bluuebunny/crossref_metadata_2025 huggingface dataset:
Systematic review and narrative synthesis of the impact of Appreciative Inquiry in healthcare
Authors: Merriel Abi , Wilson Amie , Decker Emily , Hussein Julia , Larkin Michael , Barnard Katie , O’Dair Millie , Costello Anthony , Malata Address , Coomarasamy Arri
Published: June 2022

Background

Appreciative Inquiry is a motivational, organisational change intervention, which can be used to improve the quality and safety of healthcare. It encourages organisations to focus on the positive and investigate the best of ‘what is’ before thinking of ‘what might be’, deciding ‘what should be’ and experiencing ‘what can be’. Its effects in healthcare are poorly understood. This review seeks to evaluate whether Appreciative Inquiry can improve healthcare.

Methods

Major electronic databases and grey literature were searched. Two authors identified reports of Appreciative Inquiry in clinical settings by screening study titles, abstracts and full texts. Data extraction, in duplicate, grouped outcomes into an adapted Kirkpatrick model: participant reaction, attitudes, knowledge/skills, behaviour change, organisational change and patient outcomes.

Results

We included 33 studies. One randomised controlled trial, 9 controlled observational studies, 4 qualitative studies and 19 non-controlled observational reports. Study quality was generally poor, with most having significant risk of bias. Studies report that Appreciative Inquiry impacts outcomes at all Kirkpatrick levels. Participant reaction was positive in the 16 studies reporting it. Attitudes changed in the seventeen studies that reported them. Knowledge/skills changed in the 14 studies that reported it, although in one it was not universal. Behaviour change occurred in 12 of the 13 studies reporting it. Organisational change occurred in all 23 studies that reported it. Patient outcomes were reported in eight studies, six of which reported positive changes and two of which showed no change.

Conclusion

There is minimal empirical evidence to support the effectiveness of Appreciative Inquiry in improving healthcare. However, the qualitative and observational evidence suggests that Appreciative Inquiry may have a positive impact on clinical care, leading to improved patient and organisational outcomes. It is, therefore, worthy of consideration when trying to deliver improvements in care. However, high-quality studies are needed to prove its effects.

PROSPERO registration number

CRD42015014485.
Quality of end-of-life nursing home care in dementia: relatives’ perceptions
Authors: Vandenbogaerde Isabel , De Vleminck Aline , van der Heide Agnes , Deliens Luc , Van den Block Lieve , Smets Tinne
Published: June 2022

Objectives

Only a few studies have investigated the quality of end-of-life care provided to nursing home residents with dementia as perceived by their relatives. We aim to investigate the quality of end-of-life care as perceived by relatives and to investigate which characteristics of nursing home residents with dementia, their relatives and the care they received are associated with the evaluation the quality of end-of-life care as perceived by the relatives.

Methods

Data used were from two cross-sectional studies performed in Flanders in 2010 and 2015. Questionnaires were sent to bereaved relatives of nursing home residents with dementia and 208 questionnaires were returned. The quality of end-of-life care as perceived by the relatives was measured with the End-of-Life with Dementia–Satisfaction With Care scale (scores ranging 10–40).

Results

In total, 208 (response rate2010: 51.05%, response rate2015=60.65%) bereaved relatives responded to the questionnaire. The quality of end-of-life care as perceived by them was positively associated with the nursing home resident being male (b=1.78, p<0.05), relatives receiving information on palliative care (b=2.92, p<0.01) and relatives receiving information about medical care from care providers (b=2.22, p<0.01).

Conclusion

This study suggests that relatives need to be well informed about palliative and medical care. Future end-of-life care interventions in nursing homes should focus on how to increase the information exchange and communication between nursing home staff and relatives.
Addressing inequitable access to hospice care
Authors: Mayland Catriona R , Mitchell Sarah , Flemming Kate , Tatnell Lynn , Roberts Lesley , MacArtney John I
Published: June 2022

The ‘Lancet Commission on the Value of Death’ proposes radical change and challenges the very core of hospice service provision. Without action, inequalities in access to hospice care will continue to be amplified. The COVID-19 pandemic brought increased needs and demands in the community setting but also provided opportunities for new palliative partnerships and ways of working. Returning to the status quo should not be an option. Rather moving towards a shared vision and purpose, which has the person and their community network at its centre, enables hospices to have a pivotal role and bring about more equitable palliative care.
Global quality of life in breast cancer: systematic review and meta-analysis
Authors: Javan Biparva Akbar , Raoofi Samira , Rafiei Sima , Pashazadeh Kan Fatemeh , Kazerooni Mitra , Bagheribayati Farzaneh , Masoumi Maryam , Doustmehraban Maryam , Sanaei Mohaddeseh , Zarabi Farnaz , Raoofi Neda , Beiramy Chomalu Zahra , Ahmadi Behrooz , Seyghalani Talab Fatemeh , Sadat Hoseini Batool , Asadollahi Elnaz , Mir Maryam , Deylami Sama , Zareei Mahsa , Sanaei Hadis , Dousti nia Kakavand Fateme , Koohestani Hooman , Nasiri Mahmoud , Vali Negin , Ghashghaee Ahmad
Published: June 2022

Background

Breast cancer (BC) is the most common cancer among women worldwide. We conducted a systematic review and meta-analysis to cover the existing research gap and contribute to existing knowledge to provide both researchers and clinicians with a better profile on the topic and consequently help improve the quality of life (QoL) of patients with BC.

