Local application of gentamicin-containing collagen implant in the prophylaxis and treatment of surgical site infection following vascular surgery

AbstractBackgroundThe development of surgical site infection (SSI) following vascular surgery is an important issue for healthcare providers as it has serious implications for both patient morbidity and mortality.MethodsFive publications were identified using the PubMed online database and search terms ‘gentamicin-containing collagen implant’ plus ‘surgical site infection’, ‘wound infection’ and ‘vascular surgery’.ResultsThe reviewed publications demonstrated that prophylactic use of GCCI in conjunction with standard treatment reduces the SSI rate in patients operated on for femeropopliteal bypass grafting. The prophylactic use of GCCI may also have a role to play in patients at high-risk of infection (e.g. in those with co-morbidities such as obesity) and in high-risk procedures (e.g. surgical revision to correct anastomotic aneurysm or dehiscence). GCCI in conjunction with systemic antibiotics may also be effective in the treatment of wound infections of the groin following vascular reconstruction.ConclusionThis review demonstrates that GCCI have a role to play in preventing and treating SSI following vascular reconstruction when used in conjunction with standard treatment approaches. Additional randomised, controlled studies are required to further establish the efficacy and cost-effectiveness of GCCI in vascular surgery

Visual Analysis Algorithms for Embedded Systems

The main contribution of this thesis is the design and development of an optimized framework to realize the deep neural classifiers on the embedded platforms. Deep convolutional networks exhibit unmatched performance in image classification. However, these deep classifiers demand huge computational power and memory storage. That is an issue on embedded devices due to limited onboard resources. The computational demand of neural networks mainly stems from the convolutional layers. A significant improvement in performance can be obtained by reducing the computational complexity of these convolutional layers, making them realizable on embedded platforms. In this thesis, we proposed a CUDA (Compute Unified Device Architecture)-based accelerated scheme to realize the deep architectures on the embedded platforms by exploiting the already trained networks. All required functions and layers to replicate the trained neural networks were implemented and accelerated using concurrent resources of embedded GPU. Performance of our CUDA-based proposed scheme was significantly improved by performing convolutions in the transform domain. This matrix multiplication based convolution was also compared with the traditional approach to analyze the improvement in inference performance. The second part of this thesis focused on the optimization of the proposed framework. The flow of our CUDA-based framework was optimized using unified memory scheme and hardware-dependent utilization of computational resources. The proposed flow was evaluated over three different image classification networks on Jetson TX1 embedded board and Nvidia Shield K1 tablet. The performance of proposed GPU-only flow was compared with its sequential and heterogeneous versions. The results showed that the proposed scheme brought the higher performance and enabled the real-time image classification on the embedded platforms with lesser storage requirements. These results motivated us towards the realization of useful real-time classification and recognition problems on the embedded platforms. Finally, we utilized the proposed framework to realize the neural network-based automatic license plate recognition (ALPR) system on a mobile platform. This highly-precise and computationally demanding system was deployed by simplifying the flow of trained deep architecture developed for powerful desktop and server environments. A comparative analysis of computational complexity, recognition accuracy and inference performance was performed

Transforming spatio-temporal self-attention using action embedding for skeleton-based action recognition

Over the past few years, skeleton-based action recognition has attracted great success because the skeleton data is immune to illumination variation, view-point variation, background clutter, scaling, and camera motion. However, effective modeling of the latent information of skeleton data is still a challenging problem. Therefore, in this paper, we propose a novel idea of action embedding with a self-attention Transformer network for skeleton-based action recognition. Our proposed technology mainly comprises of two modules as, i) action embedding and ii) self-attention Transformer. The action embedding encodes the relationship between corresponding body joints (e.g., joints of both hands move together for performing clapping action) and thus captures the spatial features of joints. Meanwhile, temporal features and dependencies of body joints are modeled using Transformer architecture. Our method works in a single-stream (end-to-end) fashion, where MLP is used for classification. We carry out an ablation study and evaluate the performance of our model on a small-scale SYSU-3D dataset and large-scale NTU-RGB+D and NTU-RGB+D 120 datasets where the results establish that our method performs better than other state-of-the-art architectures.publishedVersio

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.publishedVersio

Impact of Leader-Member Exchange Relationship and Job design on Counterproductive Work Behavior (CWB): The Role of Job Burnout

The aim of this study is to synthesize the effect of Leader-member exchange relationship (LMX) and Job design on Counterproductive work behavior (CWB) through the mediating lens of all three dimensions of Job Burnout that based upon Conservation of Resource (COR) theory. A sample size of 350 respondents was used for collecting data with the help of research survey by distributing questionnaires to the employees who are working in public sector universities of higher education sector that are currently based in twin cities (Islamabad and Rawalpindi) of Pakistan. Techniques of Simple and Multiple linear regressions were carried out for accessing mediation analysis via SPSS version 21.0 and AMOS version 27.0.  Findings of this study has revealed that LMX relationship is significantly but negatively related to Counterproductive work behavior (CWB) and Job design is significantly and positively related to (CWB). Also Job Burnout is a significant variable that mediates between Leader-member exchange for developing quality exchange relationships, job design and counterproductive work behaviors. This study gave into new insights and results into the existing body of knowledge along with practical implications and outcomes. Limitations for this study along with future directions of research are also discussed at the end

