Application Of Established Methods Research Articles

Magnetocaloric effect in the Laves phases RCo2 (R = Er, Ho, Dy, and Tb) in high magnetic fields

The heavy rare-earth-based Laves phases are well-studied intermetallic materials that stand out for their remarkably high magnetocaloric effects, particularly at cryogenic temperatures. In this study, we present the findings of our comprehensive investigation of cobalt Laves phases RCo2 with R standing for erbium, holmium, dysprosium, and terbium. This includes the determination of the magnetocaloric effect by indirect methods using calorimetric and magnetization data. Furthermore, for the first time in these materials, we directly measured the adiabatic temperature change at high magnetic fields up to 20 T. The largest ΔTad value of 17 K, we obtained for ErCo2. Because the order of the transition significantly impacts the efficiency of thermodynamic cycles, we have also focused on determining the transition order in these materials. This was done through the application of established methods and a recently proposed quantitative criterion including the value of the local exponent n. Further, we compare our results with other materials using a straightforward material-based figure of merit - the temperature-averaged entropy change (TEC). Our results demonstrate the great potential of these materials for applications such as for magnetic hydrogen liquefaction.

Stabilizing control of Markovian jump systems with sampled switching and state signals and applications

AbstractThis article addresses the stabilization problem of continuous‐time Markovian jump systems (MJSs). Because of both switching and state signals sampled, a novel system with an exponential matrix characterizing the sampling effects is developed. It is used to be an auxiliary system and plays important roles in system analysis and synthesis. Sufficient stabilization conditions are presented and fully consider the sampling rate. The applications of established methods and results are applied to networked control in the presence of denial‐of‐service (DoS). The frequency and duration of DoS attacks ensuring stability of the closed‐loop system are characterized. Meanwhile, more special situations about particular transition rate matrix, sampling logic and improved mode‐dependent controller are considered respectively. The analysis results are significant and bring an important method to study the control for MJSs with sampled switching and state signals. A numerical example is offered to verify the effectiveness and superiority of the methods proposed in this study.

Novel approaches beyond standard immunofluorescence for kidney biopsies.

Immunofluorescence on frozen tissue (IF-F) utilizing antibodies against immunoglobulin (Ig) heavy and light chains (IgA, IgG and IgM, kappa and lambda) and components of classical and alternative complement pathways (C1q, C3c and C4) is the standard of renal pathology. However, conventional IF-F has limitations, particularly in nephropathies associated with organized and/or monoclonal Ig deposits. This review will discuss new applications of established methods beyond conventional IF-F and recent novel immunohistochemical methods. The combined application of paraffin immunofluorescence (IF-P) and IgG subtype staining excluded monotypic deposits in 62-66% of DNA J homolog subfamily B member 9-associated fibrillary glomerulonephritis (FGN) with apparent monotypic deposits by IF-F, whereas IF-P unmasks IgG deposits in a subset of cases of immunotactoid glomerulopathy. A novel IF technique targeting epitopes at the junction of the Ig heavy and light chains was introduced and unmasked polytypic deposits in a subset of glomerulonephritis with apparent monotypic deposits on IF-F. A recent study described the successful application of co-detection by indexing (CODEX) multiplexed IF to visualize more than a dozen target antigens within a single kidney tissue section. Finally, immunohistochemical protocols for detection of the novel antigens in membranous nephropathy have already entered the clinical practice of renal pathology. Novel ancillary techniques in renal pathology have the potential to significantly enhance our ability to evaluate renal biopsies.

New Criteria for Safety Distances During Pneumatic Pressure Testing of Vessels and Pipes

The industrial gas and liquefied natural gas (LNG) industries routinely perform equipment pressure testing pneumatically, rather than hydraulically, due to the necessity for keep piping and equipment dry. Hazards associated with potential failure of pneumatically pressurized equipment under test are well understood, but today, there is no commonly recognized industry guidance on recommended safety distances to protect personnel during such tests. Air Products, Air Liquide and BakerRisk have worked together to develop such guidance for the testing of vessels, process pipes and pipelines, and present in this article new, simple to use correlations, with results also presented in the form of lookup tables. These new correlations are based on the application of established methods, validated against previously published independent test data and new test data presented here for the first time. © 2018 American Institute of Chemical Engineers Process Saf Prog 38: e12025, 2019

PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?

