bluuebunny/crossref_metadata_2025 huggingface dataset:
COVID-19 surge readiness: use cases demonstrating how hospitals leveraged digital identity access management for infection control and pandemic response
Authors: Gellert George A , Kelly Sean P , Hsiao Allen L , Herrick Brian , Weis Donna , Lutz Jeffrey , Stanton Glynn , Bonilla Santos , Borgasano Daniel , Erich Matthew , Reilly Claire , Johnston Daniel
Published: February 2023

Background

Surging volumes of patients with COVID-19 and the high infectiousness of SARS-CoV-2 challenged hospital infection control/safety, staffing, care delivery and operations as few crises have. Imperatives to ensure security of patient information, defend against cybersecurity threats and accurately identify/authenticate patients and staff were undiminished, which fostered creative use cases where hospitals leveraged identity access and management (IAM) technologies to improve infection control and minimise disruption of clinical and administrative workflows.

Methods

Working with a leading IAM solution provider, implementation personnel in the USA and UK identified all hospitals/health systems where an innovative use of IAM technology improved facility infection control and pandemic response management. Interviews/communications with hospital clinical informatics leaders collected information describing the use case deployed.

Results

Eight innovative/valuable hospital use cases are described: symptom-free attestation by clinicians at shift start; detection of clinician exposure/contact tracing; reporting of clinician temperature checks; inpatient telehealth consults in isolation units; virtual visits between isolated patients and families; touchless single sign-on authentication; secure access enabled for rapid expansion of personnel working remotely; and monitoring of temporary worker attendance.

Discussion

No systematic, comprehensive survey of all implemented IAM client sites was conducted, and other use cases may be undetected. A standardised reporting/information sharing vehicle is needed whereby IAM use cases aiding facility pandemic response and infection control can be disseminated.

Conclusions

Clinical care, infection control and facility operations were improved using IAM solutions during COVID-19. Facility end-user innovation in how IAM solutions are deployed can improve infection control/patient safety, care delivery and clinical workflows during surges of epidemic infectious diseases.
Opti’care protocol: a randomised control trial to evaluate the impact of a mobile antenatal care clinic in isolated rural areas on prenatal follow-up
Authors: Debost-Legrand Anne , Legrand Guillaume , Duclos-Médard Julie , Thomazet Juliette , Pranal Marine , Langlois Eric , Mourgues Charline , Vendittelli Françoise
Published: February 2023

Introduction

Rural residence appears to be a factor of vulnerability among pregnant women with poor clinical antenatal care. Our principal objective is to assess the impact of an infrastructure for a mobile antenatal care clinic on the completion of antenatal care for women identified as geographically vulnerable in a perinatal network.

Methods and analysis

Controlled cluster-randomised study in two parallel arms comparing an intervention group with an open-label control group. This study will concern the population of pregnant women who must live in one of the municipalities covered by the perinatal network and considered to be an area of geographic vulnerability. The cluster randomisation will take place according to the municipality of residence. The intervention will be the implementation of pregnancy monitoring by a mobile antenatal care clinic. The completion of antenatal care between the intervention and control groups will be a binary criterion: 1 will be attributed to each antenatal care that includes all visits and supplementary examinations. Sample size has been estimated to be 330 at least with an 80% participation rate.

The univariate analyses will compare the follow-up rates (with Fisher’s exact test), and all individual characteristics collected (Fisher’s exact test, Student’s t-test) between the two groups. The multivariate analysis will use a mixed linear model analysis and consider the cluster effect as random; the initial model will include known confounders from the literature, confounders identified in univariate analyses, and the clinically relevant prognostic factors. All of these factors will be taken into account in the model as a fixed effect.

Ethics and dissemination

The Patient Protection Committee North-West II approved this study on 4 February 2021 (IRB 2020-A02247-32). The results will be the subject of scientific communications and publications.

Trial registration number

NCT04823104.
Factors associated with severity and persistence of fatigue in patients with primary biliary cholangitis: study protocol of a prospective cohort study with a mixed-methods approach (SOMA.LIV)
Authors: Toussaint Anne , Buck Laura , Hartl Johannes , Löwe Bernd , Schramm Christoph
Published: February 2023

Introduction

Fatigue is a common symptom and the major ‘unmet need’ in the management of patients with primary biliary cholangitis (PBC). To date, only few prospective studies have addressed the development of PBC-associated fatigue over time. At the same time, few biological and psychosocial risk factors and mechanisms have been identified that could explain the development and maintenance of fatigue in PBC. It is the overall aim of this study to identify factors that determine the course and severity of fatigue in PBC, and to target these factors within deliverable interventions in order to improve patients’ quality of life.

