Operative Score Research Articles

Merging academy and healthcare in the Public Health training of medical students

BackgroundPublic Health remains central to understand health and its determinants, and Public Health teams are essential for an integrated collaborative medical practice. However, current teaching of public health to medical students varies in the European Region though an investment in multidisciplinary workforce is recognised essential to deliver high quality public health services. A recent medical education curricula restructuring in the University of Porto Medical School resulted in the inclusion of a Public Health module linking academic teaching to field practice and provided the opportunity to make an initial appraisal of students’ perceptions.Case studyWe analysed final reports (n = 196), debriefing meetings notes (n = 2), and e-mails sent by students (n = 34) regarding the activities they observed or participated at, their contact with Public Health services’ teams, knowledge and critical appraisal, and opinion about the module. Students gained basic knowledge about how epidemiological surveillance, environmental health, health planning, and health promotion are performed in practice. They reported a better understanding of the roles and importance of Public Health services and its teams. Most considered that this module had an important role in their training. Some activities observed in the field lacked the needed standardisation to provide the students the feeling that core operations were experienced, which needs to be addressed in the future.ConclusionsPublic Health practice-based training within field institutions may bring a better understanding of the discipline and specialty for medical students. It may strengthen interconnectivity and coordination of healthcare agents, which may improve future medical practice with potential improvement of patient-centred care and in terms of public health response, and back their roles as health agents and decision-makers.

Existing in Discrete States: On the Techno-Aesthetics of Algorithmic Being-in-Time

Against a remarkable hardware oblivion in discussions of algorithmic intelligence, this article insists that algorithmic thought, or abstract computation, cannot be separated from its technological implementation. It requires a material medium for an abstract mechanism to become a procedural event. Temporality is both the condition and the limiting (and irritating) factor in the computational function. ‘Radical’ media archaeology is proposed as a method for such an analysis, and the neologism of techno lógos to describe some aspects of algorithmic reason which only unfold in the moment of its techno-processual coming-into-being. Some core operations, such as the time-discrete rhythm of actual computing algorithms, are discussed, where the ‘tempoReal’ flashes up in computing. In a wider sense, the time-discreteness of digital computing is related to an aesthetics of existence which acknowledges the machine element within human reasoning itself, while at the same time re-actualizing previous cultural techniques of non-narrative chronology. Turing the ‘man’ himself, in the sense of the Turing machine, can be addressed ‘itself’, in its archival sense as a sequence of expressions by symbols.

Shared Resource Laboratory Operations: Changes Made During Initial Global COVID-19 Lockdown of 2020.

Undoubtedly, the global pandemic caused by the SARS‐CoV‐2 virus has had a significant impact on Shared Resource Laboratories (SRL) operations worldwide. Unlike other crises (e.g., natural disasters, acts of war, or terrorism) which often result in a sudden and sustained cessation of scientific research usually affecting one or two cities at a time, this impact is being seen simultaneously in every SRL worldwide albeit to a varying degree. The alterations to SRL operations caused by the COVID‐19 pandemic can generally be divided into three categories: (1) complete shutdown, (2) partial shutdown, and (3) uninterrupted operations. In many cases, SRLs that remained partially or fully operational during the initial wave of global infections saw a concurrent increase in COVID‐19‐related research coming through their facilities. This forced SRLs to make rapid adjustments to core operations at the same time as infectious disease experts were still developing recommendations for the safety of frontline medical workers. Although many SRLs already had contingency plans in place, this pandemic has highlighted the importance of having such plans for continuity of service, if possible, during a crisis. Immediate changes have occurred in the way SRLs operate due to potential virus transmission and in line with this new “Best Practices” have been established, that is,social distancing, remote working, and technology‐based meetings and training. Many of these changes are likely to be in place for some time with the threat of further waves of infections toward the end of 2020 and into 2021. Some of these best practices, such as having many training resources recorded and available online, are likely to remain long‐term. Although many changes have been made in haste, these will alter the future operations of SRLs. In addition, we have learnt how to deal with future crises that may be encountered in the workplace. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.

