bluuebunny/crossref_metadata_2025 huggingface dataset:
STRategies to manage Emergency ambulance Telephone Callers with sustained High needs: an Evaluation using linked Data (STRETCHED) – a study protocol
Authors: Aslam Rabeea'h W , Snooks Helen , Porter Alison , Khanom Ashrafunnesa , Cole Robert , Edwards Adrian , Edwards Bethan , Evans Bridie Angela , Foster Theresa , Fothergill Rachael , Gripper Penny , John Ann , Petterson Robin , Rosser Andy , Tee Anna , Sewell Bernadette , Hughes Heather , Phillips Ceri , Rees Nigel , Scott Jason , Watkins Alan
Published: March 2022

Introduction

UK ambulance services have identified a concern with high users of the 999 service and have set up ‘frequent callers’ services, ranging from within-service management to cross-sectoral multidisciplinary case management approaches. There is little evidence about how to address the needs of this patient group.

Aim

To evaluate effectiveness, safety and efficiency of case management approaches to the care of people who frequently call the emergency ambulance service, and gain an understanding of barriers and facilitators to implementation.

Objectives

(1) Develop an understanding of predicted mechanisms of change to underpin evaluation. (2) Describe epidemiology of sustained high users of 999 services. (3) Evaluate case management approaches to the care of people who call the 999 ambulance service frequently in terms of: (i) Further emergency contacts (999, emergency department, emergency admissions to hospital) (ii) Effects on other services (iii) Adverse events (deaths, injuries, serious medical emergencies and police arrests) (iv) Costs of intervention and care (v) Patient experience of care. (4) Identify challenges and opportunities associated with using case management models, including features associated with success, and develop theories about how case management works in this population.

Methods and analysis

We will conduct a multisite mixed-methods evaluation of case management for people who use ambulance services frequently by using anonymised linked routine data outcomes in a ‘natural experiment’ cohort design, in four regional ambulance services. We will conduct interviews and focus groups with service users, commissioners and emergency and non-acute care providers. The planned start and end dates of the study are 1 April 2019 and 1 September 2022, respectively

Ethics and dissemination

The study received approval from the UK Health Research Authority (Confidentiality Advisory Group reference number: 19/CAG/0195; research ethics committee reference number: 19/WA/0216).

We will collate feedback from our Lived Experience Advisory Panel, the Frequent Caller National Network and Research Management Group for targeted dissemination activities.
Effects of patient-level risk factors, departmental allocation and seasonality on intrahospital patient transfer patterns: network analysis applied on a Norwegian single-centre data set
Authors: Zhang Chi , Eken Torsten , Jørgensen Silje Bakken , Thoresen Magne , Søvik Signe
Published: March 2022

Objectives

Describe patient transfer patterns within a large Norwegian hospital. Identify risk factors associated with a high number of transfers. Develop methods to monitor intrahospital patient flows to support capacity management and infection control.

Design

Retrospective observational study of linked clinical data from electronic health records.

Setting

Tertiary care university hospital in the Greater Oslo Region, Norway.

Participants

All adult (≥18 years old) admissions to the gastroenterology, gastrointestinal surgery, neurology and orthopaedics departments at Akershus University Hospital, June 2018 to May 2019.

Methods

Network analysis and graph theory. Poisson regression analysis.

Outcome measures

Primary outcome was network characteristics at the departmental level. We describe location-to-location transfers using unweighted, undirected networks for a full-year study period. Weekly networks reveal changes in network size, density and key categories of transfers over time. Secondary outcome was transfer trajectories at the individual patient level. We describe the distribution of transfer trajectories in the cohort and associate number of transfers with patient clinical characteristics.

Results

The cohort comprised 17 198 hospital stays. Network analysis demonstrated marked heterogeneity across departments and throughout the year. The orthopaedics department had the largest transfer network size and density and greatest temporal variation. More transfers occurred during weekdays than weekends. Summer holiday affected transfers of different types (Emergency department-Any location/Bed ward-Bed ward/To-From Technical wards) differently. Over 75% of transferred patients followed one of 20 common intrahospital trajectories, involving one to three transfers. Higher number of intrahospital transfers was associated with emergency admission (transfer rate ratio (RR)=1.827), non-prophylactic antibiotics (RR=1.108), surgical procedure (RR=2.939) and stay in intensive care unit or high-dependency unit (RR=2.098). Additionally, gastrosurgical (RR=1.211), orthopaedic (RR=1.295) and neurological (RR=1.114) patients had higher risk of many transfers than gastroenterology patients (all effects: p<0.001).