Methods and analysis

To identify biological and psychosocial risk factors for severe fatigue, a prospective 12-month cohort study with one baseline and two follow-up measurements will be conducted. In a cross-sectional part, we will simultaneously examine clinically relevant biomedical and psychosocial factors and systematically assess and compare associations and interactions between these factors and fatigue in n=240 patients with PBC (a patient group severely affected by fatigue) and n=240 patients with primary sclerosing cholangitis, a control cholestatic liver disease group much less affected by fatigue. In a prospective part, we will longitudinally monitor these variables and assess their predictive value at 12-month follow-up. Within an embedded mixed-methods design, we will conduct an experimental study and qualitative interviews in patients with newly diagnosed PBC.

Ethics and dissemination

The study was approved by the Ethics Committee of the Hamburg Medical Association (2020–10196-BO-ff). The study will shed light onto the mechanisms underlying the evolvement and maintenance of fatigue in patients with PBC and enable the development of evidence-based intervention strategies. Findings will be disseminated through peer-reviewed publications, scientific conferences and the involvement of relevant stakeholders, patients and the lay public.

Trial registration number

NCT14379650.
Protocol for a process evaluation of a stepped wedge randomised controlled trial to reduce unnecessary hospitalisations of older people from residential aged care: the EDDIE+ study
Authors: Bracci Ella , Allen Michelle , Carter Hannah E , Cyarto Liz , Dwyer Trudy , Graves Nicholas , Lee Xing Ju , Meyer Claudia , Oprescu Florin , Harvey Gillian
Published: February 2023

Introduction

The Early Detection of Deterioration in Elderly residents (EDDIE+) programme is a theory-informed, multi-component intervention aimed at upskilling and empowering nursing and personal care staff to identify and manage early signs of deterioration in residents of aged care facilities. The intervention aims to reduce unnecessary hospital admissions from residential aged care (RAC) homes. Alongside a stepped wedge randomised controlled trial, an embedded process evaluation will be conducted to assess the fidelity, acceptability, mechanisms of action and contextual barriers and enablers of the EDDIE+ intervention.

Methods and analysis

Twelve RAC homes in Queensland, Australia are participating in the study. A comprehensive mixed-methods process evaluation, informed by the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, will assess intervention fidelity, contextual barriers and enablers, mechanisms of action, and the acceptability of the programme from various stakeholder perspectives. Quantitative data will be collected prospectively from project documentation, including baseline context mapping of participating sites, activity tracking and regular check-in communication sheets. Qualitative data will be collected postintervention via semi-structured interviews with a range of stakeholder groups. The i-PARIHS constructs of innovation, recipients, context and facilitation will be applied to frame the analysis of quantitative and qualitative data.

Ethics and dissemination

Ethical approval for this study has been granted by the Bolton Clarke Human Research Ethics Committee (approval number: 170031) with administrative ethical approval granted by the Queensland University of Technology University Human Research Ethics Committee (2000000618). Full ethical approval includes a waiver of consent for access to residents’ demographic, clinical and health services de-identified data. A separate health services data linkage based on RAC home addresses will be sought through a Public Health Act application. Study findings will be disseminated through multiple channels, including journal publications, conference presentations and interactive webinars with a stakeholder network.

Trial registration number

Australia New Zealand Clinical Trial Registry (ACTRN12620000507987).
Efficacy and safety of transcutaneous electrical acupoints stimulation for preoperative anxiety in thoracoscopic surgery: study protocol for a randomised controlled trial
Authors: Wu Xindi , Chen Tongyu , Wang Ke , Wei Xuqiang , Feng Jijie , Zhou Jia
Published: February 2023

Introduction

Preoperative anxiety occurs at a very high rate in patients undergoing video-assisted thoracoscopic surgery (VATS). Moreover, it will result in poor mental state, more analgesic consumptions, rehabilitation delay and extra hospitalisation costs. Transcutaneous electrical acupoints stimulation (TEAS) is a convenient intervention for pain control and anxiety reduction. Nevertheless, TEAS efficacy of preoperative anxiety in VATS is unknown.

Methods and analysis

This single-centre randomised sham-controlled trial will be conducted in cardiothoracic surgery department of the Yueyang Hospital of Integrated Traditional Chinese and Western Medicine in China. A total of 92 eligible participants with pulmonary nodules (size ≥8 mm) who are arranged for VATS will be randomly assigned to a TEAS group and a sham TEAS (STEAS) group in a 1:1 ratio. Daily TEAS/STEAS intervention will be administered starting on 3 days before the VATS and continued once per day for three consecutive days. The primary outcome will be the generalised anxiety disorder scale score change between the day before surgery with the baseline. The secondary outcomes will include serum concentrations of 5-hydroxytryptamine, norepinephrine and gamma-aminobutyric acid, intraoperative anaesthetic consumption, time to postoperative chest tube removal, postoperative pain, and length of postoperative hospital stay. The adverse events will be recorded for safety evaluation. All data in this trial will be analysed by the SPSS V.21.0 statistical software package.

Ethics and dissemination

Ethics approval was obtained from the Ethics Committee of the Yueyang Hospital of Integrated Traditional Chinese and Western Medicine affiliated to Shanghai University of Traditional Chinese Medicine (approval number: 2021-023). The results of this study will be distributed through peer-reviewed journals.

