General Programming Research Articles

Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling

Freva – Free Evaluation System Framework for Earth system modeling is an efficient solution to handle evaluation systems of research projects, institutes or universities in the climate community. It is a scientific software framework for high performance computing that provides all its available features both in a shell and web environment. The main system design is equipped with the programming interface, history of evaluations, and a standardized model database. Plugin – a generic application programming interface allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language. History – the configuration sub-system stores every analysis performed with the evaluation system in a database. Databrowser – an implemented meta data system with its advanced but easy-to-handle search tool supports scientists and their plugins to retrieve the required information of the database. The combination of these three core components, increases the scientific outcome and enables transparency and reproducibility for research groups using Freva as their framework for evaluation of Earth system models.

Vocational teachers` professional development in assessment for learning

ABSTRACT Formative assessment, including vocational education and training (VET), has been included in the assessment regulations in Norway since 2006. This study examines how a continuing education course entitled Assessment for learning in vocational education (15 credits) might contribute to vocational teachers’ professional development related to assessment for learning. Qualitative data was collected in two stages from vocational teachers participating on the course. The findings reveal that perceptions of assessment for learning and vocational formative assessment practice are developed through the lens of the general study programmes and are dominated by academic subjects in upper secondary schools. However, assessment for learning is embedded in vocational learning and teaching and needs to be voiced in the educational discourse in schools related to formative assessment. Vocational teachers’ professional development in assessment for learning should be based on the characteristics and attributes of vocational learning.

Evaluation of a survey for acute care programme directors on the utilisation of point-of-care ultrasound

PurposePoint-of-care ultrasound (POCUS) is ultrasound brought to the patient’s bedside and performed in ‘real time’ by the healthcare provider. The utility of POCUS to facilitate management of the acutely ill...

Physarum Powered Differentiable Linear Programming Layers and Applications

Consider a learning algorithm, which involves an internal call to an optimization routine such as a generalized eigenvalue problem, a cone programming problem or even sorting. Integrating such a method as layers within a trainable deep network in a numerically stable way is not simple – for instance, only recently, strategies have emerged for eigendecomposition and differentiable sorting. We propose an efficient and differentiable solver for general linear programming problems which can be used in a plug and play manner within deep neural networks as a layer. Our development is inspired by a fascinating but not widely used link between dynamics of slime mold (physarum) and mathematical optimization schemes such as steepest descent. We describe our development and demonstrate the use of our solver in a video object segmentation task and meta-learning for few-shot learning. We review the relevant known results and provide a technical analysis describing its applicability for our use cases. Our solver performs comparably with a customized projected gradient descent method on the first task and outperforms the very recently proposed differentiable CVXPY solver on the second task. Experiments show that our solver converges quickly without the need for a feasible initial point. Interestingly, our scheme is easy to implement and can easily serve as layers whenever a learning procedure needs a fast approximate solution to a LP, within a larger network.

Memory-aware framework for fast and scalable second-order random walk over billion-edge natural graphs

Second-order random walk is an important technique for graph analysis. Many applications including graph embedding, proximity measure and community detection use it to capture higher-order patterns in the graph, thus improving the model accuracy. However, the memory explosion problem of this technique hinders it from analyzing large graphs. When processing a billion-edge graph like Twitter, existing solutions (e.g., alias method) of the second-order random walk may take up 1796TB memory. Such high memory consumption comes from the memory-unaware strategies for the node sampling during the random walk. In this paper, to clearly compare the efficiency of various node sampling methods, we first design a cost model and propose two new node sampling methods: one follows the acceptance-rejection paradigm to achieve a better balance between memory and time cost, and the other is optimized for fast sampling the skewed probability distributions existed in natural graphs. Second, to achieve the high efficiency of the second-order random walk within arbitrary memory budgets, we propose a novel memory-aware framework on the basis of the cost model. The framework applies a cost-based optimizer to assign desirable node sampling method for each node or edge in the graph within a memory budget meanwhile minimizing the time cost of the random walk. Finally, the framework provides general programming interfaces for users to define new second-order random walk models easily. The empirical studies demonstrate that our memory-aware framework is robust with respect to memory and is able to achieve considerable efficiency by reducing 90% of the memory cost.