Methods

A comprehensive review of original articles published in English from January 2000 to October 2021 from databases including Embase, Scopus, PubMed and Web of Science was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

Results

Based on the meta-regression which examined a total of 9012 patients with BC, the QoL score calculated by EORTC QLQ-C30 was 64.72 (95% CI 59.24 to 70.20), while the score obtained from FACT-B was 84.39 (95% CI 64.24 to 104.54) and the scores from QLQ-BR23 and SF-36 were 66.33 (95% CI 62.76 to 69.90) and 57.23 (95% CI 47.65 to 66.82), respectively. A meta-analysis affirmed a significant direct relationship between the QoL score of patients with BC and their age (p=0.03). The results also revealed that the QoL scores of patients who had completed treatment were higher than those who were currently under treatment.

Conclusion

The present systematic review identified several factors that affect the QoL of women with BC worldwide and provided several implications for developing policy interventions to effectively improve the QoL of women with BC. In this way, clinicians can sufficiently give advice to their patients with the purpose of improving their QoL.

PROSPERO registration numberCRD42022309791.
Remote Patient Monitoring in the Management of Chronic Obstructive Pulmonary Disease
Authors: Chalupsky Megan R , Craddock Krystal M , Schivo Michael , Kuhn Brooks T
Published: June 2022

Remote patient monitoring allows monitoring high-risk patients through implementation of an expanding number of technologies in coordination with a healthcare team to augment care, with the potential to provide early detection of exacerbation, prompt access to therapy and clinical services, and ultimately improved patient outcomes and decreased healthcare utilization.

In this review, we describe the application of remote patient monitoring in chronic obstructive pulmonary disease including the potential benefits and possible barriers to implementation both for the individual and the healthcare system.
Direct to angiosuite strategy versus standard workflow triage for endovascular therapy: systematic review and meta-analysis
Authors: Galecio-Castillo Milagros , Vivanco-Suarez Juan , Zevallos Cynthia B , Dajles Andres , Weng Julie , Farooqui Mudassir , Ribo Marc , Jovin Tudor G , Ortega-Gutierrez Santiago
Published: June 2022

Background

Reducing stroke workflow times when performing endovascular thrombectomy is associated with improvement in clinical outcomes. We compared outcomes among large vessel occlusion (LVO) stroke patients following the direct to angiosuite (DTAS) strategy versus standard workflow (SW) when undergoing endovascular therapy.

Methods

We conducted a systematic review and meta-analysis to compare rates of functional outcomes, reperfusion, symptomatic intracranial hemorrhage (sICH) and stroke workflow metrics. We included observational studies and clinical trials that compared the DTAS strategy versus SW, and at least one outcome of interest was assessed. Clinical, methodological and statistical heterogeneity were measured, and a random-effects model was used.

Results

12 studies were included in the systematic review and 8 in the meta-analysis (n=2890). The DTAS strategy was associated with significant higher odds of good functional outcome at 90 days (47.3% vs 34.9%; OR 1.58, 95% CI 1.16 to 2.14) and a significant average reduction of door-to-puncture (mean differences (MD) −35.09, 95% CI −49.76 to −20.41) and door-to-reperfusion times (MD –32.88, 95% CI –50.75 to –15.01). We found no differences in sICH (OR 0.80, 95% CI 0.53 to 1.20), mortality (OR 1.00, 95% CI 0.60 to 1.67) or successful reperfusion rates (OR 1.37, 95% CI 0.82 to 2.29). Moreover, the DTAS strategy was associated with greater odds of dramatic clinical improvement at 24 hours (OR 1.79, 95% CI 1.15 to 2.79).

Conclusion

Patients undergoing the DTAS strategy had a significant reduction in door-to-puncture and door-to-reperfusion times. This resulted in an increased rate of early neurological and 90-day functional recovery without compromising safety in LVO patients undergoing endovascular thrombectomy.
Preprocedural determination of an occlusion pathomechanism in endovascular treatment of acute stroke: a machine learning-based decision
Authors: Baek Jang-Hyun , Kim Byung Moon , Kim Dong Joon , Heo Ji Hoe , Nam Hyo Suk , Kim Young Dae , Rho Myung Ho , Chung Pil-Wook , Won Yu Sam , Chung Yeongu
Published: June 2022

Objective

To evaluate whether an occlusion pathomechanism can be accurately determined by common preprocedural findings through a machine learning-based prediction model (ML-PM).

Methods

A total of 476 patients with acute stroke who underwent endovascular treatment were retrospectively included to derive an ML-PM. For external validation, 152 patients from another tertiary stroke center were additionally included. An ML algorithm was trained to classify an occlusion pathomechanism intoembolicorintracranial atherosclerosis. Various common preprocedural findings were entered into the model. Model performance was evaluated based on accuracy and area under the receiver operating characteristic curve (AUC). For practical utility, a decision flowchart was devised from an ML-PM with a few key preprocedural findings. Accuracy of the decision flowchart was validated internally and externally.