Trial registration number

NCT04895852.
Linking electronic mental healthcare and benefits records in South London: design, procedure and descriptive outcomes
Authors: Stevelink Sharon A M , Phillips Ava , Broadbent Matthew , Boyd Andy , Dorrington Sarah , Jewell Amelia , Leal Ray , Bakolis Ioannis , Madan Ira , Hotopf Matthew , Fear Nicola T , Downs Johnny
Published: February 2023

Objectives

To describe the process and outcomes of a data linkage between electronic secondary mental healthcare records from the South London and Maudsley (SLaM) NHS Foundation Trust with benefits records from the Department for Work and Pensions (DWP). We also describe the mental health and benefit profile of patients who were successfully linked.

Design

A deterministic linkage of routine records from health and welfare government service providers within a secure environment.

Setting and participants

Adults aged≥18 years who were referred to or accessed treatment at SLaM services between January 2007 and June 2019, including those who were treated as part of Improving Access to Psychological Therapies (IAPT) services between January 2008 and June 2019 (n=448 404). Benefits data from the DWP from January 2005 to June 2020.

Outcome measures

The linkage rate and associated sociodemographic, diagnostic and treatment factors. Recorded primary psychiatric diagnosis based on International Classification of Diseases (ICD)-10 codes and type of benefit receipt.

Results

A linkage rate of 92.3% was achieved. Women, younger patients and those from ethnic minority groups were less likely to be successfully linked. Patients who had subsequently died, had a recorded primary psychiatric diagnosis, had also engaged with IAPT and had a higher number of historical postcodes available were more likely to be linked. Overall, 83% of patients received benefits at some point between 2005 and 2020. Benefit receipt across the psychiatric diagnosis spectrum was high, over 80% across most ICD-10 codes.

Conclusions

This data linkage is the first of its kind in the UK demonstrating the use of routinely collected mental health and benefits data. Benefit receipt was high among patients accessing SLaM services and varied by psychiatric diagnosis. Future areas of research are discussed, including exploring the effectiveness of interventions for helping people into work and the impact of benefit reforms.
Does systematic analysis of patient complaints and compensation cases at hospitals provide useful information to guide quality improvement? Experience from Denmark
Authors: Morsø Lars , Birkeland Søren Fryd , Walløe Sisse , Grøn Peter Sigerseth , Rexbye Helle , Bogh Søren Bie Bie
Published: February 2023

Background

Patient complaints and compensation cases are analysed individually and do not allow for organisational learning. Systematic information on complaint patterns needs evidence-based measures. The Healthcare Complaints Analysis Tool (HCAT) can systematically code and analyse complaints and compensation claims, but whether this information is useful for quality improvement is underexplored. We aim to explore if and how HCAT information is perceived useful to inform healthcare quality gaps.

Methods

To explore the HCAT’s usefulness for quality improvement purposes, we used an iterative process. We accessed all complaints relating to a large university hospital. Trained HCAT raters systematically coded all cases, using the Danish version of HCAT.

Intervention

The intervention had four phases: (1) coding of cases, (2) education, (3) selection of HCAT analyses for dissemination, (4) ‘dashboard’ development and delivery of targeted HCAT reports. To study the interventions and phases, we used quantitative and qualitative approaches. The coding patterns were descriptively displayed on department and hospital level. The educational programme was monitored using passing rates, coding reliability checks and rater feedback. Online interviews recorded dissemination feedback. We used a phenomenological approach with thematised quotations from the interviews to analyse the usefulness of the information from cases coded.

Results

We coded 5217 complaint cases (11 056 complaint points). The average case coding time was 8.5 min (95% CI 8.2 to 8.7). All four raters passed the online test with >80% correct answers. Using rater feedback, we handled 25 cases of doubt. None affected the HCAT structure or categories. Interviews verified the usefulness of analyses after expert group dissemination. Three themes were important: ‘overview of complaints’, ‘learning from complaints’ and ‘listening to the patients’. Stakeholders perceived the ‘dashboard’ development as highly relevant.

Conclusion

Through the development process with several adjustments, stakeholders found the systematic approach useful for quality improvement. The hospital management evaluated the approach as promising and decided to test the approach in clinical practice.
Prediction of comprehensive mechanical properties of thermoplastic vulcanizate (TPV) with various composition ratios based on a micromechanical method
Authors: Liu Congchao , Ma Lifeng , He Chengyuan , Xu Xinhu
Published: February 2023

