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2,682 results for “implementation”
Matrix multiplication software and results bundle for paper "Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library" for P^3MA submission
<p>This is the archive containing the matrix multiplication software and the results of the publication "<em>Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library</em>" submitted to the P^3MA workshop 2017.</p> <p><strong>The archive has the following content:</strong></p> <ul> <li>Source code for the (tiled) matrix multiplication in "src": <ul> <li>regular version in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-compatible-alpaka-0-1-0</li> <li>Commit: a63ba4810d6bfcca62c68dd57408af15028e78a3</li> </ul> </li> <li>forked version for XL in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-xl-workaround</li> <li>Commit: 1fee028eccb8cf7b677e8071233e08aa9f81846a</li> </ul> </li> </ul> </li> <li>The compiled binaries and the results of the tuning and scaling runs are in "runs" in sub folders for each type of run and architectures.</li> </ul>
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
Survey on the implementation of digital technology architecture in teacher training
<p>This file contains the results of a detailed survey exploring the implementation of digital technology architecture in teacher education. The survey was designed to assess how educational institutions are integrating advanced digital tools into their professional development programmes and the impact this has on teaching and learning. The data collected provides valuable insight into the effectiveness of these technologies in the educational environment, areas of success and challenges that still need to be addressed to optimise educator training in the digital age.</p>
Determining non-significant bits on a C++ implementation of the LeNet-5 convolutional neural network to be used for storing error correcting codes to protect weights and biases. Robustness assessment of the network after integrating the proposed codes.
<p>The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).</p><p>This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid.</p><p>It consists of the following layers:</p><ul><li><strong>conv1</strong>: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li><strong>relu1</strong>: Rectified Linear Unit (3@28x28).</li><li><strong>max1</strong>: Subsampling buy max pooling (3@14x14).</li><li><strong>conv2</strong>: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li><strong>relu2</strong>: Rectified Linear Unit (6@14x14).</li><li><strong>max2</strong>: Subsampling buy max pooling (6@7x7).</li><li><strong>fc1</strong>: Fully connected (294, 147)</li><li><strong>fc2</strong>: Fully connected (147, 10)</li></ul><p>The fault hypotheses for this work include the occurrence of:</p><ul><li><strong>S0</strong>/<strong>S1</strong>: multiple adjacent stuck-at-0 and stuck-at-1 faults to determine the least significant bits of weights and biases that could be used to store the proposed error correcting codes.</li><li><strong>BF</strong>: single, double, and triple bit-flip faults to assess the robustness of the considered CNN</li></ul><p>In the memory cells containing all the parameters of the CNN: </p><ul><li><strong>w</strong>: weights (float32)</li><li><strong>b</strong>: biases (float32)</li></ul><p>All the images (10000) from the MNIST dataset have been used as workload.</p><p>The weights and biases of the LeNet-5 architecture have been protected using six different error correcting codes that have been deployed in the least significant bits of these elements.</p><p>The parity check matrices (H = P I) that define these ECCs are:</p><ul><li><strong>SEC(32, 26)</strong> (Hamming) under a <i>classic policy </i>(see methodology below):</li></ul><p><i> 11010010001000011101101000 100000</i></p><p><i> 10101001000100011011010100 010000</i></p><p><i> 01100100100010010110110010 001000</i></p><p><i> 00011100010001001110001101 000100</i></p><p><i> 00000011110000100001111011 000010</i></p><p><i> 00000000001111100000000111 000001</i></p><ul><li><strong>SEC(23, 18)</strong> (Hamming) under a <i>conservative policy</i> (see methodology below):</li></ul><p><i> 111100001111000000 10000</i></p><p><i> 110011101000111000 01000</i></p><p><i> 101011010100100110 00100</i></p><p><i> 010110110010010101 00010</i></p><p><i> 001101110001001011 00001</i></p><ul><li><strong>SEC(13, 9)</strong> (Hamming) under an <i>aggressive policy </i>(see methodology below):</li></ul><p><i> 110111000 1000</i></p><p><i> 101100110 0100</i></p><p><i> 011010101 0010</i></p><p><i> 111001011 0001</i></p><ul><li><strong>DEC(32, 21)</strong> (low redundancy and reduced overhead DEC) under a <i>classic policy </i>(see methodology below):</li></ul><p><i> 111000011001010010000 10000000000</i></p><p><i> 110110000011101000000 01000000000</i></p><p><i> 101011000110000010001 00100000000</i></p><p><i> 100101101000110001000 