bluuebunny/crossref_metadata_2025 huggingface dataset:
PP34 Prehospital feedback & follow-up: a review of current practice in the United Kingdom
Authors: Wilson Caitlin , Janes Gillian , Lawton Rebecca , Benn Jonathan
Published: August 2022

Background

Prehospital feedback is increasingly receiving attention from clinicians, managers and researchers. The effectiveness of feedback in changing professional behaviour and improving clinical performance is strongly evidenced across a range of healthcare settings, but this has not yet been replicated within the prehospital context. Without a firmer evidence base, development in practice relies on isolated initiatives with no clear intervention model or evaluative framework. The aim of this study was to understand how UK ambulance services are currently meeting the challenge of providing prehospital feedback.

Methods

This was a national survey, which formed the first part of a realist evaluation of prehospital feedback. The survey used purposive and snowball sampling. It was advertised on social media and sent to prehospital feedback initiative leads identified through grey literature and networking (n=35). In addition n=8 NHS Ambulance Trusts formally supported this study and identified further initiatives (n=17). Data was analysed using content analysis.

Results

We received responses from n=41 unique prehospital feedback initiatives, equating to a response rate of 79%. Of these n=27 were currently active. Responses were received from within 10 of the 13 NHS Ambulance Trusts in the UK. Initiatives most commonly provided individual patient outcome feedback to individual frontline ambulance staff members using email format, triggered by a request for feedback (i.e. a pull initiative). Common facilitators were engaged and responsive collaborators, with barriers being information governance and the time and resources involved.

Conclusions

This study suggests that the majority of UK Ambulance Trusts currently have examples of prehospital feedback initiatives within their footprint. In contrast with findings from a recent systematic review, there appear to be more initiatives providing individual patient outcome feedback than performance feedback. To further understand how prehospital feedback initiatives work, we will conduct in-depth case studies of 4 initiatives and develop a programme theory as part of our ongoing realist evaluation.
PP40 Could we identify the prehospital optimal shock energy for defibrillation? Answering the question that is POSED
Authors: Pocock Helen , Deakin Charles D , Lall Ranjit , Perkins Gavin D
Published: August 2022

Background

Defibrillation of a shockable rhythm improves patients’ chance of survival following cardiac arrest. Evidence is limited regarding optimal shock energy level and delivery strategy. Most studies are based on old resuscitation regimes and use a variety of endpoints, making meaningful comparison difficult. The POSED study seeks to determine whether it is feasible to conduct a randomised, pragmatic clinical effectiveness trial in UK ambulance services to identify the optimal energy for defibrillation.

Methods

This is a pragmatic, cluster-randomised, open label, controlled trial. Defibrillators within a single UK Ambulance service will be randomised to one of three groups delivering shock strategies in current UK use. Adult (≥18 yrs) patients will be eligible if they sustain a cardiac arrest in the out-of-hospital environment that requires defibrillation. Defibrillators will be randomised to deliver one of the following strategies to 90 participants (30 in each group): 120-150-200J, 150-200-200J, 200-200-200J. Relatives of non-survivors of trial participation will be actively informed. Focus groups will explore the experiences of paramedics recruiting patients. The primary feasibility outcome is the proportion of eligible patients who received the randomised study intervention.

Results

Baseline variables will be reported. The secondary feasibility outcomes to be reported are the recruitment rate, rate of adherence to allocated treatment, data completeness of clinical outcomes and issues identified by ambulance staff with suggestions for study optimisation. Clinical outcomes are Return of an Organised Rhythm (ROOR) at 2 minutes post-shock, refibrillation rate, Return of Spontaneous Circulation (ROSC) at hospital handover, survival and neurological outcome at 30 days.

Conclusions

The POSED study will assess the feasibility and required sample size of a large-scale trial and will explore opportunities to optimise the trial protocol.
PP41 Simulated ambulance shifts using virtual reality: a service evaluation
Authors: Johnston Sasha , Strong Gary , Knowles Steve
Published: August 2022

Background

One of the consequences of delivering the frontline response during the COVID-19 pandemic has been the reduced capacity of UK Ambulance organisations to provide practical placement opportunities for undergraduate paramedic students. In lieu of traditional ambulance shifts, Virtual Reality (VR) can be used to simulate experiences without the student going through the real event. This service evaluation explores student feedback to understand whether VR technology could bridge the gap.

Methods

Framed by the Plan, Do, Study, Act [PDSA] improvement cycle a series of VR simulated scenarios, related to mapped learning outcomes, supplemented practical ambulance placements for undergraduate paramedic students in south west England during the COVID-19 pandemic. Following simulated sessions, participating students were asked to voluntarily complete feedback questionnaires. Using Likert-scale questions, students were asked to rate the quality of tutors and the session content. Collated comments were entered into an Excel spreadsheet and content analysis was used to code and categorise responses based upon frequency.

