Previous Implementations Research Articles

Evaluating Barriers and Facilitators to the Uptake of mHealth Apps in Cancer Care Using the Consolidated Framework for Implementation Research: Scoping Literature Review

Mobile health (mHealth) solutions have proven to be effective in a wide range of patient outcomes and have proliferated over time. However, a persistent challenge of digital health technologies, including mHealth, is that they are characterized by early dropouts in clinical practice and struggle to be used outside experimental settings or on larger scales. This study aimed to explore barriers and enablers to the uptake of mHealth solutions used by patients with cancer undergoing treatment, using a theory-guided implementation science model, that is, the Consolidated Framework for Implementation Research (CFIR). A scoping literature review was conducted using PubMed (MEDLINE), Web of Science, and ScienceDirect databases in March 2022. We selected studies that analyzed the development, evaluation, and implementation of mHealth solutions for patients with cancer that were used in addition to the standard of care. Only empirical designs (eg, randomized controlled trials, observational studies, and qualitative studies) were considered. First, information on the study characteristics, patient population, app functionalities, and study outcomes was extracted. Then, the CFIR model was used as a practical tool to guide data collection and interpretation of evidence on mHealth uptake. Overall, 91 papers were included in the data synthesis. The selected records were mostly randomized controlled trials (26/91, 29%) and single-arm, noncomparative studies (52/91, 57%). Most of the apps (42/73, 58%) were designed for both patients and clinicians and could be used to support any type of cancer (29/73, 40%) and a range of oncological treatments. Following the CFIR scheme (intervention, outer setting, inner setting, individuals, process), multistakeholder co-design, codevelopment, and testing of mHealth interventions were identified as key enablers for later uptake. A variety of external drivers emerged, although the most relevant outer incentive fostering mHealth use was addressing patient needs. Among organizational factors likely to influence technology uptake, interoperability was the most prominent, whereas other providers' dimensions such as managerial attitudes or organizational culture were not systematically discussed. Technology-related impediments that could hamper the use of mHealth at the individual level were considered least often. The hype surrounding mHealth in cancer care is hindered by several factors that can affect its use in real world and nonexperimental settings. Compared with the growing evidence on mHealth efficacy, knowledge to inform the uptake of mHealth solutions in clinical cancer care is still scarce. Although some of our findings are supported by previous implementation research, our analysis elaborates on the distinguishing features of mHealth apps and provides an integrated perspective on the factors that should be accounted for implementation efforts. Future syntheses should liaise these dimensions with strategies observed in successful implementation initiatives.

Hardware implementation of Boolean functions based on the automaton model

Objectives. Currently, electronic control devices are increasingly being introduced into various household and production products. Microcontrollers of a wide variety of configurations are widely used as such devices. Another approach can be proposed where a control device with a standard structure is synthesized from typical integrated circuits and implements a Boolean function describing the required control actions.The purpose of the work is to investigate the possibility of implementing Boolean functions using devices with a standard structure, the design of which is based on the use of a discrete automaton model.Methods. The original Boolean function to be implemented is given as a disjunctive normal form. A binary decision diagram (BDD) is built for such function, optimized by the number of vertices, on the basis of which a graph of transitions of a synchronous Moore automaton with an abstract state is formed. Further, after performing the state encoding step of the machine, input information for flashing (programming) of the matrix memory of the read-only memory (ROM) is generated based on its transition table.Results. A device that implements a Boolean function based on an automaton model is synthesized from typical microcircuits. The main component is ROM, which, according to the standard structure of the device, is supplemented by a shift register, a state register, a trigger and three selectors of the initial and two final states.Conclusion. The process of designing a device with standard structure that implements the Boolean function, as a result, comes down to programming the ROM matrix memory based on an automaton transition table. The use of a reprogrammable ROM allows to change the functionality of the device while maintaining the previous circuit implementation. The disadvantage of such a device, as well as devices implemented on the basis of microcontrollers, is the low speed, the advantage is the possibility of use it in various products and devices, primarily for household purposes, which do not require a high-speed response to the change of input signal.