Results

An ML-PM could determine an occlusion pathomechanism with an accuracy of 96.9% (AUC=0.95). In the model, CT angiography-determined occlusion type, atrial fibrillation, hyperdense artery sign, and occlusion location were top-ranked contributors. With these four findings only, an ML-PM had an accuracy of 93.8% (AUC=0.92). With a decision flowchart, an occlusion pathomechanism could be determined with an accuracy of 91.2% for the study cohort and 94.7% for the external validation cohort. The decision flowchart was more accurate than single preprocedural findings for determining an occlusion pathomechanism.

Conclusions

An ML-PM could accurately determine an occlusion pathomechanism with common preprocedural findings. A decision flowchart consisting of the four most influential findings was clinically applicable and superior to single common preprocedural findings for determining an occlusion pathomechanism.
Symmetric states for C∗-Fermi systems
Authors: Fidaleo Francesco
Published: June 2022

After introducing the infinite Fermi [Formula: see text]-tensor product of a single [Formula: see text]-graded [Formula: see text]-algebra as an inductive limit, we systematically study the structure of the so-called symmetric states, that is those which are invariant under the group consisting of all finite permutations of a countable set. Among the obtained results, we mention the extension of De Finetti theorem which asserts that a symmetric state is a “mixture” of product states, each of which is a product of a single even state. This result induces a canonical morphism of the simplexes made of the symmetric even states on the usual infinite [Formula: see text]-tensor product and the symmetric states on the infinite Fermi [Formula: see text]-tensor product. We then extend the so-called Klein transformation to the infinite Fermi [Formula: see text]-tensor product, available when the parity automorphism is inner. In such a situation, we investigate further properties of product states, the last being the extremal symmetric states on such an infinite Fermi [Formula: see text]-tensor product [Formula: see text]-algebra. This paper is complemented with a finite dimensional illustrative example for which the Klein transformation is not implementable, and then the Fermi tensor product might not generate a usual tensor product. Therefore, in general, the study of the symmetric states on the Fermi algebra cannot be easily reduced to that of the corresponding symmetric states on the usual infinite tensor product, even if both share many common properties.
Optimized Convolutional Neural Network for Tamil Handwritten Character Recognition
Authors: Lincy R. Babitha , Gayathri R.
Published: June 2022

Digital recognitions are playing a vital function in the current era of technological advancements. Hence, they offer more possible ways of performing handwritten character recognition (HCR). Generally, recognizing the Tamil handwritten texts is highly complicated, in comparison to the Western scripts. Nevertheless, many researchers have presented several real-time approaches to achieve Tamil character recognition (TCR) in offline mode. This paper introduces a new handwritten TCR (HTCR) approach with two phases: (1) pre-processing and (2) classification. Primarily, the scanned document in the Tamil language is pre-processed via the steps like RGB to grayscale conversion, binarization with thresholding, image complementation, application of morphological operations and linearization. The pre-processed images are then classified using an optimized convolutional neural network (CNN) model. Further, the fully connected layer (FCL) and the weights are tuned optimally via a new sea lion with self-adaptiveness (SL-SA) algorithm. Lastly, the adopted model is evaluated using various measures to prove its supremacy over the existing schemes.
3D Human Pose Estimation via Spatio-Temporal Matching from Monocular RGB Images
Authors: Yan Jielu , Zhou Ming Liang , Fang Bin , Xu Ke
Published: June 2022

Three-dimensional (3D) human pose estimation aims to locate 3D keypoints of individuals from given input RGB images. For two-dimensional (2D) human pose estimation problems, majority methods inferring 2D poses are from 2D heatmaps. However, it is hard to extend this method to 3D poses inferring area which makes computational loads increase sharply. To address the above problem, we propose STM-CNN method to estimate reconstruction coefficient matrix to calculate the final 3D pose instead of estimating 3D heatmaps to decrease the computational loads. First, STM-CNN does a preprocessing procedure to calculate a set of shape and weight bases. Second, STM-CNN infers a 2D matrix called reconstruction coefficient from the STM-CNN architecture. Third, STM-CNN utilizes the preprocessing shape and weight bases and estimated reconstruction coefficient matrix to calculate the final 3D pose. Meanwhile, STM-CNN method achieves better performances compared with the state-of-the-art methods on Human3.6M.
Design and Implementation of Low Power, High-Speed Configurable Approximation 8-Bit Booth Multiplier
Authors: Kumar Sampath , Poonia Minakshi , Kumar Rahul , Sharma Gaurav , Kumar Somesh
Published: June 2022

Multimedia, machine learning and deep learning applications have a significant constraint on power consumption. A multiplier is a crucial component for many error-aware applications. An efficient approximate computing scheme is used for the error-tolerant applications due to higher accuracy in power cases. In the Booth, multiplier approximation is implemented for partial product generation and accumulations network. The significant stage of a multiplier is accumulation. In this paper, an efficient accumulation stage is suggested for the Radix-4 and 8-bit approximate Booth multiplier. The proposed accumulation multiplier has high speed, minimum area, negligible path delay and low power consumption. Compared to the Booth multiplier design with modified Booth encoding and conventional carry look-ahead adder for product generation with no other error, the proposed 8-bit multiplier design-I reduced power consumption, area and delay a maximum of 13.7%, 8.4% and 19.8%, respectively. Also, our proposed design is compared with the design of Booth multiplier with approximate Booth encoding and conventional carry look-ahead adder for product generation. The proposed 8-bit multiplier design-II reduced power consumption, area and delay by a maximum of 38.2%, 28.3% and 13.7%, respectively.
A Fully Integrated Mixed-Mode LDO Regulator with Fast Transient Response Performance
Authors: Abugharbieh Khaldoon , Yaseen Basel , Deeb Abdullah , Ahmad Hani , Jeit Ayman
Published: June 2022