In the field of thermoplastic vulcanizate (TPV), experimental methods cannot quantify the relationship between the internal structure and performance of TPV, and are not conducive to the accurate design of TPV structure and performance, which is one of the problems to be solved in this field. In this study, a simple and effective two-dimensional micromechanical model was established based on the real microstructure of TPV by using the micromechanical method and the mechanical properties of TPV with different ethylene propylene diene monomer (EPDM) mass fractions were studied. The results show that with the increase of EPDM content, the maximum stress distribution area of TPV would change, the elastic modulus of TPV would gradually decrease, while the maximum stress of polypropylene (PP) phase would first decrease and then increase and strain corresponding to elastic–plastic change would also increase. The resilience of TPV increases with the increase of EPDM content and decreases with the increase of strain load. When the EPDM content is higher than 70%, the “S” bending deformation would occur at the thinnest part of PP matrix ligament.
Automatic Multi-Class Brain Tumor Classification Using Residual Network-152 Based Deep Convolutional Neural Network
Authors: Potadar Mahesh Pandurang , Holambe Raghunath Sambhaji
Published: February 2023

Brain tumor is one of the leading causes of death in humans worldwide. Image recognition or computer vision uses deep learning based approaches for automatic tumor detection by classifying brain images. It is difficult to analyze the similarity between brain tissues while processing the magnetic resonance imaging (MRI) brain images for tumor classification. In this paper, residual network-152 (ResNet-152) with softmax layer is proposed for accurate detection of brain tumor with low complexity. Initially, the brain images are pre-processed and segmented with adaptive canny mayfly algorithm (ACMA). More discriminative features are extracted from the pre-processed image with spatial gray level dependence matrix (SGLDM), and optimal features are selected with modified chimpanzee optimization algorithm (MChOA). The optimal feature selection and optimal performance of classification are obtained by eliminating poor generalization and over specialization. After eliminating redundancies, the features are fed to residual classification. The overall performance of the proposed tumor classification method is evaluated using various parameters such as accuracy, precision, recall, F-score, MCC and balanced accuracy. The evaluation results indicate that our proposed method reached the accuracy level of 98.85%, which is efficient than other conventional approaches such as convolutional neural network (CNN), ResNet, recurrent neural network (RNN), random belief network (RBN), liner support vector machine (LSVM) and poly-SVM.
An Automatic Music-Driven Folk Dance Movements Generation Method Based on Sequence-To-Sequence Network
Authors: Cai Xingquan , Xi Mengyao , Jia Sichen , Xu Xiaowei , Wu Yijie , Sun Haiyan
Published: February 2023

Music-driven automatic dance movement generation has become a hot research topic in the field of computer vision and internet of things in the recent past. To address the problems of increasing loss of Chinese folk dance culture, high cost of manual choreography methods and requirements for professional background, this paper proposes an automatic generation method for folk dance movements. Firstly, the proposed method collects paired folk music and dance videos to construct a synchronized folk music–dance dataset, extracting music and dance features using a feature extraction tool and a multi-scale fusion high-resolution network, respectively. Afterward, a sequence-to-sequence network model is constructed and then trained based on music features and dance features to synthesize rhythmically matched dance sequences for new music clips. Finally, an easy-to-use and effective automatic folk dance choreography method is implemented. Experimental data show that the proposed method performs well in automatic folk dance generation and the generated dances have folk characteristics and match the rhythm of the given music.
Total and paired domination numbers of windmill graphs
Authors: Eakawinrujee Pannawat , Trakultraipruk Nantapath
Published: February 2023

Let [Formula: see text] be a graph without isolated vertices. A total dominating set of [Formula: see text] is a set [Formula: see text] of vertices of [Formula: see text] such that every vertex of [Formula: see text] is adjacent to at least one vertex in [Formula: see text]. A total dominating set [Formula: see text] is a paired dominating set of [Formula: see text] if the subgraph of [Formula: see text] induced by [Formula: see text] has a perfect matching. The minimum cardinality of a total dominating set (respectively, a paired dominating set) is called the total domination number (respectively, the paired domination number). This paper determines the total domination numbers and the paired domination numbers of windmill graphs.
Effect of Big Data Analytics on Performance and Consumer Loyalty of Manufacturing Industries: An Empirical Research
Authors: Mishra Bibhu Prasad , Biswal Bibhuti Bhusan , Behera Ajay Kumar , Das Harish Chandra
Published: February 2023