3D Printing for Repair: An Approach for Enhancing Repair

The availability and storage of spare parts are the main barriers to product repair. One possibility would be to 3D print spare parts, which would also enable the repair of products not intended to be repaired. Besides manufacturers, 3D printing spare parts is an interesting option for self-repair by consumers. However, the digitisation of spare parts for 3D printing is a challenge. There is little guidance on how to make a 3D-printed version of the original part. This paper establishes a framework through a literature review and experimental study to describe how to use 3D printing to produce spare parts for repair. Additionally, qualitative data coding was used to find the influence of previous experience, process implementation, and part complexity on the overall success of the 3D printing for repair (3DPfR) process. Our study showed that the 3DPfR process can be described as an iterative design for an additive manufacturing process that is integrated into a repair process. Additionally, it was found that the incorrect implementation of process steps was the most important predictor of the repair result. The steps that were performed incorrectly the most were synthesising design concepts (64%) and validating print quality (also 64%).

SpECTRE Cauchy-characteristic evolution system for rapid, precise waveform extraction

We give full details regarding the new Cauchy-characteristic evolution (CCE) system in spectre. The implementation is built to provide streamlined flexibility for either extracting waveforms during the process of a spectre binary compact object simulation or as a stand-alone module for extracting waveforms from worldtube data provided by another code base. Using our recently presented improved analytic formulation, the CCE system is free of pure-gauge logarithms that would spoil the spectral convergence of the scheme. It gracefully extracts all five Weyl scalars, in addition to the news and the strain. The spectre CCE system makes significant improvements on previous implementations in modularity, ease of use, and speed of computation.

Efficient medical image retrieval system using Geometric Invariant Point Bilateral Transformation (GIPBT)

Sensor rich environments assist CBIR system to provide granularity in detail for many applications. From sensor rich environments, CBIR can provide the process to exploit images that can integrate both search & retrieval mechanisms. Era of digital technology with Smart devices connected to internet so as to communicate is made possible with Internet of Things (IoT). As IoT deals with huge data in the form of images or videos, to be communicated between smart devices through IoT, deals with complex data processing. Proposed Image Retrieval System reduces the computation overhead for wide range of IoT applications. Medical Images are not only image which holds the pixel also the valuable information of medical diagnosis of every human being. Medical image retrieval has become an inescapable task in every hospital IT system due to the influx of medical pictures. A novel texture fusion technique and a rapid similarity measure metric were used in this study to create an efficient medical CBIR. Proposed feature extraction method of Geometric Invariant Point Bilateral Transformation (GIPBT) algorithm provides the texture information. In this method features are computed as point-to-point geometric information of bilateral filtered image. It automatically inoculates the universal information into the local descriptor and reinforces the feature depiction. To further enhance the descriptor, we used Histogram of Second Order Gradient (HSOG) feature extraction method. By use of these two features, we are performing the feature matching. To analyze and categorize the non-linearity between the features, we propose the kernalized discriminant analysis concept into the score prediction metric. Our proposed similarity measure method is Discriminant Kernalized Disparity (DKD) metric. In DKD, kernel applied Eigen decomposition is utilized for each feature set and disparity metric is evaluated. Finally the proposed system is simulated and evaluated with different medical database images and retrieval performance of Retrieval Efficiency, Normalized Average Rank of Retrieval, Precision, Recall, SSIM and processing Time. Some previous implementations are examined and compared to our results. The proposed method has produced Retrieval efficiency of 98.4%, NARR of 15.18%, Precision and Recall of 98.4%, F1 score of 98.39%, Correlation of 98.2%, SSIM of 97.92% and all at a speed of 30 s 5% more precision as well as less processing time are achieved compared to prior efforts by our proposed solution.

