Showing papers on "Software portability published in 2022"
TL;DR: The novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures are described.
Abstract: As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high-performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. We describe the novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.
117 citations
TL;DR: In this paper , the authors describe recent research progress related to the development and improvement of screen-printed electrodes (SPEs) and demonstrate the wide range of applications, also highlighting the market directions and the need for novel devices to be used by non-specialists.
Abstract: Portability is one of the essential keys in the development of modern analytical devices. Screen printing technology is an established technology for both chemical and biosensor development. Screen printing technology has been used to generate a variety of electronic sensors that are rapid, cost-effective, on-site, real-time, inexpensive, and practical for use in healthcare, environmental monitoring, industrial monitoring, and agricultural monitoring. This review aims to describe recent research progress related to the development and improvement of screen-printed electrodes (SPEs). We also demonstrate the wide range of applications, also highlighting the market directions and the need for novel devices to be used by non-specialists. Finally, we conclude and provide an overview of the constraints and future opportunities of SPEs in biosensor application.
88 citations
TL;DR: The authors examined the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort.
Abstract: The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.
76 citations
TL;DR: Kokkos as mentioned in this paper provides key abstractions for both the compute and memory hierarchy of modern hardware, such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations.
Abstract: As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high-performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. We describe the novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.
68 citations
TL;DR: This article propose a training representation based on the dependency paths between entities in a dependency tree which they call lexicalized dependency paths (LDPs), which is fast, efficient and transparent.
Abstract: Log-linear models and more recently neural network models used for supervised relation extraction requires substantial amounts of training data and time, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in a dependency tree which we call lexicalized dependency paths (LDPs). We show that this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviate the data sparsity problem. We apply lexicalized dependency paths to supervised learning using the ACE corpus and show that it can achieve similar performance level to other state-of-the-art methods and even surpass them on several categories.
61 citations
TL;DR: In this article , a comprehensive review of thermoelectric-assisted related materials and their medical devices is presented, and the challenges and future directions on medication-related thermolectrics are discussed.
55 citations
TL;DR: A lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models, and nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy.
Abstract: Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural network (CNN) requires high computational resources, limiting its portability. In this study, a lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models. The models were trained, validated, and tested using tomato leaf disease datasets split into an 8:1:1 ratio. The efficacy of the various attention modules in plant disease classification was compared in terms of the performance and computational complexity of the models. The performance of the models was evaluated using the standard classification accuracy metrics (precision, recall, and F1 score). The results showed that CNN with attention mechanism improved the interclass precision and recall, thus increasing the overall accuracy (>1.1%). Moreover, the lightweight model significantly reduced network parameters (~16 times) and complexity (~23 times) compared to the standard ResNet50 model. However, amongst the proposed lightweight models, the model with attention mechanism nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy. Although all the attention modules enhanced the performance of CNN, the convolutional block attention module (CBAM) was the best (average accuracy 99.69%), followed by the self-attention (SA) mechanism (average accuracy 99.34%).
48 citations
TL;DR: In this paper , a systematical review of recent advancements in electrochemical model development and parameterization is presented, where the classic pseudo-two-dimensional model and related model order reduction methodologies are summarized and analyzed.
39 citations
TL;DR: A middleware platform to mitigate the application portability issue among clouds is proposed and shows that adding this middleware mildly affects the latency, but it dramatically reduces the developer’s overhead of implementing each service for different clouds to make it portable.
Abstract: Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in a lack of interoperability and portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes a middleware platform to mitigate the application portability issue among clouds. A literature review is also conducted to analyze the solutions for application portability. The middleware allows an application to be ported on various platform-as-a-service (PaaS) clouds and supports deploying different services of an application on disparate clouds. The efficiency of the abstraction layer is validated by experimentation on an application that uses the message queue, Binary Large Objects (BLOB), email, and short message service (SMS) services of various clouds via the proposed middleware against the same application using these services via their native code. The experimental results show that adding this middleware mildly affects the latency, but it dramatically reduces the developer’s overhead of implementing each service for different clouds to make it portable.
39 citations
TL;DR: In this paper , the authors focus on laser absorption spectroscopy (LAS)-based sensors owing to their simple architecture, easy implementation, and market penetration, and detail recent advancements made in LAS variants using new laser sources and techniques.
37 citations
TL;DR: Li et al. as mentioned in this paper presented a reduced graphene oxide (rGO)-cloth based pressure sensor with laser-induced graphene (LIG) prepared by a simple and low-cost preparation process.