Big data has, as of late been perceived as one of the most significant domains of upcoming innovation. It has been fascinated numerous enterprises, since it can possibly explore organizations with high market worth. The paper investigates the types of market worth that organizations can make from big data analytics (BDA), the immediate effects it has on the economical performance of an industry, and the impacts of retail performance and consumer loyalty. According to the hypothesis upon resource-based view, investigation exhibits that the market value accomplished from interests in BDA prompts the interest as far as the economical performance (EP) of an industry. The survey is composed of two segments. The first segment, which was replied by all the industries, estimated the nearness or absence of BDA arrangements. We segregated the replies to this segment, and if the responders acknowledged certifiably, they were then approached to involve in the next segment of the poll. Out of the 300 finished questionnaires, 146 of the surveys were discovered appropriate to analyze our model, when our segregating questions in the first segment of the survey had been executed. The outcomes offer proof of the presence of a consumer loyalty (CL) intervention impact and of the absence of a retail performance (RP) impact. Hypothetical and pragmatic ramifications are conferred towards the last part of paper. BDA arrangements should be designed comprehensively to produce operational, organizational, structural and informational value. The outcomes of this research will assist in improving logical information at the junction of innovation and the board sciences. This study has some limitations that open up interesting opportunities for future research. First, this study has had a cross-sectional research design, in which all the measurement items were collected at the same point of time. A longitudinal study could extend this research by capturing the dynamics of the business value of big data analytics arrangements on different industry performances. Second, this research has employed one data collection method. Objective data from multiple sources could be used to further verify the proposed research model. This study has extended relevant theories in big data analytics arrangements by framing, in an original combination, four dimensions of the market worth of big data analytics arrangements on one outcome construct, that is, economical performance and the mediation variable of consumer loyalty.
Evaluation of ISO 9001 Barriers in Manufacturing Organizations Using ISM-Fuzzy MICMAC Approach
Authors: Vikas  , Mishra Akanksha
Published: February 2023

The prime motive of this research study is to identify the barriers of ISO 9001 in manufacturing organizations in order to institute the appropriate associations and classify them into different categories based on their driving power ([Formula: see text]) and dependence power ([Formula: see text]). For this purpose, 20 barriers influencing the ISO 9001 implementation has been identified through existing literature works. Afterwards, interpretive structural modeling (ISM) approach has been applied to develop the relationship hierarchy and fuzzy cross-impact matrix multiplication applies to classification (MICMAC) analysis has been carried out to form the category groups. The frameworks developed in this paper will serve as an information bank in order to identify the significant barriers in ISO 9001 implementation path and develop suitable strategies or steps in order to eliminate them.
Neighborhood Path Complex for the Quantitative Analysis of the Structure and Stability of Carboranes
Authors: Liu Jian , Chen Dong , Pan Feng , Wu Jie
Published: February 2023

Thanks to the tremendous progress in data, computing power and algorithms, AI-based material mining and design have gained much attention. However, building high-performance AI models requires efficient material structure representation. In this work, we propose a structural characterization method based on the neighborhood path complex for the first time. Specifically, we use persistent neighborhood path homology to obtain the structural features by introducing a filtration. This approach preserves more elemental information, as well as the corresponding physicochemical information, through the directed edges of the neighborhood digraph. To validate our model, we perform cross-validation with the carborane structures. The Pearson coefficient for stability prediction is as high as 0.903, which is a 15.5% improvement compared to the traditional persistent homology method. In addition, we constructed a prediction model based on the neighborhood path complex, and the Pearson coefficients for the prediction of carboranes’ HOMO, LUMO, and HOMO–LUMO gaps were 0.915, 0.946, and 0.941, respectively. The results show that our proposed method can effectively extract structural information and achieve accurate material property prediction.
Measuring ground surface elevation changes in a slow-moving colluvial landslide using combinations of regional airborne lidar, UAV lidar and UAV photogrammetric surveys
Authors: Johnson Sarah E. , Haneberg William C. , Bryson L. Sebastian , Crawford Matthew M.
Published: February 2023

Slow-moving, chronically destructive landslides are projected to grow in number globally in response to precipitation increases from climate change, and land disturbances from wildfire, mining and construction. In the Cincinnati and northern Kentucky metropolitan area, USA, landslides develop in colluvium that covers the steep slopes along the Ohio River and its tributaries. Here we quantify elevation changes in a slow-moving colluvial landslide over 14 years using county-wide lidar, uncrewed aerial vehicle (UAV) structure-from-motion (SfM) surveys and a UAV lidar survey. Because the technology and quality differ between surveys, the challenge was to calculate a threshold of detectable change for each survey combination. We introduce two methods; the first uses propagated elevation difference errors and the second back-calculates the individual survey errors. Thresholds of detection range from ±0.05 to ±0.20 m. Record rainfall in 2011 produced the largest vertical changes. Since then, the landslide toe has continued to deform, and the landslide has doubled its width by extending into a previously undisturbed slope. Although this study presents a technique to utilize older datasets in combination with modern surveys to monitor slow-moving landslides, it is broadly applicable to other studies where topographic data of differing quality are available.
Learning Disentangled Features for Person Re-identification under Clothes Changing
Authors: Chan Patrick P. K. , Hu Xiaoman , Song Haorui , Peng Peng , Chen Keke
Published: February 2023

Clothes changing is one of the challenges in person re-identification (ReID), since clothes provide remarkable and reliable information for decision, especially when the resolution of an image is low. Variation of clothes significantly downgrades standard ReID models, since the clothes information dominates the decisions. The performance of the existing methods considering clothes changing is still not satisfying, since they fail to extract sufficient identity information that excludes clothes information. This study aims to disentangle identity, clothes, and unrelated features with a Generative Adversarial Network (GAN). A GAN model with three encoders, one generator, and three discriminators, and its training procedure are proposed to learn these kinds of features separately and exclusively. Experimental results indicate that our model generally achieves the best performance among state-of-the-art methods in both ReID tasks with and without clothes changing, which confirms that the identity, clothes, and unrelated features are extracted by our model more precisely and effectively.
Enforcing Resilience in Cyber-physical Systems via Equilibrium Verification at Runtime
Authors: Camilli Matteo , Mirandola Raffaela , Scandurra Patrizia
Published: February 2023