Mixed ℋ -Infinity and Passive Synchronization of Markovian Jumping Neutral-Type Complex Dynamical Networks with Randomly Occurring Distributed Coupling Time-Varying Delays and Actuator Faults

This article examines mixed ℋ -infinity and passivity synchronization of Markovian jumping neutral-type complex dynamical network (MJNTCDN) models with randomly occurring coupling delays and actuator faults. The randomly occurring coupling delays are considered to design the complex dynamical networks in practice. These delays complied with certain Bernoulli distributed white noise sequences. The relevant data including limits of actuator faults, bounds of the nonlinear terms, and external disturbances are available for designing the controller structure. Novel Lyapunov–Krasovskii functional (LKF) is constructed to verify the stability of the error model and performance level. Jensen’s inequality and a new integral inequality are applied to derive the outcomes. Sufficient conditions for the synchronization error system (SES) are given in terms of linear matrix inequalities (LMIs), which can be analyzed easily by utilizing general numerical programming. Numerical illustrations are given to exhibit the usefulness of the obtained results.

Attribute Homomorphic Encryption Scheme Based Lattice on AMI

With the development of smart grid, the Advanced Meter Infrastructure (AMI) is faced with the problems such increasing data collection and more frequency collection. Meanwhile, in order to improve user's participation and save power resources, the reliability and security of network communication are further challenged. The 5G network has the characteristics of large bandwidth, low power consumption and low delay, so it is imperative to build a network structure to meet the needs of AMI communication. The 5G network slicing takes full advantage of network virtualization and general API programming to allocate various hardware resources. With the further implementation of 5g network and the increase of information data collection, the risk of user privacy data exposure increases. In order to deal with the traditional problem of user privacy protection under the new network slice architecture, we need to further strengthen the protection of user privacy while building the corresponding slice network and slice resource management.

A method and tools for prototyping components of intelligent systems based on transformations

The paper describes a method and tools for prototyping components of intelligent systems. These means are the parts of the PESoT technology based on generative and visual programming as well as the model transformations. The proposed method redefines the main stages and models of the standardized model-driven approach, in particular, Model Driven Architecture in the context of developing knowledge bases and expert systems that use the logical rules formalism. The description of the method is presented. Proposed tools include languages and software. Specifically, the Rule Visual Modeling Language and Personal Knowledge Base Designer are used for knowledge bases engineering including modeling and codification; the Transformation Model Representation Language and Knowledge Base Development System support model transformations and the rapid creation of computation-independent models. Described tools were applied to solve problems in the field of reliability and safety of technical systems.

Creating Destinations for a Better Tomorrow: UN Development Aid for Cultural Tourism in the 1960s

Tourism is today considered as a crucial employment sector in many developing countries. In the growing field of historical tourism research, however, the relationships between tourism and development, and the role of international organizations, above all the UN, have been given little attention to date. My paper will illuminate how during the 1960s tourism first became the subject of UN policies and a praised solution for developing countries. Examples from expert consultancy missions in developing countries such as Ethiopia, India and Nepal will be contextualized within the more general debates and programme activities for heritage conservation and also the first UN development decade. Drawing on sources from the archives of UNESCO, as well as tourism promotion material, it will be possible to understand how tourism sectors in many so-called developing countries were shaped considerably by this international cooperation. Like in other areas of development aid, activities in tourism were grounded in scientific studies and based on statistical data and analysis by international experts. Examining this knowledge production is a telling exercise in understanding development histories colonial legacies under the umbrella of the UN during the 1960s and 1970s.

Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.