Conclusions

Network and transfer chain analysis applied on patient location data revealed logistic and clinical associations highly relevant for hospital capacity management and infection control.
Cost saving in primary versus tertiary level of reproductive health services in Sana’a, Yemen, 2013: a comparative cross-sectional study
Authors: Nassar Abdulkareem Ali Hussein , Raja’a Yahia Ahmed , Bahubaishi Najia Saleh
Published: March 2022

Objectives

To estimate the cost saving for utilisation of vaginal delivery (VD), antenatal care (ANC) and an intrauterine device (IUD) services at primary health level facilities (PHLF) instead of tertiary health level facilities (THLF) in Sana’a.

Design

A comparative cross-sectional study.

Setting

Eight PHLF in Sana’a governorate and three THLF in Sana’a city.

Participants

A total of 180 women aged (15–45 years) were enrolled equally from PHLF and THLF. Sixty women attended for each reproductive health service (VD, ANC and IUD services).

Primary and secondary outcome measures

The direct and indirect costs of services at PHLF and THLF, and the cost saving for utilisation of PHLF instead of THLF.

Results

The median of direct medical cost (DMC) of VD, ANC and IUD services were US$43.86, US$14.77 and US$9.07 at THLF compared with US$19.54, US$0.93 and US$11.17 at PHLF, respectively. The DMC difference of VD, ANC and IUD services between THLF and PHLF was US$24.32, US$13.84 and US$−2.1, respectively. Regarding the direct non-medical costs (DNMC), the median of VD, ANC and IUD services were US$43.05, US$19.07 and US$17.27 at THLF compared with US$13.96, US$0.00 and US$0.00 at PHLF, respectively. The DNMC difference of VD, ANC and IUD service between THLF and PHLF was US$29.09, US$18.07 and US$16.27, respectively. Moreover, the median of indirect cost (INDC) for VD, ANC and IUD services were US$23.93, US$9.49 and US$10.44 at THLF compared with US$7.90, US$1.59 and US$1.06 at PHLF, respectively. The INDC difference of VD, ANC and IUD service between THLF and PHLF was US$16.03, US$7.90 and US$9.38, respectively.

Conclusion

The study found the utilisation of VD, ANC and IUD services at PHLF instead of THLF is a considerable cost saving for families. Therefore, shifting the utilisation of services from THLF to PHLF reduces the financial burden affecting individuals, families and their productivity.
Minimum clinically important difference of the Social Functioning in Dementia Scale (SF-DEM): cross-sectional study and Delphi survey
Authors: Levene Tamara , Livingston Gill , Banerjee Sube , Sommerlad Andrew
Published: March 2022

Objectives

Good social functioning is important for people living with dementia and their families. The Social Functioning in Dementia Scale (SF-DEM) is a valid and reliable instrument measuring social functioning in dementia. However the minimum clinically important difference (MCID) has not yet been derived for SF-DEM. This study aims to define the MCID for the SF-DEM.

Design

We used triangulation, incorporating data from a cross-sectional study to calculate the MCID using distribution-based and anchor-based methods, and a Delphi survey.

Setting and participants

The cross-sectional survey comprised 299 family carers of people with dementia. Twenty dementia experts (researchers, clinicians, family carers) rated whether changes on clinical vignettes represented a meaningful change in the Delphi survey.

Primary outcome measures

We calculated the distribution-based MCID as 0.5 of an SD for each of the three SF-DEM domains (1—spending time with others, 2—communicating with others, 3—sensitivity to others). We used the carers’ rating of social functioning to calculate the anchor-based MCID. For the Delphi survey, we defined consensus as ≥75% agreement. Where there was lack of consensus, experts were asked to complete a further survey round.

Results

We found that 0.5 SD of SF-DEM was 1.9 points, 2.2 and 1.4 points in domains 1, 2 and 3, respectively. Using the anchoring analysis, the MCIDs were 1.7 points, 1.7 points, and 0.9 points in domains 1, 2 and 3, respectively. The Delphi method required two rounds. In the second round, a consensus was reached that a 2-point change was considered significant in all three domains, but no consensus was reached on a 1-point change.

Conclusions

By triangulating all three methods, the SF-DEM’s MCIDs were 1.9, 2.0 and 1.4 points for domains 1, 2 and 3, respectively. For individuals, these values should be rounded to a 2-point change for each domain.
Factors affecting the UK junior doctor workforce retention crisis: an integrative review
Authors: Lock Florence Katie , Carrieri Daniele
Published: March 2022

Objectives

To determine the factors contributing to the junior doctor workforce retention crisis in the UK using evidence collected directly from junior doctors, and to develop recommendations for changes to address the issue.

Design

Integrative review.

Data sources

Searches were conducted on Ovid Medline and HMIC to locate evidence published between January 2016 and April 2021. This was supplemented by publications from relevant national organisations.