Continuous k Nearest Neighbor Queries over Large-Scale Spatial–Textual Data Streams

Continuous k nearest neighbor queries over spatial–textual data streams (abbreviated as CkQST) are the core operations of numerous location-based publish/subscribe systems. Such a system is usually subscribed with millions of CkQST and evaluated simultaneously whenever new objects arrive and old objects expire. To efficiently evaluate CkQST, we extend a quadtree with an ordered, inverted index as the spatial–textual index for subscribed queries to match the incoming objects, and exploit it with three key techniques. (1) A memory-based cost model is proposed to find the optimal quadtree nodes covering the spatial search range of CkQST, which minimize the cost for searching and updating the index. (2) An adaptive block-based ordered, inverted index is proposed to organize the keywords of CkQST, which adaptively arranges queries in spatial nodes and allows the objects containing common keywords to be processed in a batch with a shared scan, and hence a significant performance gain. (3) A cost-based k-skyband technique is proposed to judiciously determine an optimal search range for CkQST according to the workload of objects, to reduce the re-evaluation cost due to the expiration of objects. The experiments on real-world and synthetic datasets demonstrate that our proposed techniques can efficiently evaluate CkQST.

Corporate Social Responsibility in The Sustainable Development Of Bangladesh: An Overview

Corporate Social Responsibility (CSR) is gaining fast global acceptance as a standard to assume environmentally sustainable and socially equitable business practices. Sustainable development, on the other, is emerged as a new development paradigm to meet the needs of the present without compromising the ability of future generations to meet their own needs. The study aims at investigating CSR activities in the way of sustainable development of Bangladesh. In doing so, extensive desk study and literature survey were done. Although internationally CSR is being practiced in the way of sustainable development widely, Bangladesh is still lagging behind. The difference between the world standard and the practice in Bangladesh shows the lacking here and the scope for further development. There are many challenges to implement CSR in Bangladesh. But its future and prospects are good. Companies can contribute to social and environmental objectives, through integrating CSR as a strategic investment into their core business strategy, management instruments and operations leading to the sustainable development of Bangladesh.

Preparing for the Unprecedented: The Association of Biomolecular Resource Facilities (ABRF) Community Coronavirus Disease 2019 (COVID-19) Pandemic Response Part 1: Efforts to Sustainably Ramp Down Core Facility Activities.

The coronavirus disease 2019 (COVID-19) pandemic has curtailed all but the most critical laboratory research in many institutions around the world. These unplanned and unprecedented operational changes have put considerable stress on every aspect of the research enterprise, from funding agencies to research institutes, individual and core laboratories, researchers, and research administrators, with drastic changes in demands and deliverables. The Association of Biomolecular Resource Facilities Core Administrators Network Coordinating Committee initiated a forum-wide discussion followed by a global survey to gain information on how institutions and, specifically, shared resource core facilities were responding to the COVID-19 pandemic. The survey aimed to identify shared resource core facility challenges and opportunities related to operational ramp downs, shutdowns, or research "pauses" during the COVID-19 pandemic, as well as new practices and resources needed to ensure business continuity. Although a number of positive outcomes from remote work hold promise for improved core operations, the survey results revealed a surprising level of unfamiliarity with business continuity planning for cores and limited coordination within institutions. Recommendations for business continuity planning include key stakeholders working together to assess risk, prioritize work, and promote transparency across campus.

Automatic Generation of High-Performance FFT Kernels on Arm and X86 CPUs

This article presents AutoFFT, a template-based code generation framework that can automatically generate high-performance FFT kernels for all natural-number radices. AutoFFT is based on the Cooley-Tukey FFT algorithm, which exploits the symmetric and periodic properties of the DFT matrix, as the outer parallelization framework. Because butterflies are the core operations of the Cooley-Tukey algorithm, we explore additional symmetric and periodic properties of the DFT matrix and formulate multiple optimized calculation templates to further reduce the number of floating-point operations for butterflies of arbitrary natural numbers. To fully exploit hardware resources, we encapsulate a series of optimizations in an assembly template optimizer. Given any DFT problem, AutoFFT automatically generates C FFT kernels using these calculation templates and converts them into efficient assembly kernels using the template optimizer. Through a series of experiments on Arm, Intel, and AMD processors, we show that AutoFFT-generated kernels can outperform those in Fastest Fourier Transform in the West (FFTW), the Arm Performance Libraries (ARMPL), and the Intel Math Kernel Library (MKL).