TL;DR: In this article , the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology.
Abstract: The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.
TL;DR: The GPT-3 as mentioned in this paper is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts, such as writing essays, answering complex questions, matching pronouns to their nouns, and conducting sentiment analyses.
Abstract: Generative pretrained transformer models have been popular recently due to their enhanced capabilities and performance. In contrast to many existing artificial intelligence models, generative pretrained transformer models can perform with very limited training data. Generative pretrained transformer 3 (GPT-3) is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts. Some examples include writing essays, answering complex questions, matching pronouns to their nouns, and conducting sentiment analyses. However, questions remain with regard to its implementation in health care, specifically in terms of operationalization and its use in clinical practice and research. In this viewpoint paper, we briefly introduce GPT-3 and its capabilities and outline considerations for its implementation and operationalization in clinical practice through a use case. The implementation considerations include (1) processing needs and information systems infrastructure, (2) operating costs, (3) model biases, and (4) evaluation metrics. In addition, we outline the following three major operational factors that drive the adoption of GPT-3 in the US health care system: (1) ensuring Health Insurance Portability and Accountability Act compliance, (2) building trust with health care providers, and (3) establishing broader access to the GPT-3 tools. This viewpoint can inform health care practitioners, developers, clinicians, and decision makers toward understanding the use of the powerful artificial intelligence tools integrated into hospital systems and health care.
TL;DR: In this article , advances in the detection of pathogenic bacteria using microfluidic biosensors were discussed, and key applications of microfluidity-based rapid bacteria detection were presented.
TL;DR: In this article , the main principles of lateral flow assays (LFAs), challenges, and prospects for more development in this field in sensing pathogenic bacteria have been summarized, and visually-read LFAs improvement to further progressive platforms have been explored by considering the prospects of this very flexible detection of pathogenic organisms.
TL;DR: In this article , the authors reviewed the recent advances of wind harvesters based on TENG, where the material, structure design, power management and the developed strategies to optimize the performance of TENG-based wind harvesting system were summarized.
TL;DR: In this paper, the authors demonstrate the unique challenges involved in preparing the VPIC code for operation at exascale, outlining important optimizations to make VPIC efficient on accelerators, and demonstrate the achieved performance-portability trade-off through a suite of studies on nine different varieties of modern pre-exascale hardware.
Abstract: VPIC is a general purpose particle-in-cell simulation code for modeling plasma phenomena such as magnetic reconnection, fusion, solar weather, and laser-plasma interaction in three dimensions using large numbers of particles. VPIC's capacity in both fidelity and scale makes it particularly well-suited for plasma research on pre-exascale and exascale platforms. In this article, we demonstrate the unique challenges involved in preparing the VPIC code for operation at exascale, outlining important optimizations to make VPIC efficient on accelerators. Specifically, we show the work undertaken in adapting VPIC to exploit the portability-enabling framework Kokkos and highlight the enhancements to VPIC's modeling capabilities to achieve performance at exascale. We assess the achieved performance-portability trade-off through a suite of studies on nine different varieties of modern pre-exascale hardware. Our performance-portability study includes weak-scaling runs on three of the top ten TOP500 supercomputers, as well as a comparison of low-level system performance of hardware from four different vendors.
TL;DR: In this article , a comprehensive overview of highly integrated energy conversion and storage system is presented, with an emphasis on all-in-one power system, which possesses the highest integration in this review.
Abstract: The vigorous development in the field of energy conversion and storage devices directly contributes to the full utilization and convenient use of clean energy. However, some drawbacks of independent energy conversion and storage devices, including unstable, insufficient energy output and dependence on external power supply, are difficult to overcome by self‐optimization, thus, hindering their further development and direct application. Coincidentally, the combination of above two devices can solve these problems, which conforms to their intrinsic needs for development. At the same time, the pursuit of portability and miniaturization also promotes the development of the power system toward a highly integrated direction. Therefore, we introduce several integration modes of energy conversion and storage systems, with emphasis on all‐in‐one power system, possessing the highest integration in this review. From the aspect of device configuration, working mechanisms and their performances, the all‐in‐one power systems based on different energy sources (e.g., mechanical, solar, thermal, and chemical energy) are discussed and analyzed. Finally, the design strategies are summarized and the potential development directions in the future are proposed. This review aims to provide a comprehensive overview of highly integrated energy conversion and storage system, and seeks to point out the opportunities and orientations of future research in this field.
