A THOUGHT ON “DATACENTER AS A SMALL / MEDIUM SCALE INDUSTRY IN EMERGING ECONOMIES”

Cloud Technology is emerging very fast and it has become no more rocket science with wide scale development of open source solutions.  The cloud and big data are evolving at a great velocity and so there is a need for infrastructure to manage them. Social network sites, many web applications are generating data at enormous velocity. The web is also growing at enormous speed. The total number of websites has reached more than a billion. The number of mobile applications is grown to the extent of 2 million in android play store alone.The cost of clouds for hosting/usage of smartphone applications is very high. There is also much of network latency while accessing these applications from the cloud. So there is also a need for infrastructure that is closer to the smart phones (Mobile Cloud Computing)

A LITERATURE STUDY ON VARIOUS FACTORS AFFECTING COMPUTATIONAL OFFLOADING PERFORMANCE

Mobile systems, such as smart phones, tablets, have become the primary resources of computation for many users. Many surveys have shown that longer battery lifetime as the most important feature of such systems.  Consumers spend more time on media through mobile applications. Many mobile applications are too computation intensive to perform on a mobile system such as games, image processing and many more. The hardware limitations of mobile devices for higher performance and/or energy savings can be addressed by offloading computationally intensive task to external resource.  There are many issues associated with computation offloading such as network bandwidth, intermittent connectivity, the transmission delays, the distance of remote computing resources from primary computing resource. This paper presents a literature study on research work done till date with respect to computation offloading strategies developed to overcome these challenges

Restoration of neglected fracture dislocation hip in elderly: a case report

Neglected traumatic fracture dislocation of the hip is a challenging problem due to soft tissue contractures, adhesions, fibro fatty tissue filling acetabulum, avascular necrosis, arthritis and myositis ossificans. These types of injury often get missed at initial evaluation in the presence of distracting injuries and in poly trauma patients. Femoral head fractures account for only 7-16% of all hip fracture dislocations, with combined femoral head and acetabular fractures in elderly being even lower. Literature favours primary hip replacement as compared to hip salvage in age above 60 years and in patients with neglected hip fracture dislocations of more than 3 months duration due to high chances of afore mentioned complications. Here, we report a case of 69 years old male with neglected hip fracture dislocation associated with posterior acetabular wall and femoral head fracture for the challenges in management with a total hip replacement

Accelerated Neural Network Training with Rooted Logistic Objectives

Many neural networks deployed in the real world scenarios are trained using cross entropy based loss functions. From the optimization perspective, it is known that the behavior of first order methods such as gradient descent crucially depend on the separability of datasets. In fact, even in the most simplest case of binary classification, the rate of convergence depends on two factors: (1) condition number of data matrix, and (2) separability of the dataset. With no further pre-processing techniques such as over-parametrization, data augmentation etc., separability is an intrinsic quantity of the data distribution under consideration. We focus on the landscape design of the logistic function and derive a novel sequence of {\em strictly} convex functions that are at least as strict as logistic loss. The minimizers of these functions coincide with those of the minimum norm solution wherever possible. The strict convexity of the derived function can be extended to finetune state-of-the-art models and applications. In empirical experimental analysis, we apply our proposed rooted logistic objective to multiple deep models, e.g., fully-connected neural networks and transformers, on various of classification benchmarks. Our results illustrate that training with rooted loss function is converged faster and gains performance improvements. Furthermore, we illustrate applications of our novel rooted loss function in generative modeling based downstream applications, such as finetuning StyleGAN model with the rooted loss. The code implementing our losses and models can be found here for open source software development purposes: https://anonymous.4open.science/r/rooted_loss

Toxicity profile of thal-dex regime in patients with multiple myeloma

Background: To study the spectrum, incidence and severity of toxicity among Multiple Myeloma patients receiving Thal-Dex in South Indian population.Methods: Between November 2005 and November 2005, 25 adult patients with previously- untreated Multiple Myeloma were assigned to receive Thal-Dex at Regional Cancer Centre, Trivandrum. During chemotherapy, patients were followed-up to detect the development of any toxicity symptoms. The toxicities recorded, were graded according to the criteria of the World Health Organization toxicity-guidelines.Results: In the 25 patients who received Thal-Dex, peripheral neuropathy, infection and constipation were significantly seen, while gastrointestinal toxicities were seen to a lesser extent, and haematological toxicities were low.Conclusions: The Thal-Dex regimen was tolerated well by majority of the patients and showed favourable toxicity profile, reiterating its acceptability as a front line antimyeloma regime

Role of closed subcutaneous drain in prevention of surgical site infection in perforation peritonitis