Windmapper: An Efficient Wind Downscaling Method for Hydrological Models

AbstractEstimates of near‐surface wind speed and direction are key meteorological components for predicting many surface hydrometeorological processes that influence critical aspects of hydrological and biological systems. However, observations of near‐surface wind are typically spatially sparse. The use of these sparse wind fields to force distributed models, such as hydrological models, is greatly complicated in complex terrain, such as mountain headwaters basins. In these regions, wind flows are heavily impacted by overlapping influences of terrain at different scales. This can have a great impact on calculations of evapotranspiration, snowmelt, and blowing snow transport and sublimation. The use of high‐resolution atmospheric models allows for numerical weather prediction (NWP) model outputs to be dynamically downscaled. However, the computation burden for large spatial extents and long periods of time often precludes their use. Here, a wind‐library approach is presented to aid in downscaling NWP outputs and terrain‐correcting spatially interpolated observations. This approach preserves important spatial characteristics of the flow field at a fraction of the computational costs of even the simplest high‐resolution atmospheric models. This approach improves on previous implementations by: scaling to large spatial extents O(1M km2); approximating lee‐side effects; and fully automating the creation of the wind library. Overall, this approach was shown to have a third quartile RMSE of 1.8 and a third quartile RMSE of 58.2° versus a standalone diagnostic windflow model. The wind velocity estimates versus observations were better than existing empirical terrain‐based estimates and computational savings were approximately 100‐fold versus the diagnostic model.

Highly Optimized Hardware Morphological Neural Network Through Stochastic Computing and Tropical Pruning

This work aimed to enhance a previous neural network hardware implementation based on an efficient combination of Stochastic Computing (SC) and Morphological Neural Networks (MNN). This enhancement focused on exploiting the natural ease of morphological neurons to be pruned in order to drastically shrink the hardware resources and increase the compactness of our network. That is why we extended our original hybrid two-layer neural network to classify the MNIST problem, a much more demanding benchmark with about 160,000 trainable parameters. The 92% of the weights of the morphological layer were discarded, allowing a drastic shrinkage of the hardware resources and power dissipation without degrading test accuracy. An extensive comparison with other recent neural networks designs shows that the proposed design achieves significant improvements in terms of energy efficiency, throughput and hardware resources.

Designing for Emotion Regulation Interventions: An Agenda for HCI Theory and Research

There is a growing interest in human-computer interaction (HCI) to envision, design, and evaluate technology-enabled interventions that support users’ emotion regulation. This interest stems in part from increased recognition that the ability to regulate emotions is critical to mental health, and that a lack of effective emotion regulation is a transdiagnostic factor for mental illness. However, the potential to combine innovative HCI designs with the theoretical grounding and state-of-the-art interventions from psychology has yet to be fully realised. In this article, we synthesise HCI work on emotion regulation interventions and propose a three-part framework to guide technology designers in making: (i) theory-informed decisions about intervention targets; (ii) strategic decisions regarding the technology-enabled intervention mechanisms to be included in the system; and (iii) practical decisions around previous implementations of the selected intervention components. We show how this framework can both systematise HCI work to date and suggest a research agenda for future work.

A continuous improvement implementation framework for manufacturing companies: a Delphi study-based approach for development and validation

PurposeDue to the absence of industry and region-specific implementation guides, change efforts, in particular continuous improvement implementations, continue to report high failure rates. The purpose of this paper is, therefore, to present a finalised implementation framework specifically tailored for manufacturing companies.Design/methodology/approachA conceptual framework derived from the existing literature is validated, through a Delphi study, by an expert panel to review its content and suitability for a practical application.FindingsThis paper proposes a finalised framework tailored to the practical needs of users. The results of the Delphi approach offer a phased implementation framework that provides an overview of the implementation process of continuous improvement initiatives and the key activities involved. The framework is suitable for use by both business leaders and continuous improvement practitioners with varying degrees of previous implementation experience.Research limitations/implicationsThe research was carried out in the UK and is specifically focused on the manufacturing industry. It is therefore not clear the applicability of the framework presented to other regions and industries. The framework, although validated through several phases of research, still requires practical application and testing.Originality/valueBusiness leaders and continuous improvement practitioners will benefit from having the necessary content presented in a manner that will promote its practical application. It has also been demonstrated that the framework is robust in its content, incorporating the literature findings as well as the experience of an expert panel encompassing 20 people.

Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation

The expansion of telecommunications incurs increasingly severe crosstalk and interference, and a physical layer cognitive method, called blind source separation (BSS), can effectively address these issues. BSS requires minimal prior knowledge to recover signals from their mixtures, agnostic to the carrier frequency, signal format, and channel conditions. However, previous electronic implementations did not fulfil this versatility due to the inherently narrow bandwidth of radio-frequency (RF) components, the high energy consumption of digital signal processors (DSP), and their shared weaknesses of low scalability. Here, we report a photonic BSS approach that inherits the advantages of optical devices and fully fulfils its “blindness” aspect. Using a microring weight bank integrated on a photonic chip, we demonstrate energy-efficient, wavelength-division multiplexing (WDM) scalable BSS across 19.2 GHz processing bandwidth. Our system also has a high (9-bit) resolution for signal demixing thanks to a recently developed dithering control method, resulting in higher signal-to-interference ratios (SIR) even for ill-conditioned mixtures.

Optimization of the memory reset rate of a quantum echo-state network for time sequential tasks

Quantum reservoir computing is a class of quantum machine learning algorithms involving a reservoir of an echo state network based on a register of qubits, but the dependence of its memory capacity on the hyperparameters is still rather unclear. In order to maximize its accuracy in time–series predictive tasks, we investigate the relation between the memory of the network and the reset rate of the evolution of the quantum reservoir. We benchmark the network performance by three non–linear maps with fading memory on IBM quantum hardware. The memory capacity of the quantum reservoir is maximized for central values of the memory reset rate in the interval [0,1]. As expected, the memory capacity increases approximately linearly with the number of qubits. After optimization of the memory reset rate, the mean squared errors of the predicted outputs in the tasks may decrease by a factor ∼1/5 with respect to previous implementations.

Climate change awareness in the Lezha area through artistic practice

The phenomenon of global warming and its triggering effects among which are the rising of water levels is a fact that is happening unstoppably day by day. Many coastal parts across the globe are in danger of disappearing. One of the cases that should be considered is the lagoon of Kune-Vain in the Lezha district. The whole area administered by the Municipality of Lezha has problems and will have problems with the floods of the lands that make up this administrative unit. At the same time, the cultural heritage sites of this municipality are endangered. Seen from a wider scale, man has his own responsibilities for these climate changes, such as rising temperatures and coastal erosion. For this reason, the awareness of the residents of this municipality but also that of tourists can play an important role in protecting the ecosystem and even more, in protecting the Kune-Vain lagoon. Based on several previous implementations. the lagoon in addition to a national park can be turned into an art park through the implementation of art pieces, which will serve to raise public awareness against climate change. The art pieces can give an even greater visibility to the lagoon, increasing the number of visitors. Artworks that have the same concern about climate changes that are already showed and are part of public art, they can be installed in the Kune – Vain lagoon, by use of sensors, colors, lights, etc. the installations will interact with the rising hypothesis of the increment of water; activating on the predictions of the sea level and rain flood. The works provide a visual reference of future sea level rise. One of the art works that will suit very well as part of the lagoon is “Lines (57° 59N, 7° 16’W)” (Niittyvirta & Aho, 2018), an interactive site specific light installation located Outer Hebrides in Scotland, UK. The project was a collaboration between Timo Aho & Pekka Niittyvirta. This installation will explore the catastrophic impact of our relationship with nature and its long term effects. The work provokes a dialogue on how the rising sea levels will affect coastal areas, its inhabitants and land usage in the future.

Introducing electrostatic stimulation to normal mode analysis.

Normal Mode Analysis (NMA) has proven to be a powerful tool to understand the macromolecular movement of protein complexes. While less precise than an all-atom molecular dynamics simulation, it allows to predict movements on timescales that are not yet accessible to MD simulations on a routine basis. In this work, we build on the previous successful implementation EnCoM to include electrostatic interactions in the NMA analysis. The motivation behind this step is to study the conformational rearrangements of voltage-gated ion channels that are sensitive to the electric field over the membrane. We show here preliminary results on the extension of the inter-residue interactions to include electrostatics and first results on the effect of an electric field on the normal modes of a voltage-gated potassium (Kv) channel.