00010000000</i></p><p><i> 011010101100100000100 00001000000</i></p><p><i> 010101010100001001010 00000100000</i></p><p><i> 001100110010010100100 00000010000</i></p><p><i> 000011110001000110010 00000001000</i></p><p><i> 000000001111001101001 00000000100</i></p><p><i> 000000000000111100111 00000000010</i></p><p><i> 000000000000000011111 00000000001</i></p><ul><li><strong>DEC(28, 18)</strong> (low redundancy and reduced overhead DEC) under a <i>conservative policy </i>(see methodology below):</li></ul><p><i> 111111000000000000 1000000000</i></p><p><i> 110100111100000000 0100000000</i></p><p><i> 110000100011110000 0010000000</i></p><p><i> 001110010011001100 0001000000</i></p><p><i> 101100001010101010 0000100000</i></p><p><i> 010001001101010110 0000010000</i></p><p><i> 001011000101101001 0000001000</i></p><p><i> 101000011000110101 0000000100</i></p><p><i> 010001110000011011 0000000010</i></p><p><i> 000010100110000111 0000000001</i></p><ul><li><strong>DEC(17, 9)</strong> (low redundancy and reduced overhead DEC) under an <i>aggressive policy </i>(see methodology below):</li></ul><p><i> 111110000 10000000</i></p><p><i> 111001100 01000000</i></p><p><i> 110101010 00100000</i></p><p><i> 101010110 00010000</i></p><p><i> 101101001 00001000</i></p><p><i> 100110101 00000100</i></p><p><i> 100011011 00000010</i></p><p><i> 110000111 00000001</i></p><p>This dataset contains the raw data obtained from:</p><ul><li>running exhaustive fault injection campaigns for increasingly multiple stuck-at faults in the least significant bits of all weights and biases (simultaneously) and for all the images in the workload.</li><li>running statistical fault injection campaigns for single, double, and triple bit-flip faults, randomly targeting the considered locations and images in the workload.</li></ul><h3>Files information</h3><ul><li><i>no_ecc </i>folder: Results obtained for the original (not protected) version of the CNN.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults.</li><li><i>sampling_SBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults.</li><li><i>sampling_TBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults.</li><li><i>locating_sensitive_bits </i>folder: Prediction obtained for all the images considered in the workload in presence of stuck-at-0/stuck-at-1 faults that simultaneously target the N least significant bits of all weights and biases. There is one file for each parameter of type of fault and range of targeted bits. Files for bits in the range [11, 0] are not included as they obtain eactly the same results as the Golden Run (faults do not alter the behaviour of the network).</li></ul></li><li><i>sec/classic</i>, <i>sec/conservative</i>, and <i>sec/aggressive</i> folders: They contain the results obtained for the CNN protected by SEC(32, 26), SEC(23, 18), and SEC(13, 9), respectively.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li><i>sampling_SBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li><li><i>dec/classic</i>, <i>dec/conservative</i>, and <i>dec/aggressive </i>folders: They contain the results obtained for the CNN protected by DEC(32, 21), DEC(28, 18), and DEC(17, 9), respectively.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li><i>sampling_TBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li></ul><h3>Methodology information</h3><p>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is <i>golden_run.csv</i> file.</p><p>To locate non-significant bits in weights and biases, fault injection experiments were executed targeting all elements of all parameters of the CNN using the following procedure:</p><ul><li>The initial mask targeted only the least significant bit</li><li>Until the mask targets all bits of the elements (32 bits as they are single-precision floating point values):<ul><li>Affect the bits (setting them to 0 or 1 in case of stuck-at-0 or stuck-at-1 faults) identified by the mask for all elements of all parameters.</li><li>Classify all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Remove the fault from the CNN by restoring the affected bits to its previous value.</li><li>Add the next adjacent bit to the mask, so it targets an additional least significant bit.</li></ul></li></ul><p>The analysis of the obtained results may help in determining which bits can be used to store an ECC:</p><ul><li>which bits never affect the behaviour of the CNN, as the predicted classification is exactly the same than in the absence of faults.</li><li>which bits midly affect the behaviour of the CNN, as although the predicted classifications differ from those in the absence of faults, the accuracy of the network is barely affected.</li><li>which bits greatly affect the behaviour of the CNN, as the accuracy of the network is significantly affected.</li></ul><p>Accordingly, three different policies have been identified for deploying an ECC using these bits:</p><ul><li><strong>Classic policy</strong>: The ECC protects as much bits as possible.