This work presents a fully integrated mixed-mode low-dropout voltage regulator that achieves a fast transient response by utilizing two feedback mechanisms. The first feedback mechanism is the conventional analog regulation that includes an operational amplifier. The second feedback mechanism is based on digitizing any fast change in the output voltage using multiple comparators and subsequently enabling either an NMOS-based or a PMOS-based current DAC. The DAC provides current in opposite polarity to the sharp transient change in load current. As a result, addressing sharp changes in load current is not limited by the gain–bandwidth product of the error amplifier. The LDO was implemented using 180-nm CMOS technology devices. It uses a supply voltage input range of 1.6–2[Formula: see text]V and produces an output voltage of 1.2[Formula: see text]V. In simulations, the LDO regulator achieves 143-[Formula: see text] A quiescent current, [Formula: see text]56-dB PSRR @ 1-kHz noise frequency and an output voltage drop of around 200[Formula: see text]mV for a load current step of 100[Formula: see text]mA. The LDO can provide a maximum load current of 200[Formula: see text]mA.
Effectiveness of Acapella along with institutional based chest physiotherapy techniques on pulmonary functions and airway clearance in post-operative CABG patients
Authors: Jage Bhagyashree , Thakur Anuprita
Published: June 2022

Background: Patients undergoing Coronary Artery Bypass Graft (CABG) surgery often develop pulmonary complications in the early post-operative period as result of decreased lung function and impaired cough. Conventional physiotherapy in early post-operative period aims at increasing lung volumes and airway clearance.

Objective: This study aimed to determine the effectiveness of the addition of Acapella to conventional chest physiotherapy in improving lung volumes and secretion clearance in early post-operative CABG patients.

Methods: Twenty patients of both genders (40–70 years) who had undergone CABG and were in Phase I of Cardiac Rehabilitation were involved in this pilot randomized control trial (9 control, 11 experimental). Post-surgery intervention commenced on post-operative day 2 (POD 2) and continued till POD 6. Patients in the control group were given conventional physiotherapy that included breathing exercises, incentive spirometry and manual techniques. Patient in the experimental group used an Acapella device along with the conventional intervention. Outcome measures considered were pulmonary function parameters (FVC, FEV1 & PEFR) and amount of sputum expectorated.

Results: A significant increase in lung volumes was observed in both the groups on POD 6 as compared to POD 2 ([Formula: see text]). However, the increase was significantly greater on POD 6 in experimental group than the control group [mean difference (95% CI) FVC: 0.44[Formula: see text]L (0.24–0.63), FEV1: 0.43[Formula: see text]L (0.19–0.66), PEFR: 0.86[Formula: see text]L/s (0.57–1.14)]. The amount of sputum expectoration significantly greater in the experimental group as compared to the control group [2.71[Formula: see text]mL (0.53–4.90)].

Conclusion: The addition of Acapella enhanced the effect conventional physiotherapy in improving lung volumes and airway clearance in the early post-operative period for CABG patients.
A VALUES-BASED APPROACH TO RADICAL INNOVATION: FACILITATING THE REINTERPRETATION OF CORE VALUES THROUGH DESIGN-DRIVEN PRACTICES
Authors: CASTELLAZZI SILVIA ELEONORA , BELLINI EMILIO
Published: June 2022

This paper contributes to a growing body of literature on values-based approaches to innovation. In particular, we explore the role that core values should have in radical innovation processes and in guiding the ability of organisations in learning and unlearning, i.e., identifying and developing abilities and competences coherent with the values of the organisation. First, the paper provides a conceptual explanation: it clarifies and highlights the characteristics and implications that core values should have when considered within a radical innovation approach, in particular their ability to be embodied in design objects (meaningful to internal individuals and external customers), and their ability to be open to contingency, deriving their strength from their ability to evolve and be reinterpreted. Secondly, based on this, we put forward a design-driven and interpretation-based facilitation method to support organisations in making sense of their past and future core values and illustrate this method by positioning it when compared with other existing methods. An overview of the outputs of the method and their knowledge value for the organisation together with an illustration conclude the paper.
Support Vector Machine-Based Phase Prediction of Multi-Principal Element Alloys
Authors: Chau Nguyen Hai , Kubo Masatoshi , Hai Le Viet , Yamamoto Tomoyuki
Published: June 2022