Results

Results are based on eighty-eight nodes systematically generated from free-text feedback comments provided by fifty-three students who undertook a simulated practice placement utilising VR during April and May 2021. Nodes were reduced to fourteen categories and then condensed into nine themes relating to three domains of learning; affective, psychomotor and cognitive. An overarching theme of ‘enthusiasm for VR learning’ was identified.

Conclusions

Overall, the utilisation of VR during simulated scenarios for undergraduate paramedic students appears to be a useful and acceptable adjunct to traditional ambulance shifts. The VR approach appears to be particularly useful for supporting the affective domain of learning with several students reporting increased self-confidence, positive interactions with course facilitators and the opportunity to put classroom knowledge into practice. We recommend that future use of VR should pay particular attention to the quality of simulations and the equipment used.
PP42 Perceptions and experiences of wellbeing provision for NHS ambulance staff: a qualitative interview study of wellbeing leads and frontline staff
Authors: Phung Viet-Hai , Pritchard Gary , Sanderson Kristy , Bell Fiona , Hird Kelly , Wankhade Paresh , Asghar Zahid , Siriwardena Niro
Published: August 2022

Background

The wellbeing of ambulance staff is critical to their safety and safe care delivery. This study examined the perceptions of English NHS ambulance Trust health and wellbeing leads, and the experiences of UK ambulance staff of workplace wellbeing culture and provision.

Methods

Semi-structured telephone interviews were undertaken with staff wellbeing leads for eight NHS ambulance trusts in England and with ambulance staff from three NHS ambulance trusts in England, selected to represent services with high, medium or low relative sickness absence rates. Interviews were subsequently transcribed, coded and analysed using Framework Analysis (FA).

Results

We interviewed eight wellbeing leads and 25 frontline ambulance staff from April-November 2020. Decisions around what was included in or omitted from wellbeing policies sometimes led to conflict between wellbeing leads and their superiors. Ambulance work was perceived as inherently unhealthy because of work stress and the risk of encountering traumatic incidents. Well-being leads understood the adverse impacts of work on mental health for some staff. Ambulance staff wanted empathy, understanding and practical support from managers, but the reality did not always match these needs, because of variability in provision and experiences of health and wellbeing services, poor behaviours or attitudes from line managers, and a stigmatising rather than open organisational culture. COVID-19 not only impacted significantly on staff health and wellbeing, but also challenged how ambulance trusts support them.

Conclusions

The importance of an open organisational culture and the variable availability and experiences of interventions to support staff to stay well at work means that improvements are needed in both to ensure positive staff mental health and wellbeing. Early interventions, improved training for line managers to support staff at work, bespoke wellbeing services and an open culture are key to delivering effective support to ambulance staff, especially in the light of the COVID-19 pandemic.
PP46 Paramedics in primary care: implementation matters
Authors: Kingston Mark , Porter Alison
Published: August 2022

Background

Pressures of patient demand and GP shortages have prompted a major workforce shift in primary care. Increasingly, paramedics are joining the primary care team but with wide variation in how they are employed, managed and supported. With numbers of paramedics in primary care rapidly increasing, evidence is needed to understand how PPC can be successfully implemented.

Methods

We evaluated two collaborative health board/ambulance service initiatives. Each featured advanced paramedics (APs) – paramedics with additional education - rotating between their usual role and the new primary care role. In site A, the service of 9 APs spanned 5 general practice clusters. Site B featured 1 general practice with 8 APs. We completed 28 qualitative interviews with APs, managers, practice staff, and patients to understand implementation at both sites. We used the iPARIHS implementation framework (Harvey and Kitson 2015) as a conceptual lens to analyse transcripts, applying its interrelated domains of context, innovation, recipients and facilitation.

Results

Site A was introduced as part of an innovation project, with external funding, a focus on training and personal development, and a structured lead in time. In site B paramedics were rapidly introduced to address an acute GP shortage, without formal training and support. The innovations were similar – home-visits and in-practice clinics undertaken by the practitioners. Interviewees (recipients) at both sites reported that the services helped to increase capacity, and were well received by patients and most staff. At site A: facilitation (by dedicated project workers) and training helped to shape the culture and preparedness. Site B APs and practice staff reported less readiness, impacting on morale, interprofessional working, and sustainability.

Conclusions

Paramedics increasingly feature in primary care, but there is great variation in service models and implementation. Our comparative approach, informed by iPARIHS, illustrates the importance of understanding implementation - alongside or in addition to effectiveness.
05 The RESearch PARamedic Experience (RESPARE) study
Authors: MClelland Graham , Charlton Karl , Limmer Matt
Published: August 2022

Background

The research paramedic is a relatively niche role undertaken by a small number of paramedics who support, deliver and promote research. Research paramedic positions are often temporary due to the project-based nature of studies. The routes into research, and how to establish a research career, are not as clear as routes into other areas of practice and very little has been published about the research paramedic role. The aim of this study was to explore the experience of people who work, or have worked, as research paramedics.