Eligibility criteria

English-language papers relating to UK junior doctor retention, well-being or satisfaction which contained data collected directly from junior doctors were included. Papers focusing solely on the pandemic, factors specific to one medical specialty, evaluation of interventions, or numerical data with no evidence relating to causation were excluded. Review papers were excluded.

Data extraction and synthesis

Data were extracted and coded on NVivo by FKL, then thematic analysis was conducted.

Results

47 papers were included, consisting of academic (qualitative, quantitative, mixed and commentary) and grey literature. Key themes identified were working conditions, support and relationships, and learning and development, with an overarching theme of lack of flexibility. The outcomes of these factors are doctors not feeling valued, lacking autonomy, having a poor work–life balance, and providing compromised patient care. This results in need for a break from medical training.

Conclusion

This review builds on findings of related literature regarding working environments, isolation, stigma, and desire for autonomy, and highlights additional issues around learning and training, flexibility, feeling valued, and patient care. It goes on to present recommendations for tackling poor retention of UK junior doctors, highlighting that the complex problem requires evidence-based solutions and a bottom-up approach in which junior doctors are regarded as core stakeholders during the planning of interventions.
TU$$^2$$FRG: a scalable approach for truncated unity functional renormalization group in generic fermionic models
Authors: Profe Jonas B. , Kennes Dante M.
Published: March 2022

Describing the emergence of phases of condensed matter is one of the central challenges in physics. For this purpose many numerical and analytical methods have been developed, each with their own strengths and limitations. The functional renormalization group is one of these methods bridging between efficiency and accuracy. In this paper we derive a new truncated unity (TU) approach unifying real- and momentum space TU, called TU$$^2$$

2

FRG. This formalism significantly improves the scaling compared to conventional momentum (TU)FRG when applied to large unit-cell models and models where the translational symmetry is broken.

Graphic abstract
Verification of Distributed Quantum Programs
Authors: Feng Yuan , Li Sanjiang , Ying Mingsheng
Published: March 2022

Distributed quantum systems and especially the Quantum Internet have the ever-increasing potential to fully demonstrate the power of quantum computation. This is particularly true given that developing a general-purpose quantum computer is much more difficult than connecting many small quantum devices. One major challenge of implementing distributed quantum systems is programming them and verifying their correctness. In this paper, we propose a CSP-like distributed programming language to facilitate the specification and verification of such systems. After presenting its operational and denotational semantics, we develop a Hoare-style logic for distributed quantum programs and establish its soundness and (relative) completeness with respect to both partial and total correctness. The effectiveness of the logic is demonstrated by its applications in the verification of quantum teleportation and local implementation of non-local CNOT gates, two important algorithms widely used in distributed quantum systems.
On the Feasibility of Securing Vehicle-Pavement Interaction
Authors: Sun Wei , Srinivasan Kannan
Published: March 2022

Road surface information (e.g., smooth road or bumpy road with potholes or bumps) is important for safe driving (i.e., it's necessary to be aware of the road surface conditions during driving). However, the high-cost sensor (e.g., LiDAR and camera) based road surface sensing approaches cannot work properly in inclement weather conditions (e.g., fogging and snowing) due to the line-of-sight requirement. The low-cost and ubiquitous smartphone-based road surface sensing approach is not reliable and safe to use, since it relies on the vibration of the vehicle body to sense the road surface (i.e., the vehicle's tires need to touch the bumps on the road surface). Can we automate the contact-free road surface sensing with low-cost sensors for safe driving without requiring the vehicle's tires to touch the bumps on the road surface?

In this paper, we propose Tago, a system that can achieve contact-free road surface sensing with commodity passive RFID tags. Instead of deploying RFID tags or readers along the road or lamp post (i.e., infrastructure-based deployment), we deploy the reader inside of the vehicle and attach the tag and the reader's antenna at the front end of the vehicle like the vehicle's headlights (i.e., infrastructure-free deployment). However, there is a great challenge to obtain the clean reflection from the road surface, since the reflection may be drown in the backscattered signals due to multipath effect. Moreover, it is not reliable to use the composite signals received at the reader to sense the road surface conditions. Therefore, we first comprehensively analyse the variation of composite signals received at the reader. Then, we propose a signal cancellation approach to extract the clean reflections from the road surface, such that we can accurately sense the road surface conditions for safe driving. Our experiments with different vehicles (e.g., Honda Civic Frankenfish, Folsom, Flutter and CR-V Warner) driven on different roadways (e.g., urban and residential area) show that Tago can effectively sense the road surface information.
Wheels Know Why You Travel
Authors: Liao Chengwu , Chen Chao , Guo Suiming , Wang Zhu , Liu Yaxiao , Xu Ke , Zhang Daqing
Published: March 2022