The Unofficial Guide to Surgery: Core Operations

The Unofficial Guide to Surgery: Core Operations

Where Has All the Complexity Gone? An Analysis of the Modern Surgical Resident Operative Experience

The operative experience of today's general surgery (GS) residents are changing. The Surgical Council on Resident Education (SCORE) was founded to provide a standardized, competency-based curriculum. We set out to evaluate resident operative experience in core and advanced operations. Accreditation Council for Graduate Medical Education (ACGME) national operative log reports from 2010 to 2018 were reviewed. Operative volume data for 344 operations were extracted and analyzed. Operations were designated as core, advanced, or undefined as listed by SCORE, and stratified as GS or subspecialty. National analysis utilizing ACGME operative log reports. All graduating general surgery residents between 2010 and 2018. A total of 10,118 residents completed GS training with an average of 1121.5 ± 29.3 total cases. Core operations comprised 80.5% of total volume while advanced comprised only 8.0%. The total core experience increased (+7.0 cases/year), while total advanced experience decreased (-1.4 cases/year) (p < 0.01 each). Compositional analysis among core operations revealed an increase in 9/13 GS domains and a decrease in 8/10 subspecialty domains (all p < 0.05). There has been an increase in core operative experience with a concurrent decrease in advanced operative experience of graduating GS residents. These findings highlight the continued narrowing of the operative experience for trainees, with increasing focus on GS and less on subspecialty domains. Ongoing efforts to look beyond operative volume to ensure competency of graduates will prove beneficial.

Thermal Hydraulic Analysis of a Nuclear Reactor due to Loss of Coolant Accident with and without Emergency Core Cooling System

This paper evaluates the thermal hydraulic behavior of a pressurized water reactor (PWR) when subjected to the event of Loss of Coolant Accident (LOCA) in any channel surrounding the core. The accidental break in a nuclear reactor may occur to circulation pipe in the main coolant system in a form of small fracture or equivalent double-ended rupture of largest pipe connected to primary circuit line resulting potential threat to other systems, causing pressure difference between internal parts, unwanted core shut down, explosion and radioactivity release into environment. In this computational study, LOCA for generation III+ VVER-1200 reactor has been carried out for arbitrary break at cold leg section with and without Emergency Core Cooling System (ECCS). PCTRAN, a thermal hydraulic model-based software developed using real data and computational approach incorporating reactor physics and control system was employed in this study. The software enables to test the consequences related to reactor core operations by monitoring different operating variables in the system control bar. Two types of analysis were performed -500% area break at cold leg pipe due to small break LOCA caused by malfunction of the system with and without availability of ECCS. Thermal hydraulic parameters like, coolant dynamics, heat transfer, reactor pressure, critical heat flux, temperature distribution in different sections of reactor core have also been investigated in the simulation. The flow in the reactor cooling system, steam generators steam with feed-water flow, coolant steam flow through leak level of water in different section, power distribution in core and turbine were plotted to analyze their behavior during the operations. The simulation showed that, LOCA with unavailability of Emergency Core Cooling System (ECCS) resulted in core meltdown and release of radioactivity after a specific time.

TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units.

Developed over the past decade, TeraChem is an electronic structure and ab initio molecular dynamics software package designed from the ground up to leverage graphics processing units (GPUs) to perform large-scale ground and excited state quantum chemistry calculations in the gas and the condensed phase. TeraChem's speed stems from the reformulation of conventional electronic structure theories in terms of a set of individually optimized high-performance electronic structure operations (e.g., Coulomb and exchange matrix builds, one- and two-particle density matrix builds) and rank-reduction techniques (e.g., tensor hypercontraction). Recent efforts have encapsulated these core operations and provided language-agnostic interfaces. This greatly increases the accessibility and flexibility of TeraChem as a platform to develop new electronic structure methods on GPUs and provides clear optimization targets for emerging parallel computing architectures.

Looking beyond COVID‐19: Crisis Leadership Implications for Chairs

Looking beyond COVID‐19: Crisis Leadership Implications for Chairs

Bioinformatics Core Survey Highlights the Challenges Facing Data Analysis Facilities.