</li><li><strong>Conservative policy</strong>: The ECC protects all those bits that may affect the prediction of the network.</li><li><strong>Aggressive policy</strong>: The ECC protects only those bits that significantly affect the accuracy of the network.</li></ul><p>After designing and deploying a single ECC and a double ECC for each of the identified policies, fault injection experiments were executed to verify their behaviour in the presence of faults.</p><p>Single and double ECCs were tested against single and double bit-flip, respectively (all faults should be tolerated,) and double and triple bit-flips, respectively (a correct bit could be erroneously flipped.)</p><p>Due to the heavy computational load of the decoders, statistical injection was used to run the required fault injection campaigns with a sample size (number of experiments) of 10000.</p><p>Each experiment consisted in:</p><ul><li>Randomly selecting the image to process, and the parameter, element, and bits (mask) to be targeted by the fault.</li><li>Affecting the bits (inverting them) identified by the mask.</li><li>Classifying the selected image of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li><strong>IMGID</strong>: Integer number identifying the considered image (1-9999).</li><li><strong>TENSORID</strong>: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.b, 3 - conv2.w, 4 - conv2.b, 5 - fc1.w, 6 - fc1.b, 7 - fc2.w, 8 - fc2.b).</li><li><strong>ELEMID</strong>: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - conv1.b, [0-74] - conv1.w, [0-5] - conv2.b, [0-149] - conv2.w, [0-146] - fc1.b, [0-43217] - fc1.w, [0-9] - fc2.b, [0-1469] - fc2.w).</li><li><strong>MASK</strong>: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty]).</li><li><strong>FAULT</strong>: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1).</li><li><strong>SOFTMAX</strong>: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li><strong>PRED</strong>: Integer number representing the category predicted for the processed image.</li><li><strong>LABEL</strong>: integer number representing the actual category for the processed image.</li></ul>
Dataset for publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction protocol implemented in XMapTools"
<p>Datasets for the publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction in XMapTools"</p>
Data for: Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid-ammonia nucleation
<p>Model dataset variables produced from the IFS and TM5 modules in EC-Earth3 version 3.3.4. which contains the control case and three experiments with the NPF lookup table. This paper is published at EGUshpere by journal: Geoscientific Model Development.</p> <p>The files contain:</p> <p>Compressed tar file of NetCDF data from IFS output for all four simulations. All IFS data have been averaged to monthly means from 6-hourly grib datasets. The post-process bash script which contains the function for the CDN and cloud effective radius weighted average towards cloud_time is found in the supplemented zendo link.</p> <p>NetCDF files from TM5 general output for each simulation. </p>
MERICS China Podcast: National People's Congress 2024: The state implements the party's decisions, with Nis Grünberg and Jacob Gunter
<p>The National People's Congress (NPC), China's top legislature, has held its yearly session in Beijing from the 5th to the 11th of March. This episode of the MERICS China Podcast looks at what we have learned from this year's NPC and its repercussions for China and Europe. <strong>Johannes Heller-John</strong> is joined by MERICS Lead Analysts <strong>Nis Grünberg</strong> and <strong>Jacob Gunter</strong>. They argue that science and technology as well as security took precedence over socio-economic issues. </p>
IMPLEMENTING AND EVALUATING A PAEDIATRIC EMERGENCY MANAGEMENT TRAINING PROGRAM FOR PRIMARY HEALTHCARE WORKERS IN ZARIA
<p>Nigeria continues to grapple with alarmingly high under-five mortality rates, with approximately 2,300 children dying daily due to preventable and treatable diseases such as pneumonia, malaria, and diarrheal illnesses. This crisis is particularly pronounced in rural areas, where childhood mortality rates are significantly higher than in urban centres. For many rural communities, primary healthcare (PHC) centres serve as the sole access point to medical services, making these centres critical to addressing this public health challenge. Despite their importance, PHC centres often face significant challenges, including inadequate infrastructure, limited access to essential medications, and a shortage of adequately trained health personnel.</p> <p>This study aimed to implement and evaluate a Paediatric Emergency Management Training Program for healthcare workers at PHC centres in Zaria, Kaduna State. The primary objective was to assess the effectiveness of the training in improving the competency of health workers in managing common paediatric emergencies, thereby enhancing the overall quality of care provided at these centres.