BackgroundAlthough the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health.ObjectiveThe aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health.MethodsSix databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist.ResultsThe literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data.ConclusionsAlthough the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.Trial RegistrationInternational Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048

P65 Improving the experiance of teaching for foundation doctors in a busy general surgery department

IntroductionIn the national training survey undertaken by the GMC in 2019 the general surgery teaching programme at North Bristol Trust scored 35.24 for local teaching; significantly lower than the national average. We aimed to introduce a teaching programme for foundation doctors on their general surgery rotations to help them meet the objectives set out by The UK Foundation Programme curriculum and gain further insight into general surgery and the roles of allied healthcare professionals.MethodsA survey consisting of Likert scale and open ended questions was distributed to foundation doctors at the start and end of their foundation programme placements from August to December 2019. During this period a general surgery teaching programme was introduced consisting of once weekly sessions led by allied healthcare professionals and once weekly consultant led sessions.ResultsThe number of foundation doctors agreeing with the statement ‘I think the General Surgical Unit is a good training and learning environment for foundation doctors rose by 33% (59% to 92%). There was an increase of 59% (24% to 83%) in the number of foundation doctors who felt they had good educational opportunities during their general surgical attachment.ConclusionThe implementation of a general surgery teaching programme involving specific consultant-led sessions and sessions led by allied healthcare professionals (bariatric specialists, amputation counsellors, palliative care specialists) is invaluable to foundation doctors. It retains the interest of surgically and non-surgically inclined foundation doctors and is an essential element of a general surgical placement in a teaching hospital.

GENERAL THEORETICAL FUNDAMENTALS OF FORMATION OF COMMUNICATIVE COMPETENCE OF THE HEAD OF THE INSTITUTION OF GENERAL SECONDARY EDUCATION IN THE PROCESS OF SELF-EDUCATION

The article considers the basics of formation of communicative competence for heads of general secondary education institutions in the process of self-education. The article also confirms the necessity of self-improvement and development for the work of educational managers.
 The author notes that the meaning of self-education is to meet the cognitive activity and the need of the leader in self-realization through continuing education. The content of self-education is to master the technique and culture of mental work with the ability to overcome problems and work independently on personal professional self-improvement.
 The process of self-education and its management in its structure consists of several successive stages, the basis of which is related to the preparation, implementation and receipt of the results of their activities. The main stages are determining the purpose of the activity and formulating priorities, collecting all information about the state of the object of management, defining tasks for general work, programming and forecasting, organization and correction, analysis of the effectiveness of what has been done.
 The main principles of self-education are: continuity, purposefulness, integration, general and professional culture, interconnection and continuity, creativity and variability, etc.
 The content of individual self-education of the manager includes systematic and continuous research of scientific, popular science, educational, artistic and other literature. Opportunity to directly participate in the functioning of school, district, regional, state and interstate methodological associations, webinars, conferences, pedagogical readings, development of individual problems and non-standard quick solution.

The coupling impact of subsystem interconnection and demand response on the distributed energy systems: A case study of the composite community in China

Distributed energy systems (DES) with hybrid sources has become a more effective way to increase the flexibility of energy supply. Current relevant research on DES design optimization strives to improve system efficiency and sustainability through independent application of subsystem interconnection and demand response. This paper aims to analyze the coupling impact of the above two strategies on the performance of DES. A general multi-objective mathematical programming model is proposed to work out the optimal design and operation scheme of DES, as well as flexible management of electricity, cooling energy, heating energy and steam. The minimum annual cost, carbon emission and energy demand deviation are set as objective functions. Additionally, the augmented ε-constraint method is introduced to search for the trade-off among three indicators. A composite community in China is taken as a case and three scenarios are carried out for comparison. Results reveal that the annual cost and carbon emission are further decreased by 7.3% and 32.1% under the coupling impact of subsystem interconnection and demand response. Eventually, the preferred strategy is analyzed in detailed to illustrate how this coupling strategy further improves the performance of DES.

COVID-19 pandemic implications in paediatric and congenital heart surgery in Brazil.