Over the last decade, the cost of -omics data creation has decreased 10-fold, whereas the need for analytical support for those data has increased exponentially. Consequently, bioinformaticians face a second wave of challenges: novel applications of existing approaches (e.g., single-cell RNA sequencing), integration of -omics data sets of differing size and scale (e.g., spatial transcriptomics), as well as novel computational and statistical methods, all of which require more sophisticated pipelines and data management. Nonetheless, bioinformatics cores are often asked to operate under primarily a cost-recovery model, with limited institutional support. Seeing the need to assess bioinformatics core operations, the Association of Biomolecular Resource Facilities Genomics Bioinformatics Research Group conducted a survey to answer questions about staffing, services, financial models, and challenges to better understand the challenges bioinformatics core facilities are currently faced with and will need to address going forward. Of the respondent groups, we chose to focus on the survey data from smaller cores, which made up the majority. Although all cores indicated similar challenges in terms of changing technologies and analysis needs, small cores tended to have the added challenge of funding their operations largely through cost-recovery models with heavy administrative burdens.

Analyzing profitability ratios of leading global public oil and gas corporations

Subject. The article discusses the key profitability metrics of the largest public companies in the oil and gas (O&amp;G) industry from 2006 to 2018. The analysis encompasses ExxonMobil, Chevron, ConocoPhillips, Occidental Petroleum, Devon Energy, Anadarko Petroleum, EOG Resources, Apache, Marathon Oil, Imperial Oil, Suncor Energy, Husky Energy, Canadian Natural Resources, Royal Dutch Shell, BP, TOTAL, Eni, Equinor (Statoil), PetroChina, Sinopec, CNOOC, Petrobras, PJSC Gazprom, PJSC Rosneft Oil Company и PJSC LUKOIL. Objectives. The study assesses key profitability metrics of leading public corporations in oil and gas, identifies key trends in their developments as part of the analyzable period. We also determine what triggered such a transformation. Methods. We employed methods of comparative and financial-economic analysis, summarized official annual reports on financial and business operations prepared by major public O&amp;G corporations. Results. Upon the comprehensive analysis of balance sheets prepared by 25 O&amp;G corporations, we evaluated the dynamics of key profitability indicators in the public segment of O&amp;G industry and determined what triggered the transformation. Conclusions and Relevance. For the analyzable period, major public O&amp;G corporations were found to have become less profitable, especially manifesting this during the global financial and sectoral crisis. Some independent U.S. corporations are facing the most difficult situation. The public segment saw their profitability indicators fall, because the growth rate of operational expenses exceeded revenue predominantly due to costs of wear and tear, depletion and depreciation. What else affected the corporations was a considerable increase in the carrying amount of non-working assets. The public segment of O&amp;G industry was discovered to observe gradually lowering income tax burden per unit of net revenue from core operations.

Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1

Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1

System for Measuring of Radial Clearances in Gas-Turbine Engine with Self-Compensation of Temperature Effects on the Sensor

The paper presents the description of developed hardware, algorithms and software of the system for measuring of radial clearances between stator and blade tips in the compressor of gas-turbine engine. The measuring system implements the method with self-compensation of temperature effects on the eddy-current single-coil sensor with a sensitive element in the form of a conductor segment. Due to the self-compensation the number of used sensors and related mounting holes in the power plant stator was reduced. The core operations of the self-compensation are realized in real time on hardware-and-software level. This makes it possible to use the system for the detection of dangerous states of gas-turbine engines during power plants operation. The previously unexplored phenomenon of "not-complete compensation" of temperature effects is considered. The phenomenon is related to the special features of the conversion of the single-coil eddy-current sensor’s informative parameter in the measuring circuit. It manifests in the difference of conversion functions of the system’s measuring channels under normal and nominal temperatures. The paper provides the way of the effect elimination by means of program correction. The results of experimental researches of the working model of the measuring system are given. They characterize the metrological appropriateness and efficiency of the system. The experimentally obtained calibration characteristic approximated by polynomial function was used to determine the systematic part of the error as a difference between the given and the calculated radial clearance. The random error was evaluated by the deviation of measured codes from an average value in the sample that was obtained at the specified value of radial clearance for the fixed blade’s position relatively to the sensor’s sensitive element. The efficiency of the working model was evaluated on the laboratory equipment during the rotation of the electrically driven compressor wheel. The quantitative estimates of speed and accuracy of the working model of the measuring system were obtained during experimental researches. They confirmed the possibility of using the system for the detection of dangerous states of gas-turbine engines applied in the electrical power industry.