Methods

A generic qualitative approach underpinned by phenomenological concepts was used. Participants were recruited via ambulance research leads and social media. Online focus groups allowed participants to discuss their roles with peers who may be geographically distant. Semi-structured interviews will expand on the focus group findings. Data were analysed using framework analysis.

Results

Thirteen paramedics representing nine NHS trusts participated in three focus groups in November 2021.

Initial themes include: common routes into the role; delivering versus developing research; barriers and facilitators to recruitment; networking and support; and clinical credibility.

Five interviews are planned for December 2021 to expand on these findings.

Conclusions

Further analysis is needed but initial findings indicate that many research paramedics have had similar experiences in terms of starting by delivering research for large studies then building on this experience and the networks they create to develop their own research. There are recognised organisational and financial barriers to working as a research paramedic. Progression beyond the research paramedic role is not very well defined but often involves building links outside of the ambulance services.
OP09 The COVID-19 ambulance response assessment (CARA): measuring the psychological stress and preparedness of ambulance staff to deliver care during the first wave of the SARS-COV–2 pandemic in the UK in 2020
Authors: Barrett Jack , Williams Julia , Bennett-Eastley Kate , Owen Paul , Naeem Salman , Herbland Anthony , King James , Mortimer Craig
Published: August 2022

Background

The coronavirus infectious disease 2019 (COVID-19) places significant demand on the NHS. This study aimed to measure the preparedness of healthcare professionals working for ambulance services, in both direct-contact patient facing and remote roles, and the impact the COVID-19 pandemic had on their psychological stress and perceived ability to deliver care.

Methods

Clinicians working for NHS ambulance services were invited to participate in a three-phase sequential online survey during the acceleration phase of the first wave of COVID-19 in the UK. Participants who participated in the first survey and who consented to be re-contacted were asked to complete two further surveys during the peak and deceleration phases of the pandemic. Open and closed-ended questions were used to assess participants’ perceived preparedness in undertaking their clinical duties. In addition, the General Health Questionnaire-12 (GHQ-12) was utilised to measure participants’ anxiety, mood and general health through each phase of the study. A score greater than 12 indicates the participant may be experiencing psychological distress.

Results

Phase one recruited 3717 participants, reducing to 2709 (73%) by phase two and 2159 (58%) by phase three. Overall, mean GHQ-12 scores were 16.5 (± 5.2) during phase one, reducing to 15.2(±6.7) by phase three. Most participants were found to have a GHQ-12 score great than 12 during the first phase (n = 3112, 84%). Factors leading to higher mean GHQ-12 scores were feeling unprepared for the pandemic, a lack of confidence in both using personal protective equipment and managing out of hospital cardiac arrests in patients with confirmed or suspected COVID-19.

Conclusions

A significant number of participants reported psychological distress, the reasons for which are multifactorial. Ambulance managers need to be aware of the risks to staff mental health and take action to mitigate these, to support staff in delivery of unscheduled, emergency and urgent care under these additional pressures.
Fluorescence-based measurement of the Lewis acidities of lanthanide triflates in solution
Authors: Demay-Drouhard Paul , Gaffen Joshua R. , Caputo Christopher B. , Baumgartner Thomas
Published: August 2022

Despite the prominence of rare-earth complexes to act as Lewis acid catalysts in organic synthesis, the comprehensive measure of the Lewis acid strength of such compounds has yet to be performed due to incompatibilities with existing methods. We report our results in measurement of a sequence of lanthanide triflates via our recently established fluorescent Lewis adduct (FLA) method. The persistence in solution of these Lewis acids as solvated coordination complexes is accurately measurable by the FLA method. However, several of the rare-earth species exhibit fluorescence quenching, which may potentially inhibit the measurement. Nevertheless, meaningful FLA measurements were still possible, and the results correspond to both periodic trends and were even consistent with previous correlated reported data.
The Linear Template Fit
Authors: Britzger Daniel
Published: August 2022

The estimation of parameters from data is a common problem in many areas of the physical sciences, and frequently used algorithms rely on sets of simulated data which are fit to data. In this article, an analytic solution for simulation-based parameter estimation problems is presented. The matrix formalism, termed theLinear Template Fit, calculates the best estimators for the parameters of interest. It combines a linear regression with the method of least squares. The algorithm uses only predictions calculated for a few values of the parameters of interest, which have been made available prior to its execution. The Linear Template Fit is particularly suited for performance-critical applications and parameter estimation problems with computationally intense simulations, which are otherwise often limited in their usability for statistical inference. Equations for error propagation are discussed in detail and are given in closed analytic form. For the solution of problems with a nonlinear dependence on the parameters of interest, theQuadratic Template Fitis introduced. As an example application, a determination of the strong coupling constant from inclusive jet cross section data at the CERN Large Hadron Collider is studied and compared with previously published results.
A novel reliable path planning approach for multimodal networks based on a two-factor bound convergence algorithm
Authors: Yu Wentao , Sun Huijun , Feng Tao , Lv Ying , Guo Xin , Xin Guangyu
Published: August 2022