TL;DR: An inverse design framework to approximate a general surface by a deployable origami structure is developed and provides not only a tool to design various deployable and retractable surfaces in engineering and architecture, but also a route to optimizing other properties and functionality.
TL;DR: This platform combined ultra-sensitivity, specificity, accuracy, portability, user-friendliness, and time-saving, which fulfills the actual requirements in the point-of-care testing of S. aureus and was also promising for further applications in the detection of other foodborne pathogens.
TL;DR: In this article , a self-powered visual tactile sensor is proposed, which consists of a high output triboelectric nanogenerator (TENG) and a visual light source, which can easily drive the light source to generate a light signal with a brightness of 9.8 cd m−2.
Abstract: Tactile sensors with visible light feedback functions, such as wearable displays and electronic skin and biomedical devices, are becoming increasingly important in various fields. However, existing methods cannot meet the application requirements for the tactile perception of intensity feedback and extended intersection due to their limited light‐mapping performance and insufficient portability. Herein, a freely constructible self‐powered visual tactile sensor is proposed, which consists of a high‐output triboelectric nanogenerator (TENG) and a visual light source. The transferred charge of the TENG is enhanced to 746 nC by the structural design of the triboelectric material and device, which can easily drive the light source to generate a light signal with a brightness of 9.8 cd m−2. Notably, the application of the TENG enables to realization visual sensing of the palm‐grasp state and strength feedback without an external power supply. This visual feedback and power‐free tactile sensors are expected to have potential application in the field of artificial intelligence as a new interactive medium for smart protective clothing and robotics.
TL;DR: In this article , Nitrocellulose (NC) membrane as a promising as substrate material has been extensively utilized to fabricate various paper-based biosensors owing to easy to bind protein with high efficiency.
TL;DR: In this article , a portable and visible detection platform for Staphylococcus aureus was established based on CRISPR/Cas12a, which integrated with nucleic acid isothermal amplification, CRISpl/Cas 12a detection, and lateral flow strips for final signal readout.
01 Jan 2022
TL;DR: In this article , a review analyzes various metabolites using molecularly imprinted polymer (MIP)-based electrochemical methods for their potential usage as POCT and biomarker research based on targeted metabolomics analysis requirements.
Abstract: In recent years, metabolomics, identification and profiling of metabolites, have gained broad interest compared to other omics technologies and are progressively being utilized for biomarker discoveries. Therefore, the application of metabolomics in different fields are increasing day by day because of its high throughput results. However, the application of metabolomics requires state-of-the-art analytical approaches for the analysis. The complexity and limited availability of these instruments are restricting parameters for applying metabolomics studies in routine analysis. This problem may be overcome with molecularly imprinted polymer (MIP)-based electro sensors since they have high selectivity, sensitivity, easy applicability, portability, and low cost. This is the final step before developing end point-of-care tests (POCT), which patients can easily apply. MIP sensors will have more applications in the targeted metabolomics analysis to develop POCT systems. This review analyzes various metabolites using MIP-based electrochemical methods for their potential usage as POCT and biomarker research based on targeted metabolomics analysis requirements. The future applications for the sensitive assay of metabolites in medicine and clinical trials are also discussed.
TL;DR: The face image acquisition with the 3dMD device is fast and accurate, but bulky and expensive, and the new smartphone applications combined with the TrueDepth sensors show promising results.
Abstract: OBJECTIVES
To compare three-dimensional facial scans obtained by stereophotogrammetry with two different applications for smartphone supporting the TrueDepth system, a structured light technology.
MATERIALS AND METHODS
Facial scans of 40 different subjects were acquired with three different systems. The 3dMDtrio Stereophotogrammetry System (3dMD, Atlanta, Ga) was compared with a smartphone (iPhone Xs; Apple, Cupertino, Calif) equipped with the Bellus3D Face Application (version 1.6.11; Bellus3D Inc, Campbell, Calif) or Capture (version 1.2.5; Standard Cyborg Inc, San Francisco, Calif). Times of image acquisition and elaboration were recorded. The surface-to-surface deviation and the distance between 18 landmarks from 3dMD reference images to those acquired with Bellus3D or Capture were measured.
RESULTS
Capturing and processing times with the smartphone applications were considerably longer than with the 3dMD system. The surface-to-surface deviation analysis between the Bellus3D and 3dMD showed an overlap percentage of 80.01% ± 5.92% and 56.62% ± 7.65% within the ranges of 1 mm and 0.5 mm discrepancy, respectively. Images from Capture showed an overlap percentage of 81.40% ± 9.59% and 56.45% ± 11.62% within the ranges of 1 mm and 0.5 mm, respectively.