An Adaptive Auto‐Reduction Solver for Speeding Up Integration of Chemical Kinetics in Atmospheric Chemistry Models: Implementation and Evaluation in the Kinetic Pre‐Processor (KPP) Version 3.0.0

AbstractKinetic integration of large and stiff chemical mechanisms is a computational bottleneck in models of atmospheric chemistry. It requires implicit solution of the coupled system of kinetic differential equations with time‐consuming construction and inversion of the Jacobian matrix. We present here a new version of the Kinetic Pre‐Processor (KPP 3.0.0) for fast integration of chemical kinetics featuring a range of improvements over previous versions in performance, diagnostics, versatility, and community openness. KPP 3.0.0 includes a new adaptive auto‐reduction solver to decrease the size of any mechanism locally and on the fly under conditions where full complexity is not needed, by partitioning species as “fast” or “slow” based on their local production and loss rates. Previous implementations of this adaptive solver suffered from excessive overhead in the repeated construction of the local Jacobian matrix or were hard‐wired to specific mechanisms. Here we retain the general applicability of the method to any mechanism and avoid overhead by using pre‐computed Jacobian matrix terms for the full mechanism and cropping the matrix locally to remove the slow species with no change in memory allocation. We apply this adaptive solver within KPP 3.0.0 to the GEOS‐Chem global 3‐D model of atmospheric chemistry and demonstrate a 32% reduction in solver time while maintaining a mean error lower than 1% for key species in the troposphere.

A New Global Nonlinear Force-Free Coronal Magnetic-Field Extrapolation Code Implemented on a Yin–Yang Grid

The solar magnetic field dominates and structures the solar coronal plasma. Detailed insights into the coronal magnetic field are important to understand most physical phenomena there. While direct, routine measurements of the coronal magnetic field are not available, field extrapolation of the photospheric vector-field measurements into the corona is the only way to study the structure and dynamics of the coronal field. Here we focus on global coronal structures traditionally modeled using spherical grids and synoptic vector magnetograms as boundary conditions. We developed a new code that performs nonlinear force-free magnetic-field extrapolations in spherical geometry. Our new implementation is based on a well-established optimization principle on a Cartesian grid and a single spherical finite-difference grid. In the present work, for the first time, the algorithm is able to reconstruct the magnetic field in the entire corona, including the polar regions. The finite-difference numerical scheme that was employed in previous spherical-code versions suffered from numerical inefficiencies because of the convergence of those grids on the poles. In our new code, we implement the so-called Yin–Yang overhead grid, the structure of which addresses this difficulty. Consequently, both the speed and accuracy of the optimization algorithm are improved compared to the previous implementations. We tested our new code using the well known semi-analytical model (Low and Lou solution). This is a commonly used benchmark for nonlinear force-free extrapolation codes.

ViPER+: Vehicle Pose Estimation Using Ultra-Wideband Radios for Automated Construction Safety Monitoring

Pose estimation of heavy construction equipment is the key technology for real-time safety monitoring in road construction sites where heavy equipment and workers on foot collaborate in proximity. Ultra-wideband (UWB) radios hold great promise among various sensing technologies for providing accurate object localization in indoor and outdoor environments. However, in a road construction environment with heavy vehicles and equipment, the performance of UWB radios drastically declines because of blockages in the transmission signal between the transmitter and receiver causing Non-Line of Sight (NLOS) situations. To address this deficiency, our study presents a real-time pose estimating system called ViPER+ that can overcome NLOS situations and accurately determine the boundary of heavy construction equipment with multiple UWB tags attached to the surface of the equipment. To remove the impact of NLOS signals, we introduced an input correction method prior to localization to correct the input of the localization algorithm. Evaluation of ViPER+ in a real construction environment indicates that embedding NLOS detection technique in UWB-based pose estimation resulted in 40% improvements in location accuracy and 25% improvement in update rate compared to its previous implementation (ViPER).

VLF: An R package for the analysis of very low frequency variants in DNA sequences.