Due to the influence of diverse factors, travel time is highly uncertain. Travelers are eager to find the most reliable path in multimodal networks to reduce the penalty caused by late arrival. However, the research considering the traveler preferences in multimodal transportation networks to solve the reliable path problem with given budgets is limited. Thus, we propose two multimodal reliable path models to find personalized and reliable paths. First, we build a multimodal network based on smart card data to incorporate the multimodal transfers between public and private transportation and solve corresponding parking issues effectively. Next, we build a multimodal time-reliable path model to find time-reliable paths. Further, considering traveler preferences, we design a multimodal utility-reliable path model to find personalized and reliable paths. A novel two-factor reliability bound convergence algorithm is developed to solve the proposed models and proved for its theoretical feasibility. Finally, a real-world case study is used to verify the effectiveness and efficiency of the proposed models and algorithm.
Convolutional Neural Networks (CNN) with Quantum-Behaved Particle Swarm Optimization (QPSO)-Based Medical Image Fusion
Authors: Mergin A. Ancy , Premi M. S. Godwin
Published: August 2022

Medical imaging fusion is the process of combining pictures from various imaging modalities to create a single image that may be used in clinical settings. Robust methods for merging image data from several modalities are being developed in the field of multimodal medical imaging. Deep learning (DL) has been widely researched in two areas: pattern recognition and image processing. We will demonstrate a multimodal image fusion with DL implementation that considers the characteristics of medical diagnostic imaging as well as the demands of clinical practice. For the past three years, pixel-level picture fusion has been a hot topic. This paper proposes a new multimodal medical picture fusion technique for a wide range of medical diagnostic challenges. Image fusion is crucial in biomedical research and clinical diagnostics for biomedical image processing and therapy planning. The most convincing argument for fusion is obtaining a significant amount of critical information from the input photographs. We show how a well-organized multimodal medical image fusion technique can be utilized to integrate computed tomography (CT) and magnetic resonance imaging (MRI) data in this study. Using convolutional neural networks (CNNs), the quantum-behaved particle swarm optimization (QPSO) algorithm was used to create a method for integrating multimodal medical pictures. In order to improve the overall quality and efficiency of QPSO, it was chosen to add the metrics of image entropy, standard deviation, average gradient (AG), spatial frequency (SF), and visual information fidelity (VIF). In experiments, multimodal medical images are utilized to evaluate a variety of parameters, including performance and algorithm stability. When compared to the other possibilities, the recommended technique outperformed them in the evaluations. On a range of quantitative metrics, this method outperforms the alternatives.
Some Physical Properties of Pure and Cu, Fe-doped CdS Thin Films
Authors: Hadi Hawraa , Mohammed Kahtan A. , Hadi Dhufr
Published: August 2022

In the present work, Cadmium sulfide (CdS) and CdS doped with copper (Cu) and iron (Fe) metals thin films were deposited on glass slides using chemical bath deposition technique. 1% content of Cu and Fe were used as dopants. The films were prepared at a reaction temperature of approximately 80∘C and for an hour as the reaction time without any heat treatment. The pure and doped films were diagnosed and examined by several techniques such as X-ray diffraction (XRD), field emission scanning electron microscope (FESEM) and UV-visible spectrometry to study the effect of the doping and the type of dopant material on the surface, structural and optical properties of CdS films. From the results of XRD can be noticed there is no effect for the dopant on the type of crystal structure except small shifting in the position of main peak after doping. The bandgap energy of the Fe-doped CdS films has been found to be lower than that of the undoped films between 2.62 eV and 2.43 eV.
Middle Risle River Critical Zone Observatory: monitoring karst-process evolution and its impact on surface water and on aquatic ecosystems
Authors: David Pierre-Yann , Pennequin Didier , Charlier Jean-Baptiste , Ladouche Bernard , Meire Baptiste , Feeny-Fereol Véronique , Helouin Stephane , Fournier Matthieu , Riviere Camille , Sanson Germain
Published: August 2022