CONCLUSIONS
The face image acquisition with the 3dMD device is fast and accurate, but bulky and expensive. The new smartphone applications combined with the TrueDepth sensors show promising results. They need more accuracy from the operator and more compliance from the patient because of the increased acquisition time. Their greatest advantages are related to cost and portability.
TL;DR: In this paper , the authors reviewed the research progress regarding the intelligence of biosensors in recent years, expound the research situation based on various biosensor intelligent technologies and equipment, and forecast the future applications of intelligent sensors.
TL;DR: The current work aims to demonstrate recent research progress related to the design, development, and improvement of portable electrochemical devices for detection of food contaminants.
Abstract: Traditional laboratory-based sensing strategies for food contaminant detection are often limited because they are time-consuming and expensive and require trained personnel, which makes them unsuitable for routine sensing. Therefore, the scientific and industrial community is showing enormous interest in the design and development of portable sensing devices for the on-site and point-of-care detection of food contaminants. Portability is one of the chief characteristic features of designing contemporary analytical devices. Portable devices have received tremendous attention, as these novel devices have advanced the field of sensing. Various sensing strategies have been utilized for on-site detection of food contaminants. Among these, portable electrochemical devices have emerged vigorously in the past few years. Scientists and industrialists have worked effortlessly to develop portable electrochemical devices for a minute amount of food contaminant detection in water bodies and food products. The current work aims to demonstrate recent research progress related to the design, development, and improvement of portable electrochemical devices for detection of food contaminants.
TL;DR: In this article , a method for fabricating micro-PADs using a portable thermal transfer printer that retains the convenience of wax printing was introduced, where the low cost, convenience, and portability of the thermal transfer printers make this approach an exciting prospect for replacing wax printing and facilitating the continued development of paper-based microfluidics.
Abstract: Paper-based microfluidic devices, also known as microPADs, are an emerging analytical platform with the potential to improve point-of-care diagnostics. MicroPADs are fabricated by patterning hydrophobic inks onto sheets of paper to create hydrophilic channels and test zones. One of the main advantages of microPADs is that they are inexpensive and simple to fabricate, making them accessible even to researchers with limited budgets or no prior fabrication expertise. Wax printing, where a solid ink printer is used to pattern wax on paper, has been the most convenient and popular method for fabricating paper-based microfluidic devices. Unfortunately, solid ink printers were discontinued in 2016 and are no longer available commercially. Here we introduce a method for fabricating microPADs using a portable thermal transfer printer that retains the convenience of wax printing. Devices fabricated by thermal transfer printing were comparable to devices fabricated via wax printing and laser printing. The low cost, convenience, and portability of the thermal transfer printer make this approach an exciting prospect for replacing wax printing and facilitating the continued development of paper-based microfluidics.
TL;DR: In this paper , a smartphone-based microplate reader is proposed to support laboratory-based optical techniques directly at the point of care (POC) using colorimetric, fluorescence, luminescence and turbidity analyses.
Abstract: The quantitative detection of different molecular targets is of utmost importance for a variety of human activities, ranging from healthcare to environmental studies. Bioanalytical methods have been developed to solve this and to achieve the quantification of multiple targets from small volume samples. Generally, they can be divided into two different classes: point of care (PoC) and laboratory-based approaches. The former is rapid, low-cost, and user-friendly; however, the majority of the tests are semiquantitative, lacking in specificity and sensitivity. On the contrary, laboratory-based approaches provide high sensitivity and specificity, but the bulkiness of experimental instruments and complicated protocols hamper their use in resource-limited settings. In response, here we propose a smartphone-based device able to support laboratory-based optical techniques directly at the point of care. Specifically, we designed and fabricated a portable microplate reader that supports colorimetric, fluorescence, luminescence, and turbidity analyses. To demonstrate the potential of the device, we characterized its analytical performance by detecting a variety of relevant molecular targets (ranging from antibodies, toxins, drugs, and classic fluorophore dyes) and we showed how the estimated results are comparable to those obtained from a commercial microplate reader. Thanks to its low cost (<$300), portability (27 cm [length] × 18 cm [width] × 7 cm [height]), commercially available components, and open-source-based system, we believe it represents a valid approach to bring high-precision laboratory-based analysis at the point of care.
TL;DR: This review aims to comprehensively discuss current progress on the development of polygenic risk scores, the factors that affect their generalizability, and promising areas for improving their accuracy, portable, and implementation.
Abstract: Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.