Here, we introduce VLF, an R package to determine the distribution of very low frequency variants (VLFs) in nucleotide and amino acid sequences for the analysis of errors in DNA sequence records. The package allows users to assess VLFs in aligned and trimmed protein-coding sequences by automatically calculating the frequency of nucleotides or amino acids in each sequence position and outputting those that occur under a user-specified frequency (default of p = 0.001). These results can then be used to explore fundamental population genetic and phylogeographic patterns, mechanisms and processes at the microevolutionary level, such as nucleotide and amino acid sequence conservation. Our package extends earlier work pertaining to an implementation of VLF analysis in Microsoft Excel, which was found to be both computationally slow and error prone. We compare those results to our own herein. Results between the two implementations are found to be highly consistent for a large DNA barcode dataset of bird species. Differences in results are readily explained by both manual human error and inadequate Linnean taxonomy (specifically, species synonymy). Here, VLF is also applied to a subset of avian barcodes to assess the extent of biological artifacts at the species level for Canada goose (Branta canadensis), as well as within a large dataset of DNA barcodes for fishes of forensic and regulatory importance. The novelty of VLF and its benefit over the previous implementation include its high level of automation, speed, scalability and ease-of-use, each desirable characteristics which will be extremely valuable as more sequence data are rapidly accumulated in popular reference databases, such as BOLD and GenBank.

Fast spin-echo approach for accelerated B1 gradient-based MRI.

To expand on the previously developed -encoding technique, frequency-modulated Rabi-encoded echoes (FREE), to perform accelerated image acquisition by collecting multiple lines of k-space in an echo train. FREE uses adiabatic full-passage pulses and a spatially varying RF field to encode unique spatial information without the use of traditional B0 gradients. The original implementation relied on acquiring single lines of k-space, leading to long acquisitions. In this work, an acceleration scheme is presented in which multiple echoes are acquired in a single shot, analogous to conventional fast spin-echo sequences. Theoretical analysis and computer simulations investigated the feasibility of this approach and presented a framework to analyze important imaging parameters of FREE-based sequences. Experimentally, the multi-echo approach was compared with conventional phase-encoded images of the human visual cortex using a simple surface transceiver coil. Finally, different contrasts demonstrated the clinical versatility of the new accelerated sequence. Images were acquired with an acceleration factor of 3.9, compared with the previous implementation of FREE, without exceeding specific absorption rate limits. Different contrasts can easily be acquired without major modifications, including inversion recovery-type images. FREE initially illustrated the feasibility of performing slice-selective 2D imaging of the human brain without the need for a B0 gradient along the y-direction. The multi-echo version maintains the advantages that encoding provides but represents an important step toward improving the clinical feasibility of such sequences. Additional acceleration and more advanced reconstruction techniques could further improve the clinical viability of FREE-based techniques.

Grating couplers for guided Bloch surface waves

Photonic crystal ridge waveguides supporting guided Bloch surface waves (GBSWs) are a valuable and flexible platform for the development of integrated optical devices. However, coupling light into a GBSW from a single-mode optical fiber remains challenging. In this work, we study the main issues preventing the efficient excitation of GBSWs using grating couplers, and we present some solutions to increase the coupling efficiency. Our approaches do not require any additional fabrication step and can lead to a significant improvement compared to previous implementations. Further optimization along the lines illustrated in this work may allow one to achieve even larger coupling efficiency.

An Optimized Multi-layer Spiking Neural Network implementation in FPGA Without Multipliers

This paper presents an expansion and evaluation of the hardware architecture for the Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is a state-of-the-art, event-driven multi-layer Spiking Neural Network (SNN) architecture that offers an end-to-end, online, and local supervised training method. In previous work, ODESA was successfully implemented on Field-Programmable Gate Array (FPGA) hardware, showcasing its effectiveness in resource-constrained hardware environments. Building upon the previous implementation, this research focuses on optimizing the ODESA network hardware by introducing a novel approach. Specifically, we propose substituting the dot product multipliers in the Neurons with a low-cost shift-register design. This optimization strategy significantly reduces the hardware resources required for implementing a neuron, thereby enabling more complex SNNs to be accommodated within a single FPGA. Additionally, this optimization results in a reduction in power consumption, further enhancing the practicality and efficiency of the hardware implementation. To evaluate the effectiveness of the proposed optimization, extensive experiments and measurements were conducted. The results demonstrate the successful reduction in hardware resource utilization while maintaining the network's functionality and performance. Moreover, the power consumption reduction contributes to the overall energy efficiency of the hardware implementation.