Interval Type 2 Fuzzy Adaptive Motion Drive Algorithm Design

Motion drive algorithms are a set of filters designed to simulate realistic motion and are an integral part of contemporary vehicle simulators. This paper presents the design of a novel intelligent interval type 2 fuzzy adaptive motion drive algorithm for an off-road uphill vehicle simulator. The off-road, uphill vehicle simulator is used to train and assess the driver’s behavior under varying operational and environmental conditions in mountainous terrain. The proposed algorithm is the first of its kind to be proposed for off-road uphill vehicle simulators, and it offers numerous benefits over other motion drive algorithms. The proposed algorithm enables the simulator to adapt to changes in the uphill road surface, vehicle weight distribution, and other factors that influence off-road driving in mountainous terrain. The proposed algorithm simulates driving on hilly terrain more realistically than existing algorithms, allowing drivers to learn and practice in a safe and controlled environment. Additionally, the proposed algorithm overcomes limitations present in existing algorithms. The performance of the proposed algorithm is evaluated via test drives and compared to the performance of the conventional motion drive algorithm. The results demonstrate that the proposed algorithm is more effective than the conventional motion drive algorithm for the ground vehicle simulator. The pitch and roll responses demonstrate that the proposed algorithm has enabled the driver to experience abrupt changes in terrain while maintaining the driver’s safety. The surge response demonstrated that the proposed MDA handled the acceleration and deceleration of the vehicle very effectively. In addition, the results demonstrated that the proposed algorithm resulted in a smoother drive, prevented false motion cues, and offered a more immersive and realistic driving experience.publishedVersio

A General-Purpose Graphics Processing Unit (GPGPU)-Accelerated Robotic Controller Using a Low Power Mobile Platform

Robotic controllers have to execute various complex independent tasks repeatedly. Massive processing power is required by the motion controllers to compute the solution of these computationally intensive algorithms. General-purpose graphics processing unit (GPGPU)-enabled mobile phones can be leveraged for acceleration of these motion controllers. Embedded GPUs can replace several dedicated computing boards by a single powerful and less power-consuming GPU. In this paper, the inverse kinematic algorithm based numeric controllers is proposed and realized using the GPGPU of a handheld mobile device. This work is the extension of a desktop GPU-accelerated robotic controller presented at DAS’16 where the comparative analysis of different sequential and concurrent controllers is discussed. First of all, the inverse kinematic algorithm is sequentially realized using Arduino-Due microcontroller and the field-programmable gate array (FPGA) is used for its parallel implementation. Execution speeds of these controllers are compared with two different GPGPU architectures (Nvidia Quadro K2200 and Nvidia Shield K1 Tablet), programmed with Compute Unified Device Architecture (CUDA) computing language. Experimental data shows that the proposed mobile platform-based scheme outperform s the FPGA by 5 and boasts a 100 speedup over the Arduino-based sequential implementation

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Deep convolutional neural networks achieve state-of-the-art performance in image classification. The computational and memory requirements of such networks are however huge, and that is an issue on embedded devices due to their constraints. Most of this complexity derives from the convolutional layers and in particular from the matrix multiplications they entail. This paper proposes a complete approach to image classification providing common layers used in neural networks. Namely, the proposed approach relies on a heterogeneous CPU-GPU scheme for performing convolutions in the transform domain. The Compute Unified Device Architecture(CUDA)-based implementation of the proposed approach is evaluated over three different image classification networks on a Tegra K1 CPU-GPU mobile processor. Experiments show that the presented heterogeneous scheme boasts a 50 speedup over the CPU-only reference and outperforms a GPU-based reference by 2, while slashing the power consumption by nearly 30%

Employee Turnover Intention in Call Center (Punjab) Pakistan

Purpose The purpose of this research was to find out the reasons of employee turnover in call centers of Punjab Pakistan so that the companies could retain their trained work force. Also find out the reasons which are affecting on employee’s turnover intention. The intention of this research is to find out those elements behind the employees turnover intention of job. Design/methodology the target population of this research is Multan and Lahore (Punjab). For getting the response 100 quantities of questionnaire were distributed and with use of application SPSS-18, revile the result on correlation analysis. Research findings the findings of this research identified that that there is negative relationship between the dependent and independent variables. The finding and the recommendation of this research will help the manager to develop a deeper insight of research factor for reducing the employee’s turnover intention Originality/value for determining the employees turnover intention in call center that is original study for newly and existing reader knowledge and there is no any portion of research are copy form anywhere. Implications all the company top level manager/HR manager and marketers of the industry who want to decline the turnover intention can use this research results. Keywords: employee’s turnover, job satisfaction, salary, working conditio