Designing new materials with desired properties is a complex and time-consuming process. One of the most challenging factors of the design process is the huge search space of possible materials. Machine learning methods such as [Formula: see text]-nearest neighbors, support vector machine (SVM) and artificial neural network (ANN) can contribute to this process by predicting materials properties accurately. Properties of multi-principal element alloys (MPEAs) highly depend on alloys’ phase. Thus, accurate prediction of the alloy’s phase is important to narrow down the search space. In this paper, we propose a solution of employing SVM method with hyperparameters tuning and the use of weighted values for prediction of the alloy’s phase. Using the dataset consisting of the experimental results of 118 MPEAs, our solution achieves a cross-validation accuracy of 90.2%. We confirm the superiority of this score over the performance of ANN statistically. On the other dataset containing 401 MPEAs, our SVM model is comparable to ANN and exhibits 70.6% cross-validation accuracy. We also found that additional variables, including average melting temperature and standard deviation of melting temperature, increase prediction accuracy by 3.34% in the best case.
New field observations on the Quaternary geology and vertebrate palaeontological occurrences in the Narsinghpur region of Narmada valley (central India)
Authors: Lal Ravish , Padhan Tosabanta , Jangra Bharti , Chauhan Parth R. , Sahu Shivam , Patnaik Rajeev
Published: June 2022

Since the beginning of the nineteenth century, the Narmada River Valley has been well known for discoveries of abundant vertebrate fossil localities, a wide range of prehistoric archaeological assemblages and the first-known hominin fossil site in India at Hathnora. The fossil and archaeological remains are found in various sedimentary contexts: boulder conglomerate, pebble-rich sand, cross-bedded, coarse-to-fine sand and reddish brown clay in the Early to Late Pleistocene deposits. Our preliminary field investigations in the Narsinghpur region of the basin mark the presence of 26 individual localities within a stretch of 70 km of the Narmada River and adjoining tributaries. Each location was documented by GPS and the geological details were recorded while collecting the fossils. Past and current palaeontological research has revealed that a variety of fauna adapted to the sub-humid climate of both aquatic and terrestrial landscapes that thrived in this area during the Pleistocene period. One of our long-term goals is to understand the reasons for the high number of palaeontological occurrences compared with other parts of India and use such information to predict the locations of new occurrences, including hominin fossils. Various taphonomic processes in this region are responsible for the preservation and destruction of fossil assemblages in different geological formations: soil type, climatic conditions, rich calcium carbonate presence, encrustation, patination and modern anthropogenic activities (among others). Most of the fossil localities are found at an elevation range of 310–350 m above mean sea level (AMSL). The Narmada Basin was inhabited by prehistoric human populations and a variety of fauna, as evidenced by the ample lithic and fossil assemblages across the region. There are definitive contextual patterns of occurrences of the Quaternary vertebrate fossils and archaeological assemblages in the landscape, which require detailed investigations and mapping to understand the spatial distribution pattern and nature of associated sedimentary environments. In addition, this uniquely fossil-rich area requires proper protection and long-term preservation as it is heavily impacted by modern anthropogenic factors such as agriculture, sand mining and other activities.
Understanding contaminant transport in Chalk from petroleum hydrocarbon and chlorinated solvent contamination investigations
Authors: Dent V. , Dewhurst R. E. , Dottridge J.
Published: June 2022

The Chalk is a strategically important aquifer in the UK, but it is at risk of contamination, with impacts on abstractions, because of extensive outcrop. Assessments of the risk of contamination impacting water supply are commonly based on bulk aquifer parameters, usually reported for abstraction wells, and do not consider the macro- and micro-scale variation at the site and within the Chalk succession. The presence of less permeable layers is significant in restricting contaminant entry into the aquifer, pushing the contaminants into fractures and voids, where their flow is limited by density differences that cause hydrocarbons to smear into micro-fractures. Dense solvents flowing to depth are further impacted by the presence of lower permeability layers within the Chalk, while horizontal flow is affected by fractures and faults. Porewater concentrations of contaminants are limited, by pore throat size and limited connectivity, to low concentrations of the most soluble compounds, but are characterized by electron acceptors that are observed as reduced and oxidized species.

The combination of macro- and micro-scale physical structures, combined with natural attenuation, means that initial risk assessments for hydrocarbons and solvents in Chalk can be highly conservative and should be supported by further targeted investigation.
Key Extraction Using Ambient Sounds for Smart Devices
Authors: Zhang Yuexin , Zhou Fengjuan , Huang Xinyi , Xu Li , Ye Ayong
Published: June 2022

To secure communications, this article presents a key extraction scheme for smart devices using ambient sounds. Specifically, it is designed for the scenario when smart devices do not have any pre-loaded secrets. Moreover, it can be implemented when smart devices have no access to the online trusted third party or the Network Time Protocol server. In our scheme, smart devices achieve synchronization by making use of ambient sounds. Then, they calculate and obtain the pairing distance and secure distance by applying a band-pass filter. Completing these operations, two smart devices can directly extract a communication key. We analyze the security of our scheme and evaluate the performance of it by implementing the scheme using a few off-the-shelf devices. The experimental results indicate that compared with related schemes, the bit generation rate of our scheme has significant improvement (increases at least 80%), and it can reach 312 bit/s.
Millimeter Wave and Free-space-optics for Future Dual-connectivity 6DOF Mobile Multi-user VR Streaming
Authors: Chakareski Jacob , Khan Mahmudur , Ropitault Tanguy , Blandino Steve
Published: June 2022