Rivers in karstic environments are known to be greatly influenced by surface water–groundwater interactions, with significant localized inflows during floods from springs, or with losses that can dry up rivers. The Middle Risle River is frequently affected by the development of sinkholes in a chalk karst area (Normandy, France). In the 2010s, two new major sinkholes in the riverbed caused a complete loss of water into the underlying phreatic aquifer, causing the river to dry up over several kilometres. The resulting changes in hydrogeological processes and surface water–groundwater interaction greatly affected water quality, water use and water-dependent ecosystems, causing a political crisis in this river-dependent touristic valley. To understand these phenomena and improve crisis management, the Middle Risle Critical Zone Observatory was set up to enhance monitoring, surveying and/or modelling of groundwater and river levels, river and spring flow, water temperature and conductivity, and ecosystem characteristics (fish, macro-invertebrates and vegetation). The results showed notable impacts on fish, macro-invertebrates and vegetation, some plants proving to be reliable indicators of surface-water–groundwater interaction. The dynamics of local hydrogeological processes were assessed and linked to the measured effects on ecosystems and water supply. Inverse modelling based on an analytical solution of the diffusive wave equation assessed lateral flow during floods, quantifying the spatial–temporal variability of surface-water and groundwater exchanges. It also highlighted the important role of karst zones in both storage and flood-peak attenuation processes, thereby protecting downstream villages against floods.
Deep Self-Supervised Hyperspectral Image Reconstruction
Authors: Liu Zhe , Han Xian-Hua
Published: August 2022

Reconstructing a high-resolution hyperspectral (HR-HS) image via merging a low-resolution hyperspectral (LR-HS) image and a high-resolution RGB (HR-RGB) image has become a hot research topic, and can greatly benefit for different subsequent high-level vision tasks. Recently, deep learning–based approaches have evolved for HS image reconstruction and validated impressive performance. However, to learn a good reconstruction model in the deep learning–based methods, it is mandatory to previously collect large-scale training triplets consisting of the LR-HS, HR-RGB, and HR-HS images, which is difficult to be collected in real applications. This study proposes a deep self-supervised HS image reconstruction framework (DSSH), which does not have to depend on any handcrafted prior and previously collected training triplets at all. The proposed DSSH method leverages the designed network architecture itself for capturing the prior of the underlying structure in the latent HR-HS image and employs the observed LR-HS and HR-RGB images only for network parameter learning. Experiments on two benchmark HS image datasets validated that the proposed DSSH method manifests very impressive reconstruction performance, and is even better than some state-of-the-art supervised learning approaches.
Rethinking the Interactivity of OS and Device Layers in Memory Management
Authors: Wang Tse-Yuan , Wu Chun-Feng , Tsao Che-Wei , Chang Yuan-Hao , Kuo Tei-Wei , Liu Xue
Published: August 2022

In the big data era, a huge number of services has placed a fast-growing demand on the capacity of DRAM-based main memory. However, due to the high hardware cost and serious leakage power/energy consumption, the growth rate of DRAM capacity cannot meet the increased rate of the required main memory space when the energy or hardware cost is a critical concern. To tackle this issue, hybrid main-memory devices/modules have been proposed to replace the pure DRAM main memory with a hybrid main memory module that provides a large main memory space by integrating a small-sized DRAM and a large-sized
non-volatile memory (NVM)
into the same memory module. Although NVMs have high-density and low-cost features, they suffer from the low read/write performance and low endurance issue, compared to DRAM. Thus, inside the hybrid main-memory module, it also includes a memory management design to use DRAM as the cache of NVMs to enhance its performance and lifetime. However, it also introduces new design challenges in both the OS and the memory module. In this work, we rethink the interactivity of OS and hybrid main-memory module, and propose a cross-layer cache design that (1) utilizes the information from the operating system to optimize the hit ratio of the DRAM cache inside the memory module, and (2) takes advantage of the bulk-size (or block-based) read/write feature of NVM to minimize the time overhead on the data movement between DRAM and NVM. At the same time, this cross-layer cache design is very lightweight and only introduces limited runtime management overheads. A series of experiments was conducted to evaluate the effectiveness of the proposed cross-layer cache design. The results show that the proposed design could improve access performance for up to 88%, compared to the investigated well-known page replacement algorithms.
PISCOT: A Pipelined Split-Transaction COTS-Coherent Bus for Multi-Core Real-Time Systems
Authors: Hessien Salah , Hassan Mohamed
Published: August 2022