Cyber-physical systems often operate in dynamic environments where unexpected events should be managed while guaranteeing acceptable behavior. Providing comprehensive evidence of their dependability under change represents a major open challenge. In this article, we exploit the notion of equilibrium, that is, the ability of the system to maintain an acceptable behavior within its multidimensional viability zone and propose RUNE
2
(RUNtime Equilibrium verification and Enforcement), an approach able to verify at runtime the equilibrium condition and to enforce the system to stay in its viability zone. RUNE
2
includes (i) a system specification that takes into account the uncertainties related to partial knowledge and possible changes; (ii) the computation of the equilibrium condition to define the boundaries of the viability zone; (iii) a runtime equilibrium verification method that leverages Bayesian inference to reason about the ability of the system to remain viable; and (iv) a resilience enforcement mechanism that exploits the posterior knowledge to steer the execution of the system inside the viability zone. We demonstrate both benefits and costs of the proposed approach by conducting an empirical evaluation using two case studies and 24 systems synthetically generated from pseudo-random models with increasing structural complexity.
The Design of Fast and Lightweight Resemblance Detection for Efficient Post-Deduplication Delta Compression
Authors: Xia Wen , Pu Lifeng , Zou Xiangyu , Shilane Philip , Li Shiyi , Zhang Haijun , Wang Xuan
Published: February 2023

Post-deduplication delta compression is a data reduction technique that calculates and stores the differences of very similar but non-duplicate chunks in storage systems, which is able to achieve a very high compression ratio. However, the low throughput of widely used resemblance detection approaches (e.g., N-Transform) usually becomes the bottleneck of delta compression systems due to introducing high computational overhead. Generally, this overhead mainly consists of two parts: ① calculating the rolling hash byte by byte across data chunks and ② applying multiple transforms on all of the calculated rolling hash values.

  In this article, we propose Odess, a fast and lightweight resemblance detection approach, that greatly reduces the computational overhead for resemblance detection while achieving high detection accuracy and a high compression ratio. Odess first utilizes a novel Subwindow-based Parallel Rolling (SWPR) hash method using Single Instruction Multiple Data [
1
] (SIMD) to accelerate calculation of rolling hashes (corresponding to the first part of the overhead). Odess then uses a novel Content-Defined Sampling method to generate a much smaller proxy hash set from the whole rolling hash set and quickly applies transforms on this small hash set for resemblance detection (corresponding to the second part of the overhead).

Evaluation results show that during the stage of resemblance detection, the Odess approach is ∼31.4× and ∼7.9× faster than the state-of-the-art N-Transform and Finesse (a recent variant of N-Transform [
39
]), respectively. When considering an end-to-end data reduction storage system, the Odess-based system’s throughput is about 3.20× and 1.41× higher than the N-Transform- and Finesse-based systems’ throughput, respectively, while maintaining the high compression ratio of N-Transform and achieving ∼1.22× higher compression ratio over Finesse.
Modeling Excited States of Molecular Organic Aggregates for Optoelectronics
Authors: Hernández Federico J. , Crespo-Otero Rachel
Published: February 2023

Light-driven phenomena in organic molecular aggregates underpin several mechanisms relevant to optoelectronic applications. Modeling these processes is essential for aiding the design of new materials and optimizing optoelectronic devices. In this review, we cover the use of different atomistic models, excited-state dynamics, and transport approaches for understanding light-activated phenomena in molecular aggregates, including radiative and nonradiative decay pathways. We consider both intra- and intermolecular mechanisms and focus on the role of conical intersections as facilitators of internal conversion. We explore the use of the exciton models for Frenkel and charge transfer states and the electronic structure methods and algorithms commonly applied for excited-state dynamics. Throughout the review, we analyze the approximations employed for the simulation of internal conversion, intersystem crossing, and reverse intersystem crossing rates and analyze the molecular processes behind single fission, triplet-triplet annihilation, Dexter energy transfer, and Förster energy transfer.
Review—Electrode Kinetics and Electrolyte Stability in Vanadium Flow Batteries
Authors: Bourke Andrea , Oboroceanu Daniela , Quill Nathan , Lenihan Catherine , Safi Maria Alhajji , Miller Mallory A. , Savinell Robert F. , Wainright Jesse S. , SasikumarSP Varsha , Rybalchenko Maria , Amini Pupak , Dalton Niall , Lynch Robert P. , Buckley D. Noel
Published: February 2023