Dual-connectivity streaming is a key enabler of next-generation six Degrees Of Freedom (6DOF) Virtual Reality (VR) scene immersion. Indeed, using conventional sub-6 GHz WiFi only allows to reliably stream a low-quality baseline representation of the VR content, while emerging high-frequency communication technologies allow to stream in parallel a high-quality user viewport-specific enhancement representation that synergistically integrates with the baseline representation to deliver high-quality VR immersion. We investigate holistically as part of an entire future VR streaming system two such candidate emerging technologies, Free Space Optics (FSO) and millimeter-Wave (mmWave), that benefit from a large available spectrum to deliver unprecedented data rates. We analytically characterize the key components of the envisioned dual-connectivity 6DOF VR streaming system that integrates in addition edge computing and scalable 360° video tiling, and we formulate an optimization problem to maximize the immersion fidelity delivered by the system, given the WiFi and mmWave/FSO link rates, and the computing capabilities of the edge server and the users’ VR headsets. This optimization problem is mixed integer programming of high complexity and we formulate a geometric programming framework to compute the optimal solution at low complexity. We carry out simulation experiments to assess the performance of the proposed system using actual 6DOF navigation traces from multiple mobile VR users that we collected. Our results demonstrate that our system considerably advances the traditional state of the art and enables streaming of 8K-120 frames-per-second (fps) 6DOF content at high fidelity.
AggEnhance: Aggregation Enhancement by Class Interior Points in Federated Learning with Non-IID Data
Authors: Ou Jinxiang , Shen Yunheng , Wang Feng , Liu Qiao , Zhang Xuegong , Lv Hairong
Published: June 2022

Federated learning (FL) is a privacy-preserving paradigm for multi-institutional collaborations, where the aggregation is an essential procedure after training on the local datasets. Conventional aggregation algorithms often apply a weighted averaging of the updates generated from distributed machines to update the global model. However, while the data distributions are non-IID, the large discrepancy between the local updates might lead to a poor averaged result and a lower convergence speed, i.e., more iterations required to achieve a certain performance. To solve this problem, this article proposes a novel method named AggEnhance for enhancing the aggregation, where we synthesize a group of reliable samples from the local models and tune the aggregated result on them. These samples, named class interior points (CIPs) in this work, bound the relevant decision boundaries that ensure the performance of aggregated result. To the best of our knowledge, this is the first work to explicitly design an enhancing method for the aggregation in prevailing FL pipelines. A series of experiments on real data demonstrate that our method has noticeable improvements of the convergence in non-IID scenarios. In particular, our approach reduces the iterations by 31.87% on average for the CIFAR10 dataset and 43.90% for the PASCAL VOC dataset. Since our method does not modify other procedures of FL pipelines, it is easy to apply to most existing FL frameworks. Furthermore, it does not require additional data transmitted from the local clients to the global server, thus holding the same security level as the original FL algorithms.
Evolution of Tuberculosis Pathogenesis
Authors: Pepperell Caitlin S.
Published: June 2022

Mycobacterium tuberculosis is a globally distributed, lethal pathogen of humans. The virulence armamentarium of M. tuberculosis appears to have been developed on a scaffold of antiphagocytic defenses found among diverse, mostly free-living species of Mycobacterium. Pathoadaptation was further aided by the modularity, flexibility, and interactivity characterizing mycobacterial effectors and their regulators. During emergence of M. tuberculosis, novel genetic material was acquired, created, and integrated with existing tools. The major mutational mechanisms underlying these adaptations are discussed in this review, with examples. During its evolution, M. tuberculosis lost the ability and/or opportunity to engage in lateral gene transfer, but despite this it has retained the adaptability that characterizes mycobacteria. M. tuberculosis exemplifies the evolutionary genomic mechanisms underlying adoption of the pathogenic niche, and studies of its evolution have uncovered a rich array of discoveries about how new pathogens are made.
Introducing the Circular Economy to Economists
Authors: Fullerton Don , Babbitt Callie W. , Bilec Melissa M. , He Shan , Isenhour Cindy , Khanna Vikas , Lee Eunsang , Theis Thomas L.
Published: June 2022

A circular economy (CE) would reduce both extraction and disposal by encouraging green design and circular business models, as well as repair, reuse, remanufacturing, and recycling. The CE started among architects and engineers, with little interest among economists. This article introduces CE concepts to economists, introduces key insights about the CE from other disciplines, and describes how economists can use these insights for a more complete economic analysis of policies that can better improve human welfare. An economic model of CE behavior can benefit from understanding the environmental gains from green designs based on engineering,transaction-cost savings from information based on blockchain technology, life cycle assessments based on industrial ecology, and behavioral science concepts of cultural barriers and social decision making that affect how producers and consumers respond to incentives. With various disciplines brought to bear on the subject, the combined analysis can exceed the sum of its parts.
Liquid Phase Partitioning in Virus Replication: Observations and Opportunities
Authors: Wu Chao , Holehouse Alex S. , Leung Daisy W. , Amarasinghe Gaya K. , Dutch Rebecca Ellis
Published: June 2022

