Comparison of first dorsal metacarpal artery flap done by consultants and residents and guidelines for improving outcome for beginners

Background: Thumb alone constitutes about 40% of hand function and trauma to distal part of thumb will thus affect the overall hand function. The goals of correction of traumatic deformities of distal thumb are to maintain adequate length and sensation along with giving a supple and stable soft tissue cover. Among other options, first dorsal metacarpal artery (FDMA) flap raised from the dorsum of the proximal part of index finger is a simple and widely used flap.Methods: We compared the results of FDMA flap done by residents (M.Ch trainees) and consultants in our institute. Residents operated upon a total of 12 patients and consultants operated upon 16 patients.Results: Among 12 patients operated by residents 3 flaps were lost and 2 flaps had marginal necrosis whereas among the patients operated by consultants 1 flap was lost and 1 had partial necrosis in distal part of the flap.Conclusions: Although there was no statistical difference between the operating time taken by trainees and consultant specialists but the complication rate is higher among the residents. Sticking with the basics of plastic surgery, FDMA flap is an excellent technique for thumb reconstruction and results are excellent as and when more experience is gained

Smartwatch-Based Legitimate User Identification for Cloud-Based Secure Services

Smartphones are ubiquitously integrated into our home and work environment and users frequently use them as the portal to cloud-based secure services. Since smartphones can easily be stolen or coopted, the advent of smartwatches provides an intriguing platform legitimate user identification for applications like online banking and many other cloud-based services. However, to access security-critical online services, it is highly desirable to accurately identifying the legitimate user accessing such services and data whether coming from the cloud or any other source. Such identification must be done in an automatic and non-bypassable way. For such applications, this work proposes a two-fold feasibility study; (1) activity recognition and (2) gait-based legitimate user identification based on individual activity. To achieve the above-said goals, the first aim of this work was to propose a semicontrolled environment system which overcomes the limitations of users' age, gender, and smartwatch wearing style. The second aim of this work was to investigate the ambulatory activity performed by any user. Thus, this paper proposes a novel system for implicit and continuous legitimate user identification based on their behavioral characteristics by leveraging the sensors already ubiquitously built into smartwatches. The design system gives legitimate user identification using machine learning techniques and multiple sensory data with 98.68% accuracy

A Novel Construction of Substitution Box Involving Coset Diagram and a Bijective Map

The substitution box is a basic tool to convert the plaintext into an enciphered format. In this paper, we use coset diagram for the action of PSL(2,Z) on projective line over the finite field GF29 to construct proposed S-box. The vertices of the cost diagram are elements of GF29 which can be represented by powers of α, where α is the root of irreducible polynomial px=x9+x4+1 over Z2. Let GF⁎29 denote the elements of GF29 which are of the form of even powers of α. In the first step, we construct a 16×16 matrix with the elements of GF⁎29 in a specific order, determined by the coset diagram. Next, we consider h:GF⁎29⟶GF28 defined by hα2n=ωn to destroy the structure of GF28. In the last step, we apply a bijective map g on each element of the matrix to evolve proposed S-box. The ability of the proposed S-box is examined by different available algebraic and statistical analyses. The results are then compared with the familiar S-boxes. We get encouraging statistics of the proposed box after comparison

A Decision Support System for Diagnostics and Treatment Planning in Traumatic Brain Injury.

Traumatic brain injury (TBI) occurs when an external force causes functional or structural alterations in the brain. Clinical characteristics of TBI vary greatly from patient to patient, and a large amount of data is gathered during various phases of clinical care in these patients. It is hard for clinicians to efficiently integrate and interpret all of these data and plan interventions in a timely manner. This paper describes the technical architecture and functionality of a web-based decision support system (DSS), which not only provides advanced support for visualizing complex TBI data but also predicts a possible outcome by using a state-of-the-art Disease State Index machine-learning algorithm. The DSS is developed by using a three-layered architecture and by employing modern programming principles, software design patterns, and using robust technologies (C#, ASP.NET MVC, HTML5, JavaScript, Entity Framework, etc.). The DSS is comprised of a patient overview module, a disease-state prediction module, and an imaging module. After deploying it on a web-server, the DSS was made available to two hospitals in U.K. and Finland. Afterwards, we conducted a validation study to evaluate its usability in clinical settings. Initial results of the study indicate that especially less experience clinicians may benefit from this type of decision support software tool

Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records

With over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients, building on the electronic health records (EHR) of over 23,000 cardiac patients. Taking into account the usefulness of the prediction for chronic disease patients, a prediction period of six months was selected. Two major transformer models that rely on learning bidirectional dependencies in sequential data, BERT and XLNet, were trained and compared. To our knowledge, the presented work is the first to apply XLNet on EHR data to predict mortality. The patient histories were formulated as time series consisting of varying types of clinical events, thus enabling the model to learn increasingly complex temporal dependencies. BERT and XLNet achieved an average area under the receiver operating characteristic curve (AUC) of 75.5% and 76.0%, respectively. XLNet surpassed BERT in recall by 9.8%, suggesting that it captures more positive cases than BERT, which is the main focus of recent research on EHRs and transformers.publishedVersionPeer reviewe

System Architecture of A European Platform for Health Policy Decision Making: MIDAS

Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions

Meaningful Big Data Integration For a Global COVID-19 Strategy

Abstract With the rapid spread of the COVID-19 pandemic, the novel Meaningful Integration of Data Analytics and Services (MIDAS) platform quickly demonstrates its value, relevance and transferability to this new global crisis. The MIDAS platform enables the connection of a large number of isolated heterogeneous data sources, and combines rich datasets including open and social data, ingesting and preparing these for the application of analytics, monitoring and research tools. These platforms will assist public health author ities in: (i) better understanding the disease and its impact; (ii) monitoring the different aspects of the evolution of the pandemic across a diverse range of groups; (iii) contributing to improved resilience against the impacts of this global crisis; and (iv) enhancing preparedness for future public health emergencies. The model of governance and ethical review, incorporated and defined within MIDAS, also addresses the complex privacy and ethical issues that the developing pandemic has highlighted, allowing oversight and scrutiny of more and richer data sources by users of the system

Sustainability evaluation of transportation infrastructure under uncertainty : a fuzzy-based approach

The construction and maintenance of transportation infrastructure consume significant natural resources, produces considerable waste and uses extensive human capital. Sustainability evaluation of alternative initiatives and policies for developing transportation infrastructure enables decision makers to make informed choices. Despite the availability of numerous sustainability rating tools for roadway infrastructure, there is a need to develop customizable sustainability evaluation tools for informed decision-making. Such tools, unlike the rating systems, ideally need to handle uncertain data, incorporate expert opinion and adapt to project and geographic specific constraints. Deterministic approaches for life cycle cost analysis (LCCA) and life cycle assessment (LCA) have been extensively applied to select sustainable pavement alternatives. However, the information used to conduct LCCA and LCA is often imprecise and vague in early project phases. Therefore, certain technique is required to incorporate and propagate such uncertainties so that the reliability of final results is transparent. Unlike probabilistic methods, fuzzy based techniques are more appropriate to handle uncertainties due to vagueness and imprecision in a computationally efficient manner. This study aimed to investigate the use of fuzzy logic to evaluate sustainability under uncertainty at two levels of infrastructures - Roadways as systems and pavements as components. A novel roadway sustainability evaluation framework was developed using indicators from existing green rating system. A customizable excel-based tool was programmed based on the framework to estimate the sustainability index (SI) of roadways under uncertainty using fuzzy synthetic evaluation (FSE) technique. The FSE technique enables the tool to evaluate reliable and informative SI by incorporating expert opinion. Moreover, fuzzy composite programming (FCP) technique was used to estimate the life cycle environmental and economic sustainability indices (SIs) from LCA and LCCA of pavement alternatives under uncertainty. The FCP technique improved the reliability of final results by propagating input uncertainties to the outputs. Scenario analysis was performed using FSE and FCP techniques to demonstrate the influence of uncertainties and decision maker’s preferences on the overall SI of roadways and pavements respectively. This study demonstrated a compelling utility of fuzzy-based techniques to evaluate sustainability under uncertainty in the early project phases for informed decision-making.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat

Automated data acquisition and analysis system for positron annihilation spectroscopy

Çalışma alanımızda başarılı bir araştırma gercekleştirmemiz, veri toplama modüllerinin, donanımsal uyum içinde bulunmalarıyla birlikte veri analizi stratejisine dayanır. Bu deney çok yüksek sayıda modül ve veri yollarıyla kurulmuş olmasa da ciddi bir düzeyde gözetime ihtiyaç duyar. Bu çalışmada detaylandırılan sistem, C # .NET uygulaması ve bazı komut istemleri tarafından desteklenen National Instruments™ LabVIEW'da tasarlanmış bir yazılım uygulaması tarafından izlenir ve kontrol edilmektedir. Yazılım çözümü tamamen Windows© platformuna yöneliktir. Sistemin fiziki yapısı; sıcaklık kontrolü sağlayan bir sirkülatör, HPGe ve baryum florür sintilatörleri, “gecikme”, zaman-genlik dönüştürücüsü ve diferansiyel diskriminatör modüllerinden oluşmaktadır. Bu çalışmanın sonunda oluşturulan sistem, herhangi bir örnek materyalin ayrı ayrı veya birlikte DBAR ve PALS analizi için otomatik rutinleri çalıştırmak için kullanılabilir. Doppler Genişleme Yokolma Radyasyonu (DBAR) ve Pozitron Yokolma Ömür Spektroskopisi (PALS) teknikleri, gama ışınları ile etkileşen elektron ve pozitronların yok edilmesine dayanır. Pozitron, malzeme ile etkileşime girdikten sonra hızla termal bir dengeye ulaşır, yokolmadan önce, bir elektronu yakalar ve kısa bir ömrü olan pozitronyum oluşur. Bunun kısa bir süre sonrasında meydana gelen electron-pozitron yokolması Doppler kaymasını sergiler, bunun nedeni pozitronyumun momentumudur. Bu yöntem, yayılan gama ışınlarının enerjilerinin, elektronların momentum dağılımına bağlı olmasından dolayı malzeme analizinde kullanılabilir. PALS, malzemenin içyapısı hakkında fikir oluşumunu, pozitronların malzemeyle etkileşim soncundan yokolmaları için geçen süreyi kullanarak sağlar – yokolma, malzemenin kusurlarıyla doğru orantılı bir şekilde gecikir çünkü pozitronun etkileşebileceği elektrona ulaşması daha uzun sürer. -------------------- Our field of interest requires a hardware-coordination between acquisition modules and a data analysis strategy to successfully conduct research. Though our experiments do not include a huge number of modules and acquisition channels, the amount of monitoring it requires is huge. The system detailed in this study is monitored and controlled by a software application designed in National Instruments™ LabVIEW, which is supported by a C#.NET application and some command line scripts. The software solution is entirely targeted to Windows© platform. The tangible part of the system consists of a circulator for temperature management, HPGe and Barium Fluoride scintillators, a “delay” module Time-to-Amplitude Converter (TAC), and Differential Discriminators. The system formed at the end of this study can be used to run automated routines for DBAR and PALS analysis of any given sample material, separately or together. Doppler Broadening Annihilation Radiation (DBAR) and Positron Annihilation Lifetime Spectroscopy (PALS) techniques are based on annihilation of positrons and electrons upon interaction, emitting gamma rays. The positron would quickly reach a thermal equilibrium upon interaction with the material, before annihilation, and ‘catch’ an electron to annihilate with; forming a short living positronium. The annihilation occurring shortly afterwards would be Doppler-shifted, reason being the momentum of the formed positronium. This can be used in material analysis as the energies of emitted gamma rays would depend on the momentum distribution of electrons. PALS helps form the idea of internal structure of a material using the time it takes for the positron to annihilate after interacting with the material – annihilation would be delayed as much as the material is defected, as it would take longer for the positron to interact with an electron