Two aspects of vanadium flow batteries are reviewed: electrochemical kinetics on carbon electrodes and positive electrolyte stability. There is poor agreement between reported values of kinetic parameters; however, most authors report that kinetic rates are faster for VIV/VVthan for VII/VIII. Cycling the electrode potential increases the rates of both reactions initially due to roughening but when no further roughening is observed, the VII/VIIIand VIV/VVreactions are affected oppositely by the pretreatment potential. Anodic pretreatment activates the electrode for the VII/VIIIreaction, and deactivates it for VIV/VV. Three states of the carbon surface are suggested: reduced and oxidized states R and O, respectively, both with low electrocatalytic activity, and an intermediate state M with higher activity. The role of surface functional groups and the mechanisms of electron transfer for the VII/VIIIand VIV/VVreactions are still not well understood. The induction time for precipitation of V2O5from positive electrolytes decreases with temperature, showing an Arrhenius-type dependence with an activation energy of 1.79 eV in agreement with DFT calculations based on a VO(OH)3intermediate. It also decreases exponentially with increasing VVconcentration and increases exponentially with increasing sulphate concentration. Both arsenate and phosphate are effective additives for improving thermal stability.
Methods—Ampero-Coulometry: A New Technique for Understanding Lithium-Sulfur Electrochemistry
Authors: Gulzar Umair , Lonergan Alex , Egorov Vladimir , Zhang Yan , Grant Alex , Carroll Aoife , O’Dwyer Colm
Published: February 2023

Despite limited commercial success, lithium sulfur technology (LST) is still far from competing existing Li-ion technology. One of the main reasons hindering the success of LST is the complexity of lithium-sulfur chemistry during electrochemical charging and discharging. Dissolution of sulfur species in the electrolyte solution exacerbates the difficulties of this system. Therefore, a comprehensive understanding of sulfur species and their kinetics during charge/discharge process is paramount for a high-performance lithium-sulfur battery. We present a new technique we refer to as Ampero-Coulometry, which takes the chronoamperometric (galvanostatic) charge-discharge curves and mathematically transforms them to a series of curves that reveal the cation diffusional rate inside carbon-sulfur porous electrodes at different states of charge/capacity. This technique allowed us to track the overall Li+ ion diffusional rate inside a Li-S cell over a complete state of discharge. As dissolution of sulfur species and their interplay inside a porous sulfur electrode has a significant role in limiting Li-S battery capacity, and method allows correlation between the known mechanism of polysulfide dissolution, the kinetics of a sulfur electrode, and its response.
Accuracy and processing time of kidney volume measurement methods in rodents polycystic kidney disease models: superiority of semiautomated kidney segmentation
Authors: Doss Mary Claire , Mullen Sean , Roye Ronald , Zhou Juling , Chumley Phillip , Mrug Elias , Wallace Darren P. , Qian Feng , Harris Peter C. , Yoder Bradley K. , Kim Harrison , Mrug Michal
Published: February 2023

Total kidney volume (TKV) is a valuable readout in preclinical studies for autosomal dominant and autosomal recessive polycystic kidney diseases (ADPKD and ARPKD). Since conventional TKV assessment by manual contouring of kidney areas in all images is time-consuming, we developed a template-based semiautomatic image segmentation method (SAM) and validated it in three commonly used ADPKD and ARPKD models. SAM-based TKV measurements were fast, highly reproducible, and accurate across mouse and rat ARPKD and ADPKD models.
Detailed Study of Sulfur Poisoning and Recovery of Ni-YSZ-Based Anodes Operating up to 1.8 W cm-2 in a Biogas Fuel
Authors: Ma Jianjun , Jiang Yao , Connor Paul A. , Gamble Stephen R. , Cassidy Mark , Jiang Cairong , Irvine John T. S.
Published: February 2023

Ni-YSZ (nickel-yttrium-stabilized zirconia) is a common anode for solid oxide fuel cells (SOFCs) because of its excellent catalytic performance and electronic conductivity. It shows that the nickel anode-supported cell exhibits good cell performance in a biogas fuel of 36CH4-36CO2-20H2O-4H2-4CO. Unfortunately, natural biogas fuels often contain sulfur, so using nickel anodes is not always straightforward. This paper investigates the sulfur poisoning and the recovery of BaCe0.7Zr0.1Y0.1Yb0.1O3-δ- (BCZYYb-) (Ce, Y, and Yb codoped barium zirconate) impregnated nickel anode-supported cells operating up to 1.8 W cm-2 in the biogas. The in situ gas analysis reveals that the suppression of the reforming reactions might cause sulfur poisoning in a 4 ppm (v) H2S (hydrogen sulfide) in open circuit conditions, whereas the current degradation in working conditions could be attributed to the deactivation of reforming reactions and catalyst activity. The incidence of water-gas shift reactions is associated with the degradation rate of these two reactions. After removing the H2S, the recovery is accelerated by a steam hydrogen fuel, indicating that steam facilitates the efficient release of sulfur from nickel sites.
Influence of Control Parameters during Drilling of Aluminum Reinforced with HNT Hybrid Matrix Composites
Authors: Kumar Patnala Padma , Ruban M. , Daniel Ajith Arul , Pugazhenthi R. , Santhosh K.
Published: February 2023