Viruses frequently carry out replication in specialized compartments within cells. The effect of these structures on virus replication is poorly understood. Recent research supports phase separation as a foundational principle for organization of cellular components with the potential to influence viral replication. In this review, phase separation is described in the context of formation of viral replication centers, with an emphasis on the nonsegmented negative-strand RNA viruses. Consideration is given to the interplay between phase separation and the critical processes of viral transcription and genome replication, and the role of these regions in pathogen-host interactions is discussed. Finally, critical questions that must be addressed to fully understand how phase separation influences viral replication and the viral life cycle are presented, along with information about new approaches that could be used to make important breakthroughs in this emerging field.
Pb3(OH)2(CO3)2-Acetylene Black Composites for Enhanced Hydrogen Evolution Reaction Inhibition of Lead-Acid Batteries
Authors: Hu Jie , Yang Fei , Lai Changgan , Wang Helin , Nie Liu , Sun Jian , Zhou Huan , Lei Lixu
Published: June 2022

Pb3(OH)2(CO3)2-acetylene black (BLC/AB) composite is successfully prepared by a simple and economical sonochemical method and employed as a negative additive for lead-acid batteries (LABs). The electrochemical measurements show that the obtained BLC/AB electrodes have a higher hydrogen evolution reaction (HER) overpotential compared to AB electrode. BLC/AB-2 and BLC/AB-4 battery deliver the excellent rate performance at 1C rate. What’s more, acetylene black as nucleation centers of Pb3(OH)2(CO3)2 may considerably alleviate irreversible sulfation phenomenon of negative plate, thus enhancing the conductivity and extending the cycle life. Interestingly, the cycle life (1172) of BLC/AB-2 battery is 1.44 times that of the same amount of AB battery (814) at 1C rate. Therefore, the BLC/AB composite is a promising negative additive of LABs.
Prenatal fluoxetine has long-lasting, differential effects on respiratory control in male and female rats
Authors: Biancardi Vivian , Patrone Luis Gustavo A. , Vicente Mariane C. , Marques Danuzia A. , Bícego Kênia C. , Funk Gregory D. , Gargaglioni Luciane H.
Published: June 2022

Selective serotonin reuptake inhibitors (SSRIs) readily cross the placental and the fetal blood-brain barrier where it will affect 5-HT levels in the developing brain. Although SSRI is used during pregnancy, there are no studies showing SSRI exposure during late pregnancy and postnatal effects on breathing control in males and females. We demonstrated that fluoxetine exposure during late pregnancy in rats was associated with long-lasting, sex-specific effects on breathing and brainstem monoaminergic groups.
Time-course recovery of cerebral blood velocity metrics post aerobic exercise: a systematic review
Authors: Kennedy Courtney M. , Burma Joel S. , Newel Kailey T. , Brassard Patrice , Smirl Jonathan D.
Published: June 2022

Currently, the standard approach for restricting exercise prior to cerebrovascular data collection varies widely between 6 and 24 h. This universally employed practice is a conservative approach to safeguard physiological alterations that could potentially confound one’s study design. Therefore, the purpose of this systematic review was to amalgamate the existing literature examining the extent and duration of postexercise alterations in cerebrovascular function, measured via transcranial Doppler ultrasound. Furthermore, an exploratory aim was to scrutinize and discuss common biases/limitations in the previous studies to help guide future investigations. Search strategies were developed and imported into PubMed, SPORTDiscus, and Medline databases. A total of 595 records were screened and 35 articles met the inclusion criteria in this review, which included assessments of basic cerebrovascular metrics ( n = 35), dynamic cerebral autoregulation (dCA; n = 9), neurovascular coupling (NVC; n = 2); and/or cerebrovascular reactivity (CVR-CO2; n = 1) following acute bouts of aerobic exercise. Across all studies, it was found that NVC was impacted for 1 h, basic cerebrovascular parameters and CVR-CO2 parameters for 2 h, and dCA metrics for 6 h postexercise. Therefore, future studies can provide participants with these evidence-based time restrictions, regarding the minimum time to abstain from exercise prior to data collection. However, it should be noted that other physiological mechanisms could still be altered (e.g., metabolic, hormonal, and/or autonomic influences), despite cerebrovascular function returning to baseline levels. Thus, future investigations should seek to control as many physiological influences when using cerebrovascular assessments, immediately following these time restraints. The main limitations/biases were lack of female participants, cardiorespiratory fitness, and consideration for vessel diameter.
Vomiting Management and Effect Prediction after Early Chemotherapy of Lung Cancer with Diffusion-Weighted Imaging under Artificial Intelligence Algorithm and Comfort Care Intervention
Authors: Mei Cailing , Zhang Ling , Zhang Zhiying
Published: June 2022

This aim of this research was to explore the evaluation and prediction value of diffusion-weighted imaging (DWI) under artificial intelligence algorithm in the vomiting management and chemotherapy of early lung cancer under comfort care. 118 patients with lung cancer were included as the research objects. They were randomly divided into the control group (routine care) and the experiment group (comfort care) with 59 cases in each. The DWI under the weighted nuclear norm minimization (WNNM) noise reduction algorithm was used for examinations. The noise reduction effect of the algorithm under different Gaussian noises, as well as the sensitivity, specificity, and area under the curve (AUC) of the apparent diffusion coefficient (ADC) maps under different

b

values, was compared and analyzed. The indicators of vomiting, psychological state, quality of life, serum tumor marker levels, and nursing satisfaction were also compared between the two groups of patients after chemotherapy. Compared to the photon mapping (PM) algorithm and the total variation (TV) norm minimization algorithm, the WNNM algorithm had the most ideal noise reduction effect with clearer images, which was conducive to identification. When the

b

value was 800 s/mm2, the ADC chart had the best sensitivity, specificity, and AUC values of 0.95, 0.89, and 0.87, respectively. After chemotherapy, 45.76% of patients in the experiment group had vomiting in degree 0 and 40.68% had that in degree I, which suggested that the incidence of vomiting was significantly lower than that in the control group (