</p> <p>A cross-sectional descriptive study was conducted involving 139 healthcare workers from selected PHC centres in Sabon Gari and Zaria Local Government Areas, utilizing a multi-stage random sampling technique. Data collection was performed using a pre-tested, semi-structured, self-administered questionnaire designed to evaluate the participants' baseline knowledge, followed by an assessment after the training intervention. The data were analysed using SPSS version 21, with chi-square tests employed to examine the associations between variables. Results were presented using tables and charts for clarity.</p> <p>The findings revealed a generally low baseline competency in managing common paediatric emergencies among the healthcare workers. Specifically, 66 (48.5%) of the respondents demonstrated very poor knowledge regarding the causes of common paediatric emergencies, 32 (23.5%) had poor knowledge, 34 (25.0%) had good knowledge, and only 4 (2.9%) of the respondents exhibited excellent knowledge. Similarly, knowledge regarding the treatment of these emergencies was also lacking, with 46.7% of respondents showing very poor understanding, 34.1% having poor knowledge, 16.3% demonstrating good knowledge, and only 3.0% showing excellent knowledge. A statistically significant relationship was observed between academic qualifications and knowledge levels in both causes (p = 0.004) and treatment (p = 0.000) of paediatric emergencies.</p> <p>The low baseline knowledge underscores the critical need for targeted training interventions. The study highlights the importance of establishing a sustained partnership between the Department of Paediatrics at Ahmadu Bello University Teaching Hospital and the Departments of Primary Health Care in Zaria and Sabon Gari Local Governments. Regular training workshops, held quarterly, are recommended to continuously equip PHC workers with the necessary skills to effectively manage paediatric emergencies, ultimately contributing to a reduction in childhood mortality rates in the region.</p>
Survey results - Implementation of interprofessional collaboration
<p>This dataset contains the variables, values and results for a survey run among the students of the pilot course "Interprofessionelle Gesundheitsversorgung - online" to determine effects of the course on the implementation of interprofessional collaboration in students' professional environment.</p> <p>The dataset is licensed under a Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>) license.</p>
Wireframes for the design and implementation of the National Edition of Aldo Moro's works website
<p>A series of low-fidelity graphical models, realized in Figma and used to drive the information architecture and content strategy of the website.</p>
Experiences Implementing and Utilizing a Notional Machine in the Classroom
<p><strong>Notional Machine Specification for:</strong></p> <p><br> Paul E. Dickson, Tim Richards, and Brett A. Becker. 2022. Experiences Implementing and Utilizing a Notional<br> Machine in the Classroom. In Proceedings of the 53rd ACM Technical Symposium on Computer Science<br> Education V. 1 (SIGCSE 2022), March 3–5, 2022, Providence, RI, USA. ACM, New York, NY, USA, 7 pages.<br> https://doi.org/10.1145/3478431.3499320<br> </p> <p><strong>When referring to this dataset, please cite the above article. That contains the DOI of this dataset. Please do<br> not cite this dataset directly without citing the article.</strong></p>
Open Science: Epistemologies, science policy implementation and digitalisation
<p>This workshop explores Open Science and the promises and challenges for science and society. After briefly covering the definition, history and application of Open Science policies, we discuss why they are relevant and what barriers and concerns there are for implementation. The workshop engages with the challenges of universalising access to knowledge in a world with a plurality of epistemic communities and developmental agendas. Lastly, we discuss the role that digitalisation plays in implementing Open Science policies and what this means for sustainable development.</p>
Replication Package for "Evaluating SZZ Implementations Through a Developer-informed Oracle"
<p>This is the replication package for the paper "Evaluating SZZ Implementations Through a Developer-informed Oracle" published in the 43rd International Conference on Software Engineering (ICSE 2021). <a href="https://arxiv.org/abs/2102.03300">https://arxiv.org/abs/2102.03300</a></p>
Implementation and sensitivity analysis of a Dam-Reservoir OPeration model (DROP v1.0) over Spain - Supplement
<p>Supplement of the following scientific paper submitted in GMD :</p> <p><strong>Sadki M., Munier S., Boone A., Ricci S. : Implementation and sensitivity analysis of a Dam-Reservoir OPeration model (DROP v1.0) over Spain, Geoscientific Model Development, 2022. </strong></p> <p> </p>