Tasks in modern embedded systems such as automotive and avionics communicate among each other using shared data towards achieving the desired functionality of the whole system. In commodity platforms, cores communicate data through the shared memory hierarchy and correctness is maintained by a cache coherence protocol. Recent works investigated the deployment of coherence protocols in real-time systems and showed significant performance improvements. Nonetheless, we find these works to require modifications to commodity coherence protocols, assume simple in-order pipelines, and most importantly suffer from significant latency delays due to coherence interference along with average performance degradation. In this work, we propose
PISCOT
: a predictable and coherent bus architecture that (i) provides a considerably tighter bound compared to the state-of-the-art predictable coherent solutions (4× tighter bounds in a quad-core system). (ii) It does so with a negligible performance loss compared to conventional high-performance architecture coherence delays (less than 4% for SPLASH-3 benchmarks). This improves average performance by up to 5× (2.8× on average) compared to its predictable coherence counterpart. Finally, (iii) it achieves that without requiring any modifications to conventional coherence protocols. We show this by integrating
PISCOT
on top of two protocols with a detailed implementation with complete transient states: MSI and MESI.
Distill-DBDGAN: Knowledge Distillation and Adversarial Learning Framework for Defocus Blur Detection
Authors: Jonna Sankaraganesh , Medhi Moushumi , Sahay Rajiv Ranjan
Published: August 2022

Defocus blur detection (DBD) aims to segment the blurred regions from a given image affected by defocus blur. It is a crucial pre-processing step for various computer vision tasks. With the increasing popularity of small mobile devices, there is a need for a computationally efficient method to detect defocus blur accurately. We propose an efficient defocus blur detection method that estimates the probability of each pixel being focused or blurred in resource-constraint devices. Despite remarkable advances made by the recent deep learning-based methods, they still suffer from several challenges such as background clutter, scale sensitivity, indistinguishable low-contrast focused regions from out-of-focus blur, and especially high computational cost and memory requirement. To address the first three challenges, we develop a novel deep network that efficiently detects blur map from the input blurred image. Specifically, we integrate multi-scale features in the deep network to resolve the scale ambiguities and simultaneously modeled the non-local structural correlations in the high-level blur features. To handle the last two issues, we eventually frame our DBD algorithm to perform knowledge distillation by transferring information from the larger teacher network to a compact student network. All the networks are adversarially trained in an end-to-end manner to enforce higher order consistencies between the output and the target distributions. Experimental results demonstrate the state-of-the-art performance of the larger teacher network, while our proposed lightweight DBD model imitates the output of the teacher network without significant loss in accuracy. The codes, pre-trained model weights, and the results will be made publicly available.
BullsEye
: Scalable and Accurate Approximation Framework for Cache Miss Calculation
Authors: Shah Nilesh Rajendra , Misra Ashitabh , Miné Antoine , Venkat Rakesh , Upadrasta Ramakrishna
Published: August 2022

For Affine Control Programs or Static Control Programs (SCoP), symbolic counting of reuse distances could induce polynomials for each reuse pair. These polynomials along with cache capacity constraints lead to non-affine (semi-algebraic) sets; and counting these sets is considered to be a hard problem. The state-of-the-art methods use various exact enumeration techniques relying on existing cardinality algorithms that can efficiently count affine sets.

We propose
BullsEye
, a novel, scalable, accurate, and problem-size independent approximation framework. It is an analytical cache model for fully associative caches with LRU replacement policy focusing on sampling and linearization of non-affine stack distance polynomials. First, we propose a simple domain sampling method that can improve the scalability of exact enumeration. Second, we propose linearization techniques relying on
Handelman’s theorem
and
Bernstein’s representation
. To improve the scalability of the
Handelman’s theorem
linearization technique, we propose template (Interval or Octagon) sub-polyhedral approximations.

Our methods obtain significant compile-time improvements with high-accuracy when compared to
HayStack
on important polyhedral compilation kernels such as
nussinov
,
cholesky
, and
adi
from
PolyBench
, and
harris
,
gaussianblur
from
LLVM
-TestSuite. Overall, on
PolyBench
kernels, our methods show up to 3.31× (geomean) speedup with errors below ≈ 0.08% (geomean) for the octagon sub-polyhedral approximation.
A Symbolic Approach to Detecting Hardware Trojans Triggered by Don’t Care Transitions
Authors: Dai Ruochen , Yavuz Tuba
Published: August 2022