The Coronavirus Disease 2019 (COVID-19) pandemic negatively impacted global healthcare. Consequences in Pediatric and Congenital Heart Surgery programmes and mortality of congenital heart patients infected with severe acute respiratory syndrome coronavirus-2 (SARS-Cov-2) is still to be determined. To study the COVID-19 pandemic implications in Brazilian Pediatric and Congenital Heart Surgery programmes. We conducted a national online survey covering all states that perform Pediatric and Congenital Heart Surgery from 10 November to 22 November, 2020, using a Google forms questionnaire. We formulated questions related to impact on surgical volume, case-mix, and mortality. Then we asked about short-term post-operative COVID-19 infection and outcomes. We received responses from 46 centres representing all states where there were a Pediatric and Congenital Heart Surgery programme and all high-volume centres across the country. All but one centre experienced a significant decrease in surgical volume, and 23.9% of the responders revealed less than one-quarter of volume decrement. On the other hand, in over 70% of the centres, there was a significant surgical volume reduction. In addition to this, there was a shift in case-mix in 41 centres (89.1%) towards more complex cases. More than one-third of the responders revealed increased mortality in 2020 compared to previous years, and 43.5% of the programmes (20 centres) had at least one patient contaminated by SARS-Cov-2, accounting for 48 patients. Mortality in post-operative infected patients was 45.8% (22 patients). In general, Brazilian Pediatric and Congenital Heart Surgery programmes were severely affected by decreased surgical volume, unbalanced case-mix towards more complex cases, and increased mortality. Almost half of the programmes related post-operative COVID-19 contamination with high mortality.

Designing an e-learning tool to support health practitioners caring for patients taking multiple medications.

Background: Population ageing and improvements in healthcare mean the number of people living with two or more chronic conditions, or 'multimorbidity', is rapidly increasing. This presents a challenge to current disease-specific care delivery models. Adherence to prescribed medications appears particularly challenging for individuals living with multimorbidity, given the often-complex drug regimens required to treat multiple conditions. Poor adherence is associated with increased mortality, as well as wasted healthcare resources. Supporting medication adherence is a key priority for general practitioners (GPs) and practice nurses as they are responsible for much of the disease counselling and medication prescribing associated with chronic illnesses. Despite this, practical resources and training for health practitioners on how to promote adherence in practice is currently lacking. Informed by the principles of patient and public involvement (PPI), the aim of this research was to develop a patient informed e-learning resource to help GPs and nurses support medication adherence. Method: Utilising collective intelligence (CI) and scenario-based design (SBD) methodology, input was gathered from key stakeholders in medication adherence to gain insights into barriers to supporting people with multimorbidity who are receiving polypharmacy, strategies for overcoming these barriers, and user needs and requirements to inform the design of the e-learning tool. Results: In total, 67 barriers to supporting people who are taking multiple medications were identified across 8 barrier categories. 162 options for overcoming the identified barriers were then generated. This data was used in the design of a flexible e-learning tool for continuous professional development, that has been integrated into general practice and clinical education programmes as a supportive tool. Conclusions: Using CI and SBD methodology was an effective way of facilitating collaboration, idea-generation, and the co-creation of design solutions amongst a diverse group of stakeholders. This approach could be usefully applied to address other complex healthcare-related challenges.

MRIReco.jl: An MRI reconstruction framework written in Julia.

The aim of this work is to develop a high-performance, flexible, and easy-to-use MRI reconstruction framework using the scientific programming language Julia. Julia is a modern, general purpose programming language with strong features in the area of signal/image processing and numerical computing. It has a high-level syntax but still generates efficient machine code that is usually as fast as comparable C/C++ applications. In addition to the language features itself, Julia has a sophisticated package management system that makes proper modularization of functionality across different packages feasible. Our developed MRI reconstruction framework MRIReco.jl can therefore reuse existing functionality from other Julia packages and concentrate on the MRI-related parts. This includes common imaging operators and support for MRI raw data formats. MRIReco.jl is a simple to use framework with a high degree of accessibility. While providing a simple-to-use interface, many of its components can easily be extended and customized. The performance of MRIReco.jl is compared to the Berkeley Advanced Reconstruction Toolbox (BART) and we show that the Julia framework achieves comparable reconstruction speed as the popular C/C++ library. Modern programming languages can bridge the gap between high performance and accessible implementations. MRIReco.jl leverages this fact and contributes a promising environment for future algorithmic development in MRI reconstruction.