Development and implementation of a tool for measuring the training effectiveness of the patient-centered consultation model
<p><strong>Background:</strong> The patient-centered consultation model comprises four elements: exploring health, illness, and disease experiences, understanding the whole person, finding common ground, and enhancing the patient-doctor relationship. This method is taught at the course in general practice at Copenhagen University. The aim of the study was to develop a simple tool consisting of a questionnaire about the patient-centered elements and a test video consultation. The outcome is the change in the students’ ability to identify these elements. Used as a pre-course and post-course test it can inform the teachers which elements of the patient-centered consultation need intensifying in the teaching.<br> <strong>Methods:</strong> The students from a course in general practice volunteered to participate in all steps of the development. They took part in individual interviews to select items from an already existing questionnaire (DanSCORE). The preliminary questionnaire was tested for face and content validity, pilot-tested and tested for test-retest reliability. All video consultations were transcribed and assessed for patient-centered elements through a conversation analysis. The videos showed medical students seeing real patients.<br> <strong>Results:</strong> The preliminary version of the questionnaire (called DanOBS) <a href="#_msocom_1">[CM1]</a> <a href="#_msocom_2">[MJ2]</a> had 23 items. In the subsequent interviews, items were reduced to 17, each with three response options. After the pilot test, the questionnaire was further reduced to 13 items, all strictly relevant to the model and with two response options. The final questionnaire had acceptable test-retest reliability. The number of test consultation videos underwent a reduction from six videos to one.<br> <strong>Conclusions:</strong> The DanOBS combined with a test video consultation, used as a pre-and post-course test demonstrates for teachers which elements in the patient-centered consultation need to be intensified in the teaching.</p> <p> </p> <p> </p>
Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda
<p>Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda":</p> <p><strong>Figure 6: </strong>Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country.</p> <p><strong>Figure 8:</strong> Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).</p>
Patient and GP experiences of implementing a medication review intervention in older people with multimorbidity: process evaluation of the SPPiRE trial
<p>Abstract</p> <p><strong>Background: </strong>The SPPiRE cluster randomised controlled trial (RCT) found that a GP delivered medication review that incorporated screening potentially inappropriate prescriptions (PIP), a brown bag review and a patient priority assessment, resulted in a significant but small reduction in the number of medicines and no significant reduction in PIP.</p> <p><strong>Objective: </strong>To explore the experiences of GPs and patients engaged in the SPPiRE intervention and the potential for system wide implementation.</p> <p><strong>Design: </strong>Mixed methods process evaluation; quantitative data was collected from the SPPiRE intervention website and qualitative data via semi-structured interviews.</p> <p><strong>Setting and participants:</strong> 51 general practices throughout Ireland, and 404 participants with multimorbidity aged ≥65 years, prescribed ≥15 medicines participated in the RCT. Qualitative data was collected with purposive samples of intervention GPs (18/26) and patients (27/208). </p> <p><strong>Methods: </strong>Quantitative data was analysed descriptively, qualitative data thematically and both were integrated using a triangulation protocol.</p> <p><strong>Results: </strong>The analysis generated three themes, intervention implementation, mechanisms of action, and both were underpinned by the theme of context. One fifth of patients had no review, primarily due to insufficient GP time. The brown bag review component resulted in the most deprescription of medications. GPs felt it easier to change medicines if the patient was well known to them, and patients were generally receptive to change. GPs identified lack of integration into practice software systems and resources as barriers to future implementation.</p> <p><strong>Conclusion: </strong>Consideration of implementation of successful interventions is key to informing policy and integration into clinical practice. GPs and patients viewed the intervention positively, but implementation will depend on resourcing and integration into practice software systems.</p> <p>Trial registration number: <a href="https://doi.org/10.1186/ISRCTN12752680">ISRCTN12752680</a></p>
DATASET: What is the best scale for implementing anaerobic digestion according to environmental and economic indicators?