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

The on-board processing of remotely sensed hyperspectral images is gaining momentum for applications that demand a quick response as an alternative to conventional approaches where the acquired images are off-line processed once they have been transmitted to the ground segment. However, the adoption of this on-board processing strategy brings further challenges for the remote-sensing research community due to the high data rate of the new-generation hyperspectral sensors and the limited amount of available on-board computational resources. This situation becomes even more stringent when different time-sensitive applications coexist, since different tasks must be sequentially processed onto the same computing device. In this work, we have dealt with this issue through the definition of a set of core operations that extracts spectral features useful for many hyperspectral analysis techniques, such as unmixing, compression and target/anomaly detection. Accordingly, it permits the concurrent execution of such techniques reusing operations and thereby requiring much less computational resources than if they were separately executed. In particular, in this manuscript we have verified the goodness of our proposal for the concurrent execution of both the lossy compression and anomaly detection processes in hyperspectral images. To evaluate the performance, several images taken by an unmanned aerial vehicle have been used. The obtained results clearly support the benefits of our proposal not only in terms of accuracy but also in terms of computational burden, achieving a reduction of roughly 50% fewer operations to be executed. Future research lines are focused on extending this methodology to other fields such as target detection, classification and dimensionality reduction.

Bank efficiency in Bangladesh revisited: a slack-based network DEA approach

PurposeData envelopment analysis (DEA) calculates the efficiency of a business unit if all the inputs are creating outputs within a “black box.” Under traditional DEA, the detailed process of that business unit is ignored. However, a network DEA can explain the black box structure and provide efficiency results for sub-sections within any business process. This study aims to propose a network DEA model that explains a bank's total operation.Design/methodology/approachEarlier studies have focused only on bank efficiency ignoring this breakdown. This study departs from them by using a slack-based two-stage network DEA under a novel banking business perspective.FindingsThe results reveal that network DEA provides better benchmarking insights than the traditional DEA. As such, better benchmarking can guide both the banking industry managers and policy makers in Bangladesh.Originality/valueThe major contribution of this study includes dividing a bank's total operation efficiency into two sub-operations: “core operations – collecting deposits and giving loans” and “additional operations – fees, commissions and other services.”

Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching

Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed. Recently, several data-driven approaches based on neural networks have been proposed, most of which model the graph-graph similarity as the inner product of their graph-level representations, with different techniques proposed for generating one embedding per graph. However, using one fixed-dimensional embedding per graph may fail to fully capture graphs in varying sizes and link structures—a limitation that is especially problematic for the task of graph similarity computation, where the goal is to find the fine-grained difference between two graphs. In this paper, we address the problem of graph similarity computation from another perspective, by directly matching two sets of node embeddings without the need to use fixed-dimensional vectors to represent whole graphs for their similarity computation. The model, Graph-Sim, achieves the state-of-the-art performance on four real-world graph datasets under six out of eight settings (here we count a specific dataset and metric combination as one setting), compared to existing popular methods for approximate Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) computation.

Course Design Process to Create a Coordinated, Experiential, Integrated Core Operations Management Course for Business Majors

ABSTRACTWe describe our process for transforming a traditional, core operations management/supply chain management (OM/SCM) course into an experiential, integrated, and coordinated course. We developed a vison to create a course that all business majors would find engaging and relevant and that faculty would teach consistently and at the same level of rigor. A primary result was three new learning outcomes that focused on concept integration across operations, the supply chain, and the business. We specifically focused on decision trade‐offs and the effects of OM/SCM decisions on other parts of operations, the supply chain, and the business. The redesigned course has been implemented for three semesters. Our experiences offer new insights for redesigning and coordinating an OM/SCM core course and a process that can be implemented in other redesign efforts.