 2 diabetes

This international, randomized, double-blind trial (NCT01864174) compared the efficacy and safety of metformin extended-release (XR) and immediate-release (IR) in patients with type 2 diabetes. After a 4-week placebo lead-in, pharmacotherapy-naïve adults with glycated haemoglobin (HbA1c) at 7.0% to 9.2% were randomized (1:1) to receive once-daily metformin XR 2000mg or twice-daily metformin IR 1000mg for 24weeks. The primary endpoint was change in HbA1c after 24weeks. Secondary endpoints were change in fasting plasma glucose (FPG), mean daily glucose (MDG) and patients (%) with HbA1c <7.0% after 24weeks. Overall, 539 patients were randomized (metformin XR, N=268; metformin IR, N=271). Adjusted mean changes in HbA1c, FPG, MDG and patients (%) with HbA1c <7.0% after 24weeks were similar for XR and IR: -0.93% vs -0.96%; -21.1 vs -20.6mg/dL (-1.2 vs -1.1mmol/L); -24.7 vs -27.1mg/dL (-1.4 vs -1.5mmol/L); and 70.9% vs 72.0%, respectively. Adverse events were similar between groups and consistent with previous studies. Overall, metformin XR demonstrated efficacy and safety similar to that of metformin IR over 24weeks, with the advantage of once-daily dosing

Assessment of impacts of climate change on rice and wheat in the Indo-Gangetic plains

In this paper, the climate change scenarios of A2 and B2 for 2070-2100 time scale (denoted as 2080) for several key locations of India and its impact on rice and wheat crops based on regional climate model (PRECIS) were described. The PRECIS projects an increase in temperature over most parts of India especially in the IGP (Indo-Gangetic Plains), the region that presently experiences relatively low temperatures. Extreme high temperature episodes and rainfall intensity days are projected to become more frequent and the monsoon rainfall is also projected to increase. Rabi (mid Nov-March) season is likely to experience higher increase in temperature which could impact and hence become threat to the crops which really require low temperature for their growth. Climatic variability is also projected to increase in both A2 and B2 scenarios. All these projected changes are likely to reduce the wheat and rice yields in Indo-Gangetic plains of India. It is likely that there will be more number of years with low yields occurs towards the end of the century. Such yield reductions in rice and wheat crops due to climate change are mediated through reduction in crop duration, grain number and grain filling duration. The yield loss will be more in A2 scenario compared to B2. These quantitative estimates still have uncertainties associated with them, largely due to uncertainties in climate change projections, future technology growth, availability of inputs such as water for irrigation, changes in crop management and genotype. These projections nevertheless provide a direction of likely change in crop productivity in future climate change scenarios