Robust deep hedging

We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black–Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches in highly volatile periods.

Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints.

Hospital beds are one of the most critical medical resources. Large hospitals in China have caused bed utilization rates to exceed 100% due to long-term extra beds. To alleviate the contradiction between the supply of high-quality medical resources and the demand for hospitalization, in this paper, we address the decision of choosing a case mix for a respiratory medicine department. We aim to generate an optimal admission plan of elective patients with the stochastic length of stay and different resource consumption. We assume that we can classify elective patients according to their registration information before admission. We formulated a general integer programming model considering heterogeneous patients and introducing patient priority constraints. The mathematical model is used to generate a scientific and reasonable admission planning, determining the best admission mix for multitype patients in a period. Compared with model II that does not consider priority constraints, model I proposed in this paper is better in terms of admissions and revenue. The proposed model I can adjust the priority parameters to meet the optimal output under different goals and scenarios. The daily admission planning for each type of patient obtained by model I can be used to assist the patient admission management in large general hospitals.

A kernel path algorithm for general parametric quadratic programming problem

It is well known that the performance of a kernel method highly depends on the choice of kernel parameter. A kernel path provides a compact representation of all optimal solutions, which can be used to choose the optimal value of kernel parameter along with cross validation (CV) method. However, none of these existing kernel path algorithms provides a unified implementation to various learning problems. To fill this gap, in this paper, we first study a general parametric quadratic programming (PQP) problem that can be instantiated to an extensive number of learning problems. Then we provide a generalized kernel path (GKP) for the general PQP problem. Furthermore, we analyze the iteration complexity and computational complexity of GKP. Extensive experimental results on various benchmark datasets not only confirm the identity of GKP with several existing kernel path algorithms, but also show that our GKP is superior to the existing kernel path algorithms in terms of generalization and robustness.

Designing an e-learning tool to support health practitioners caring for patients taking multiple medications

Background: Population ageing and improvements in healthcare mean the number of people living with two or more chronic conditions, or ‘multimorbidity’, is rapidly increasing. This presents a challenge to current disease-specific care delivery models. Adherence to prescribed medications appears particularly challenging for individuals living with multimorbidity, given the often-complex drug regimens required to treat multiple conditions. Poor adherence is associated with increased mortality, as well as wasted healthcare resources. Supporting medication adherence is a key priority for general practitioners (GPs) and practice nurses as they are responsible for much of the disease counselling and medication prescribing associated with chronic illnesses. Despite this, practical resources and training for health practitioners on how to promote adherence in practice is currently lacking. Informed by the principles of patient and public involvement (PPI), the aim of this research was to develop a patient informed e-learning resource to help GPs and nurses support medication adherence. Method: Utilising collective intelligence (CI) and scenario-based design (SBD) methodology, input was gathered from key stakeholders in medication adherence to gain insights into barriers to supporting people with multimorbidity who are receiving polypharmacy, strategies for overcoming these barriers, and user needs and requirements to inform the design of the e-learning tool. Results: In total, 67 barriers to supporting people who are taking multiple medications were identified across 8 barrier categories. 162 options for overcoming the identified barriers were then generated. This data was used in the design of a flexible e-learning tool for continuous professional development, that has been integrated into general practice and clinical education programmes as a supportive tool. Conclusions: Using CI and SBD methodology was an effective way of facilitating collaboration, idea-generation, and the co-creation of design solutions amongst a diverse group of stakeholders. This approach could be usefully applied to address other complex healthcare-related challenges.