Due to the globalization of Integrated Circuit supply chain, hardware Trojans and the attacks that can trigger them have become an important security issue. One type of hardware Trojans leverages the “don’t care transitions” in Finite-state Machines (FSMs) of hardware designs. In this article, we present a symbolic approach to detecting don’t care transitions and the hidden Trojans. Our detection approach works at both register-transfer level (RTL) and gate level, does not require a golden design, and works in three stages. In the first stage, it explores the reachable states. In the second stage, it performs an approximate analysis to find the don’t care transitions and any discrepancies in the register values or output lines due to don’t care transitions. The second stage can be used for both predicting don’t care triggered Trojans and for guiding don’t care aware reachability analysis. In the third stage, it performs a state-space exploration from reachable states that have incoming don’t care transitions to explore the Trojan payload and to find behavioral discrepancies with respect to what has been observed in the first stage. We also present a pruning technique based on the reachability of FSM states. We present a methodology that leverages both RTL and gate-level for soundness and efficiency. Specifically, we show that don’t care transitions and Trojans that leverage them must be detected at the gate-level, i.e., after synthesis has been performed, for soundness. However, under specific conditions, Trojan payload exploration can be performed more efficiently at RTL. Additionally, the modular design of our approach also provides a fast Trojan prediction method even at the gate level when the reachable states of the FSM is known
a priori
. Evaluation of our approach on a set of benchmarks from OpenCores and TrustHub and using gate-level representation generated by two synthesis tools, YOSYS and Synopsis Design Compiler (SDC), shows that our approach is both efficient (up to 10× speedup w.r.t. no pruning) and precise (0% false positives both at RTL and gate-level netlist) in detecting don’t care transitions and the Trojans that leverage them. Additionally, the total analysis time can achieve up to 1.62× (using YOSYS) and 1.92× (using SDC) speedup when synthesis preserves the FSM structure, the foundry is trusted, and the Trojan detection is performed at RTL.
Effect of Li+ Addition during Initial Stage of Electrodeposition Process on Nucleation and Growth of Zn
Authors: Onabuta Yusuke , Kunimoto Masahiro , Wang Songyi , Fukunaka Yasuhiro , Nakai Hiromi , Homma Takayuki
Published: August 2022

Zn negative electrodes are expected to be used in next-generation batteries. However, irregular shape evolution, such as mossy structures, limits its practical applications. Cationic additive species are useful in suppressing this, and Li+ is a promising species. To identify the effect of Li+ on the nucleation and growth of Zn, this study analyzed the Zn aggregation behavior during electrodeposition with Li+ at the initial stage via experimental methods and theoretical calculations using density functional theory and kinetic Monte Carlo simulations. The results suggest that Li+ affected the surface diffusion of Zn adatoms, changing the nucleation and growth during the initial stage of deposition. Li+ allows Zn adatoms to diffuse rapidly owing to the mitigation of the solvation effect on surface diffusion by forming rigid solvation of Li+ in the vicinity of the surface. This results in two-dimensional nucleation of the Zn(0001) facet, which is supported by the X-ray diffraction measurements. Li+ mitigates protrusion as the initial structure of the mossy structure. This analysis provides valuable insight into the control of the behavior of Zn adatoms and their nucleation and growth.
Autophagic response to exercise in peripheral blood mononuclear cells from young men is intensity-dependent and is altered by exposure to environmental heat
Authors: McCormick James J. , Côté Melissa D. , King Kelli E. , McManus Morgan K. , Goulet Nicholas , Dokladny Karol , Moseley Pope L. , Kenny Glen P.
Published: August 2022

Autophagy is essential to maintaining cellular homeostasis in all eukaryotic cells and to tolerance of acute stressors such as starvation, heat, and recovery after exercise. Limited information exists regarding the exercise intensity-dependent autophagic response in humans, and it is unknown how environmental heat stress may modulate this response. Therefore, we evaluated autophagy and accompanying pathways of cellular stress [the heat-shock response (HSR), apoptosis, and acute inflammation] in peripheral blood mononuclear cells (PBMCs) from 10 young men (mean [SD]; 22 [2] years) before, immediately after and up to 6-h postexercise recovery from 30 min of low-, moderate-, and high-intensity semirecumbent cycling [40%, 55%, and 70% of maximal oxygen consumption (V̇o2max), respectively] in a temperate environment (25°C) and at 70% of V̇o2max in a hot environment (40°C). Changes in protein content were analyzed via Western blot. Each increase in exercise intensity was associated with elevations in mean body temperature. LC3-II increased after moderate-intensity exercise, with further increases after high-intensity exercise ( P < 0.05). However, an increase in beclin-2 and ULK1, with a decrease in p62 was only observed after high-intensity exercise, which was paralleled by elevated TNF-α and cleaved-caspase-3, with the HSR peaking at 6 h after exercise ( P < 0.05). When exercise was performed in the heat, greater LC3-II and cleaved-caspase-3 accumulation were observed; however, beclin-2 declined in recovery ( P < 0.05). Therefore, our findings indicate that autophagy in PBMCs during exercise may be associated with greater heat strain exhibited during increasing exercise intensities, which is modulated by exposure to heat.
Bayesian Network-Based Marketing Risk Warning Method for Integrated Energy Companies
Authors: Guang Junhong , Ruan Zhouquan , Dong Jiadu
Published: August 2022