P
<
0.05

). All of the psychological state, quality of life, serum tumor marker levels, and nursing satisfaction of patients in the experiment group were significantly better than those in the control group (

P
<
0.05

). It showed that comfort care could alleviate the vomiting response effectively of patients with lung cancer after chemotherapy and had significant effects in improving the quality of life, the psychological state, and curative effect of patients. WNNM algorithm had the better noise reduction effect in DWI image processing. This work provided a certain reference for the nursing intervention plan after chemotherapy of early lung cancer.
The Impact of the Live Delivery of Goods on Consumers’ Purchasing Behaviour in Complex Situations Based on Artificial Intelligence Technology
Authors: Li Xia
Published: June 2022

With the development of information technology, the purchase of goods online has acquired a different dimension. This type of online purchase has made life easier for individuals by reducing the time required to travel and purchase. This online purchasing and delivery of goods or products follow the concept of supply chain management. Mobile Internet, tracking sensors, and tags that aid in tracking processes are used in online supply chain management. For data transfer and status updates, these devices rely on wireless sensor networking technology. This research analyses consumer behaviour after the delivery of goods using wireless sensor networks and artificial intelligence. This behaviour analysis focuses on whether the consumer is satisfied with the product in complex situations or not. The analysis is performed with the Differential-Evolutionary Multiobjective Optimization (D-EMO) algorithm with the aid of artificial intelligence. The proposed system’s results were compared with the existing random forest algorithm, and it was observed that the proposed system had provided a 5% increase in accuracy.
Velocity-Free Adaptation Compensation Control of MEMS with Prescribed Performance
Authors: Dong Jie , Zhang Yanzhao , Liu Xiaotian , Song Zhankui
Published: June 2022

This study investigates the prescribed performance control problem for microelectro-mechanical system (MEMS) gyroscope subject to system parameters’ uncertainty. A finite-time observer is firstly designed to estimate the unmeasurable velocity state of MEMS gyroscope. Subsequently, a coordinate transformation with the performance function is introduced into an error system which will be kept bounded to ensure expected dynamic and steady-state responses. Based on the proposed finite time-velocity reconstruction system, the adaptive backstepping design procedure is further designed to deal with the lumped uncertainty term. Furthermore, when considering actuator saturation, an improved control strategy is developed with a nonlinear input updating law, and meanwhile, it is proved that the system error converges to a preset compact set around zero in a preassigned time. Simulation results show the effectiveness and reliability of the proposed methods.
Optimization of Nanofiber Wearable Heart Rate Sensor Module for Human Motion Detection
Authors: Tang Xiangbin , Yang Aihua , Li Liangming
Published: June 2022

In order to further improve the detection performance of the wearable heart rate sensor for human physiological and biochemical signals and body kinematics performance, the wearable heart rate sensor module was optimized by using nanofibers. Nanoparticle-doped graphene films were prepared by adding nanoparticles to a graphene oxide solution. The prepared film was placed in toluene, and the nanoparticles were removed to complete the preparation of a graphene film with a porous microstructure. The graphene film and the conductive film together formed a wearable heart rate sensor module. The strain response test of the porous graphene film wearable heart rate sensor module verifies the validity of the research in this paper. The resistance change of the wearable heart rate sensor module based on the PGF-2 film is 8 to 16 times higher than that of the RGO film, and the sensitivity is better, proving that the sensor module designed by this method shows significant application potential in human motion detection.
Design of Threat Response Modeling Language for Attacker Profile Based on Probability Distribution
Authors: Zhang Shuqin , Wang Shijie , Bai Guangyao , Zhang Minzhi , Chen Peng , Zhao Chunxia , Li Shuhan , Zhou Jiehan
Published: June 2022

Threat modeling and simulation (TMS) was aimed at dynamically capturing the features of attacks, which is a challenging job in complex Industrial Internet of Things (IIoT) control systems due to the complicated relationships among attacks. Recently, Meta Attack Language (MAL) showed its powerful TMS capabilities for representing complex attacks. However, existing methods pay less attention to the impact of changes in threat profiles on the simulation of key attack techniques. This paper proposes a novel method called threat response modeling language (TRMLang) for threat modeling and simulation in complex IIoT attacks. TRMLang obtains attacker information through an automated analysis of cyber threat intelligence (CTI) to build dynamic attacker profiles. Furthermore, it merges attacker features and probabilistic attack graphs in the simulation to improve TMS performance. The experimental results demonstrate that TRMLang can represent and evaluate the security conditions of IIoT control systems with two attack cases by Lazarus Group on SEGRID smart grids.