<p>DATASET: What is the best scale for implementing anaerobic digestion according to environmental and economic indicators?</p> <p>Journal of Water Process Engineering, Volume 35, June 2020, 101235</p> <p>https://doi.org/10.1016/j.jwpe.2020.101235</p> <p> </p>
Video Examples from: Establishment and Implementation of Guidelines for Narrative Audio-based Room-scale Virtual Reality using Practice-based Methods
<p>This video accompanies our paper: Popp, C. and Murphy, D.T., "Establishment and Implementation of Guidelines for Narrative Audio-based Room-scale Virtual Reality using Practice-based Methods", held at the 2022 AES International Conference on Audio for Virtual and Augmented Reality.</p> <p>Room-scale Virtual Reality (VR) presents sound designers with new challenges to tell stories with audio in games with player-driven narratives. These challenges arise from the player moving in and interacting with the virtual environment. The paper performs a small scoping review of VR/non-VR games and associated literature to identify issues and solutions to the placement of speech-based audio using practice-based research methods. The review leads to the proposition of design guidelines and strategies for their implementation. The paper advocates that each instance of speech-based audio should be short, interactive, and complemented by non-speech audio. Furthermore, each instance’s spatial, interactive, visual, aural, and narrative representation should be considered in combination. The paper also suggests that 3D-binaural audio informed by physics can aid storytelling and make virtual environments player-responsive.</p> <p>The paper is part of the research project SuperCharging Audio Storytelling: 3D Audio in Virtual Reality, funded by UK Arts and Humanities Research Council (AHRC) XR Stories Creative Industries Cluster project, grant no. AH/S002839/1. More information about the project can be found at <a href="https://xrstories.co.uk/project/supercharging-audio-storytelling-3d-audio-in-virtual-reality/">XR Stories</a>. Firelight Technologies Pty Ltd and Unity Technologies kindly provided non-commercial licenses for research purposes as part of this research project.</p>
Biomimetic robotic skin implemented with hydrogel-elastomer hybrids and tomographic imaging methods
<p>Human skin perceives physical stimuli applied to the body and mitigates the risk of physical interaction through its soft and resilient mechanical properties. Social robots would benefit from whole-body robotic skin (or tactile sensors) resembling human skin in realizing a safe, intuitive, and contact-rich interaction with humans. However, existing soft tactile sensors show several drawbacks (complex structure, poor scalability, and fragility), which limit their application in whole-body robotic skin. Here, we introduce biomimetic robotic skin based on hydrogel-elastomer hybrids and tomographic imaging. The developed skin consists of tough hydrogel and silicone elastomer forming a skin-inspired multilayer structure, achieving sufficient softness and resilience for protection. The sensor structure can also be easily repaired with adhesives even after severe damage (incision). For multimodal tactile sensation, electrodes and microphones are deployed in the sensor structure to measure local resistance changes and vibration due to touch. The ionic hydrogel layer is deformed due to an external force, and the resulting local conductivity changes are measured via electrodes. The microphones also detect the vibration generated from touch to determine the location and type of dynamic tactile stimuli. The measurement data are then converted into multimodal tactile information through tomographic imaging and deep neural networks. We further implement a sensorized cosmetic prosthesis, demonstrating that our design could be used to implement deformable or complex-shaped robotic skin.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.