People’s daily life heavily depends on energy. The core business of electric power enterprises is commercial energy sales. In the daily work of power enterprises, energy marketing is the most important field which will directly affect the development of enterprises. Although national macro-control exists as a useful way to control the energy market, it also increases the risk of the energy market. Nowadays, the development of the energy market is affected with the adjustment of industrial structure. In the energy market, China’s power companies are state-owned enterprises and have their unique advantages for developing, but there are still some problems in risk awareness, which require us to take measures to control and prevent the impact risk.
[Retracted] Deep Convolutional Neural Network Mechanism Assessment of COVID‐19 Severity
Authors: Nirmaladevi J. , Vidhyalakshmi M. , Edwin E. Bijolin , Venkateswaran N. , Avasthi Vinay , Alarfaj Abdullah A. , Hirad Abdurahman Hajinur , Rajendran R. K. , Hailu TegegneAyalew
Published: August 2022

As an epidemic, COVID‐19’s core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID‐19) infection, an overwhelming comparison with patients queues up for pulmonary X‐rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID‐19 illness assessment seriousness. Utilizing chest X‐ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID‐19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X‐ray scans. To the evidence relating, it is the first COVID‐19 disease incidence evaluation research with four different phases, to use a reasonably high number of X‐ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.
Implementation of Creator-Based Virtual Simulation in News Interview
Authors: Zhou Wen
Published: August 2022

With the development of network information technology, the application of virtual simulation technology in medicine, electromechanical, military, and other fields has changed the traditional field operation mode and achieved good results, but the application of virtual simulation technology in the field of news and communication is very little. This paper takes the construction of virtual simulation news system as an example. Based on the support of 3D modeling technology of Multigen Creator, the functional requirements of virtual simulation news interview system are analyzed, the overall system objectives and architecture are set up, virtual scene database, 3D entity modeling, model optimization, and real-time rendering are established. A virtual simulation system for emergency news interviews is designed and developed, including real-time simulation environment and online operational feedback. This paper discusses the application effect of the system. By means of the questionnaire survey, SPSS23.0 is used to further analyze the feedback data of users. The data show that the expected functions of 3D modeling and real-time simulation of system modules have been realized. The research shows that virtual simulation technology can be successfully applied in the field of news and communication, and can successfully penetrate into the practical dimension of news and communication, change the existing single information interaction mode, and create a new era of real-time simulation.
Exploring the Practice of Teaching English Professional Translation Using IoT and Cloud Computing Multimedia Assistance
Authors: Hu Wei
Published: August 2022

With the development of physical networks and large-scale data processing technologies, schools and classrooms in major universities are equipped with multimedia facilities and dedicated multimedia classrooms. It requires students to have a complete theoretical knowledge and also to apply it in practice. Multimedia-assisted teaching meets the requirements of education modernization, but teachers must pay attention to changing the traditional teaching mode and students’ reliance on online teaching software, as well as students’ actual teaching ability, enriching teaching contents and conditions and improving learning assessment. This paper compares and applies the English teaching syllabus based on intranet and multimedia proposed in this paper to students of English translation in a university. It is concluded that the application of the curriculum has greatly improved students’ learning performance with an average score of 10.46, which shows the importance of the foundation for improving the efficiency of English teaching.
Hyperhomocysteinemia Promotes Cardiac Hypertrophy in Hypertension
Authors: Deng Yawen , Li Zhitong , An Xiangbo , Fan Rui , Wang Yao , Li Jiatian , Yang Xiaolei , Liao Jiawei , Xia Yunlong
Published: August 2022

Hyperhomocysteinemia (HHcy) is positively linked with several cardiovascular diseases; however, its role and underlying mechanisms in pathological cardiac hypertrophy are still unclear. Here, we focused on the effects and underlying mechanisms of HHcy in hypertensive cardiac hypertrophy, one of the most common and typical types of pathological cardiac hypertrophy. By a retrospective analysis of the association between HHcy and cardiac hypertrophy in a hypertensive cohort, we found that the prevalence of HHcy was higher in patients with hypertrophy and significantly associated with the presence of cardiac hypertrophy after adjusting for other conventional risk factors. In mice, HHcy induced by a methionine (2% wt/wt) diet feeding significantly promoted cardiac hypertrophy as well as cardiac inflammation and fibrosis induced by 3-week angiotensin ІІ (AngІІ) infusion (1000 ng/kg/min), while folic acid (0.006% wt/wt) supplement corrected HHcy and attenuated AngII-stimulated cardiac phenotypes. Mechanistic studies further showed that homocysteine (Hcy) exacerbated AngII-stimulated expression of Calcineurin and nuclear factor of activated T cells (NFAT), which could be attenuated by folic acid both in mice and in neonatal rat cardiomyocytes. Moreover, treatment with cyclosporin A, an inhibitor of Calcineurin, blocked Hcy-stimulated Calcineurin-NFAT signaling and hypertrophy in neonatal rat cardiomyocytes. In conclusion, our study indicates that HHcy promotes cardiac hypertrophy in hypertension, and Calcineurin-NFAT pathway might be involved in the pro-hypertrophic effect of Hcy.