The current paper aims to find the optimum control parameters such as weight percentage of HNT, weight percentage of boron nitride, drilling speed, and feed rate in drilling of aluminum hybrid metal matrix composites. The aluminum matrix material are reinforced with different weight percentage of HNT (3%, 5%, and 7%) and BN (2%, 3%, and 4%). The hybrid matrix are fabricated through the sir casting technique. The prediction of process parameter are optimized using Taguchi assisted grey relation analysis. Also the ANN model is developed to predict the output parameter. Through Taguchi S/N ratio method the effect of each control parameters are optimized. The results show that the weight percentage of HNT and feed rate are the most influencing parameters which affect the drilling of developed aluminum composites. The GRA results show that the optimum parameters are 3% of HNT, 4% of BN, 500 rpm drilling speed, and 20 mm/min in combination, which has minimum surface roughness, lower temperature, highest cutting force, and highest material removal rate.
Nonlinear Tracking Control Algorithm for Dynamical Output Systems Manipulated by the Hardly Constrained Oscillatory Actuator
Authors: Homaeinezhad M. R. , Saeidi Mostaghim M. H.
Published: February 2023

In this article, a nonlinear control algorithm has been presented, in order to reduce vibrational movements of a flexible beam cantilevered in a cart supported by nonlinear stiffness, actuated by a hardly constrained second-order dynamic system. In the presented control algorithm, the interactive model of the solid body and a flexible beam has been extracted and utilized to design an algorithm, based on which the nonlinear vibrations of the tip point of the beam whose function is smooth and nonoscillatory tracking of the predefined desired trajectory are diminished. To design a nonlinear vibration control system of the beam, it has been assumed that the feedback system includes three modules: a vibrational beam cantilevered on a base, a base affected by nonlinear stiffness and actuator’s control force, and a force imposing system with constraints of bandwidth frequency limitation and actuation saturation boundary. In order to design this control system, the generalized tracking error function has been defined to nonlinearly amplify the vibrationally induced error and its rate, which by properly adjusting them, a new quantity can be extracted that satisfies the control system design constraints. To design a tracking control system for a nonlinear vibrating beam for the aforementioned three-modulus system, first, the generalized tracking error for the three modules is separately defined and parametrized, and then, by simultaneously adjusting all the defined parameters systematically, the predominant system presence in Lyapunov stability conditions is guaranteed. To illustrate the multimodule imitative (MMI) controller design algorithm, a moving support connected to a nonlinear spring and damper is considered, which carries a flexible cantilevered beam. The applied actuation system has second-order linear dynamics with the presence of command control saturation boundaries. For each of the abovementioned modules, a generalized tracking error is defined, and then, it is explained to how simultaneously adjust the parameters based on the stability and actuation constraints. The MMI controller is applied to the mentioned mechanical system modeled in the ANSYS® Mechanical APDL environment, and then, the necessary conclusions are discussed about the performance of the control system in eliminating the vibrations of the flexible arm, considering the actuation constraints while possessing the dominant Lyapunov stability.
Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network
Authors: Foysal Md. , Hossain A. B. M. Aowlad , Yassine Abdulsalam , Hossain M. Shamim
Published: February 2023

The infectious coronavirus disease (COVID-19) has become a great threat to global human health. Timely and rapid detection of COVID-19 cases is very crucial to control its spreading through isolation measures as well as for proper treatment. Though the real-time reverse transcription-polymerase chain reaction (RT-PCR) test is a widely used technique for COVID-19 infection, recent researches suggest chest computed tomography (CT)-based screening as an effective substitute in cases of time and availability limitations of RT-PCR. In consequence, deep learning-based COVID-19 detection from chest CT images is gaining momentum. Furthermore, visual analysis of data has enhanced the opportunities of maximizing the prediction performance in this big data and deep learning realm. In this article, we have proposed two separate deformable deep networks converting from the conventional convolutional neural network (CNN) and the state-of-the-art ResNet-50, to detect COVID-19 cases from chest CT images. The impact of the deformable concept has been observed through performance comparative analysis among the designed deformable and normal models, and it is found that the deformable models show better prediction results than their normal form. Furthermore, the proposed deformable ResNet-50 model shows better performance than the proposed deformable CNN model. The gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions’ localization effort at the final convolutional layer and has been found excellent. Total 2481 chest CT images have been used to evaluate the performance of the proposed models with a train-valid-test data splitting ratio of 80 : 10 : 10 in random fashion. The proposed deformable ResNet-50 model achieved training accuracy of 99.5% and test accuracy of 97.6% with specificity of 98.5% and sensitivity of 96.5% which are satisfactory compared with related works. The comprehensive discussion demonstrates that the proposed deformable ResNet-50 model-based COVID-19 detection technique can be useful for clinical applications.