Given the exponential growth of biomedical data, researchers are faced with numerous challenges in extracting and interpreting information from these large, high-dimensional, incomplete, and often noisy data. To facilitate addressing this growing concern, the "Patterns in Biomedical Data-How do we find them?" session of the 2017 Pacific Symposium on Biocomputing (PSB) is devoted to exploring pattern recognition using data-driven approaches for biomedical and precision medicine applications. The papers selected for this session focus on novel machine learning techniques as well as applications of established methods to heterogeneous data. We also feature manuscripts aimed at addressing the current challenges associated with the analysis of biomedical data.

How are health research priorities set in low and middle income countries? A systematic review of published reports.

BackgroundPriority setting is increasingly recognised as essential for directing finite resources to support research that maximizes public health benefits and drives health equity. Priority setting processes have been undertaken in a number of low- and middle-income country (LMIC) settings, using a variety of methods. We undertook a critical review of reports of these processes.Methods and FindingsWe searched electronic databases and online for peer reviewed and non-peer reviewed literature. We found 91 initiatives that met inclusion criteria. The majority took place at the global level (46%). For regional or national initiatives, most focused on Sub Saharan Africa (49%), followed by East Asia and Pacific (20%) and Latin America and the Caribbean (18%). A quarter of initiatives aimed to cover all areas of health research, with a further 20% covering communicable diseases. The most frequently used process was a conference or workshop to determine priorities (24%), followed by the Child Health and Nutrition Initiative (CHNRI) method (18%). The majority were initiated by an international organization or collaboration (46%). Researchers and government were the most frequently represented stakeholders. There was limited evidence of any implementation or follow-up strategies. Challenges in priority setting included engagement with stakeholders, data availability, and capacity constraints.ConclusionsHealth research priority setting (HRPS) has been undertaken in a variety of LMIC settings. While not consistently used, the application of established methods provides a means of identifying health research priorities in a repeatable and transparent manner. In the absence of published information on implementation or evaluation, it is not possible to assess what the impact and effectiveness of health research priority setting may have been.

Airport capacity impact of new aircraft concepts: how to manage airport diversity and individuality

Due to the worldwide increasing air traffic demand being challenged by restricted airport capacities, the future aircraft designs and operational concepts need to be evaluated regarding their general impact on airport capacity already at the concept stage within the design process. Looking at different airport situations worldwide, however, reveals a high individuality and variety in airport infrastructure and air traffic structures suggesting that any kind of capacity assessment may only be valid for one specific airport and that the provision of general results requires careful consideration of all influencing factors. This paper’s objective is to initially introduce new applications of established methods in the fields of parameter clustering and scenario analysis, thus making them available to define operational cases for airport capacity impact assessments of new aircraft concepts. Clustering methods are used to integrate all factors relevant for assessing aircraft’s and procedures’ impact on airport capacity such that meaningful general results can be achieved limiting the effort required despite the big variety and individuality of airports worldwide. Scenario techniques are outlined that can complement the approach in order to additionally address uncertainties in the operational airport environment within the timeframe beyond the new aircraft concept’s potential entry into service. A proof of concept study is provided to show the general possibilities of application for the proposed approach.

Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry

The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent on an accurate and rigorous assessment of quantitative changes in the relative abundance of the myriad of proteins typically present in a biological sample such as blood or tissue. In this review, we provide an overview of key statistical and computational issues relevant to bottom-up shotgun global proteomic analysis, with an emphasis on methods that can be applied to improve the dependability of biological inferences drawn from large proteomic datasets. Focusing on a start-to-finish approach, we address the following topics: 1) low-level data processing steps, such as formation of a data matrix, filtering, and baseline subtraction to minimize noise, 2) mid-level processing steps, such as data normalization, alignment in time, peak detection, peak quantification, peak matching, and error models, to facilitate profile comparisons; and, 3) high-level processing steps such as sample classification and biomarker discovery, and related topics such as significance testing, multiple testing, and choice of feature space. We report on approaches that have recently been developed for these steps, discussing their merits and limitations, and propose areas deserving of further research.