Background: Surgery for perforation peritonitis is associated with the highest rates of infective complications, especially surgical site infection. SSI occurs due to failure of obliteration of dead space during abdominal wound closure resulting in formation of hematoma and seroma collection in the surgical wound viz. abdominal wound in cases of perforation peritonitis. This acts as a good culture medium for bacterial organisms to grow and cause wound infection. The bacterial pathogens can be either from intra-abdominal sepsis or nosocomial in origin. Closed suction drains can be used effectively to eliminate dead space in the wound and evacuates the seroma or hematoma collection, thereby reducing chances of SSI and also helps in early detection of SSI by inspecting the nature of drain output. Aim was to evaluate the role of closed suction drains in prevention of SSI in cases of perforation peritonitis. Methods: Comparative study of 60 cases of perforation peritonitis divided into two equal groups (Group A patient with closed suction drain in subcutaneous space vs. Group B patient without closed suction drain). Outcomes of SSI were compared. Results: The incidence of SSI in Group A was 33% whereas in Group B was 70%. 40% cases in SSI in Group A whereas 76% cases of SSI in Group B developed wound dehiscence. Most cases of SSI was diagnosed on POD 2 for Group A and on POD 4 for Group B. Conclusions: The study supports use of closed suction drain in perforation peritonitis for prevention, early detection and appropriate management of SSI

DIOR: Dataset for Indoor-Outdoor Reidentification -- Long Range 3D/2D Skeleton Gait Collection Pipeline, Semi-Automated Gait Keypoint Labeling and Baseline Evaluation Methods

In recent times, there is an increased interest in the identification and re-identification of people at long distances, such as from rooftop cameras, UAV cameras, street cams, and others. Such recognition needs to go beyond face and use whole-body markers such as gait. However, datasets to train and test such recognition algorithms are not widely prevalent, and fewer are labeled. This paper introduces DIOR -- a framework for data collection, semi-automated annotation, and also provides a dataset with 14 subjects and 1.649 million RGB frames with 3D/2D skeleton gait labels, including 200 thousands frames from a long range camera. Our approach leverages advanced 3D computer vision techniques to attain pixel-level accuracy in indoor settings with motion capture systems. Additionally, for outdoor long-range settings, we remove the dependency on motion capture systems and adopt a low-cost, hybrid 3D computer vision and learning pipeline with only 4 low-cost RGB cameras, successfully achieving precise skeleton labeling on far-away subjects, even when their height is limited to a mere 20-25 pixels within an RGB frame. On publication, we will make our pipeline open for others to use

Study on role of fibular graft in non-union and complex fractures of long bone

Background: Road traffic injuries are the seventh most common cause of long bone fractures. Following high velocity limb trauma, the defects in the long bone are usually associated with appreciable soft-tissue losses. These open long bone injuries always require multidisciplinary managements to reconstruct the composite defects of bone and soft tissue. Aims and Objectives: The aim of the study was to find out radiological and clinical outcome, complications, and union time in complex and non-union fractures of long bone managed by fibular graft. Materials and Methods: Out of 50 cases, 40 cases were of complex fractures and ten cases were of non-union. We used Fibular strut grafts in reconstruction of bone defects and soft tissue injury. Results: Thirty (60.0%) had excellent functional outcome, 10 (20.0%) had good, 6 (12.0%) had satisfactory, and 4 (8.0%) patients had poor outcome. Main complications were non-union 2 cases and 3 cases of superficial wound infection, which subsided by wound dressing and intravenous antibiotic treatment. Conclusion: Free fibular grafting has been proven to be an ideal choice in the management of large segmental bone defects as well as in situations of biological failure of bone healing

Prediction of Delamination in End Milling of GFRP Using ANSYS

The use of Glass Fiber Reinforced Plastics (GFRP) has increased manifold over the last few years. Generally developed for aerospace and other high-end applications, composites are now making inroads into the automotive and general engineering markets. Thus good quality and cost-effective manufacturing of GFRP composites becomes imperative. One of the machining process milling is the most practical operation available for producing an accurate shape and high quality surface. Delamination is recognized as one of the most critical defects that can result from the machining of composites. Delamination due to milling has been a major research for many years and a considerable amount of work has been done to reduce it by statistical means. A lot of experimental work has to be done in order to know the optimal cutting conditions with respect to factors on delamination, which is cumbersome. These necessities a need for developing suitable prediction model in order to reduce the number of experiments being conducted to determine the optimal values for various applications. In this study a suitable prediction model for milling of GFRP has been developed using Ansys 11 Software. In order to understand the effects of process parameters on the delamination milling experiments using K10 end mill on three different types of GFRP with different speed, feed and depth of cut has been performed and analyzed using FEM model. Using FEM model the desired cutting parameters for minimized appearance of delamination in different GFRP has been developed and its value has been compared with the experimental values. It has been found out that the discussed FEA model results are close to be experimental result