Trip purpose - i.e., why people travel - is an important yet challenging research topic in travel behavior analysis. Generally, the key to this problem is understanding the activity semantics from trip contexts. However, most existing methods rely on passengers' sensitive information - e.g., daily travel log or home address from surveys - to achieve accurate results, and could thus be hardly applied in real-life scenarios. In this paper, we aim to predict the passenger's trip purpose in the scenarios of door-to-door ride services (e.g., taxi trips) by only using the vehicle's GPS trajectory on roads, for which "wheels" is used as a metaphor. Specifically, we propose a novel dual-attention graph embedding model based on the vehicle's trajectory and public POI check-in data. Firstly, both data are aggregated to augment the activity semantics of trip contexts, including the spatiotemporal context and POI contexts at the origin and destination, which are important clues. Based on that, graph attention networks and soft-attention are employed to model the dependency of different contexts on the trip purpose, so as to obtain the trip's comprehensive activity semantics for the final prediction. Extensive experiments are conducted based on the large-scale labeled datasets in Beijing. The prediction results show a considerable improvement compared to state-of-the-arts. A case study demonstrates the feasibility of our study.
Ultra Low-Latency Backscatter for Fast-Moving Location Tracking
Authors: Wang Jingxian , Ranganathan Vaishnavi , Lester Jonathan , Kumar Swarun
Published: March 2022

This paper explores building an ultra-low latency and high-accuracy location tracking solution using battery-free tags. While there is rich prior work on location tracking with battery-free RFID tags and backscatter devices, these systems typically face tradeoffs with accuracy, power consumption, and latency. Such limitations make these existing solutions unsuitable for emerging applications like industrial augmented reality which requires tracking fast-moving machinery; monitoring indoor sports activities that require real-time tracking of fast-moving objects with high precision and under stringent latency constraints.

We propose and demonstrate FastLoc, a precision tracking system that locates tiny, battery-free analog backscatter tags at sub-millisecond latency and sub-centimeter accuracy. FastLoc is a hybrid system that simultaneously uses RF and optical signals to track tiny tags that can be attached to everyday objects. FastLoc leverages the RF channel responses from tags for estimating the coarse region where the tags may be located. It simultaneously uses the sensed optical information modulated on the backscatter signals to enable fine-grained location estimation within the coarse region. To achieve this, we design and fabricate a custom analog tag that consumes less than 150 uW and instantaneously converts incident optical signals to one-shot wideband harmonic RF responses at nanosecond latency. We then develop a static high-density distributed-frequency structured light pattern that can localize tags in the area of interest at a sub-centimeter accuracy and microsecond-scale latency. A detailed experimental evaluation of FastLoc shows a median accuracy of 0.7 cm in tag localization with a 0.51 ms effective localization latency.
InDepth
Authors: Zhang Yunfan , Scargill Tim , Vaishnav Ashutosh , Premsankar Gopika , Di Francesco Mario , Gorlatova Maria
Published: March 2022

Mobile Augmented Reality (AR) demands realistic rendering of virtual content that seamlessly blends into the physical environment. For this reason, AR headsets and recent smartphones are increasingly equipped with Time-of-Flight (ToF) cameras to acquire depth maps of a scene in real-time. ToF cameras are cheap and fast, however, they suffer from several issues that affect the quality of depth data, ultimately hampering their use for mobile AR. Among them, scale errors of virtual objects - appearing much bigger or smaller than what they should be - are particularly noticeable and unpleasant. This article specifically addresses these challenges by proposing InDepth, a real-time depth inpainting system based on edge computing. InDepth employs a novel deep neural network (DNN) architecture to improve the accuracy of depth maps obtained from ToF cameras. The DNN fills holes and corrects artifacts in the depth maps with high accuracy and eight times lower inference time than the state of the art. An extensive performance evaluation in real settings shows that InDepth reduces the mean absolute error by a factor of four with respect to ARCore DepthLab. Finally, a user study reveals that InDepth is effective in rendering correctly-scaled virtual objects, outperforming DepthLab.
Development of a Modular Operando Cell for X-ray Imaging of Strongly Absorbing Silver-Based Gas Diffusion Electrodes
Authors: Hoffmann Hendrik , Paulisch Melanie Cornelia , Gebhard Marcus , Osiewacz Jens , Kutter Maximilian , Hilger André , Arlt Tobias , Kardjilov Nikolay , Ellendorff Barbara , Beckmann Felix , Markötter Henning , Luik Marius , Turek Thomas , Manke Ingo , Roth Christina
Published: March 2022

Metal-based gas diffusion electrodes are utilized in chlor-alkali electrolysis or electrochemical reduction of carbon dioxide, allowing the reaction to proceed at high current densities. In contrast to planar electrodes and predominantly 2D designs, the industrially required high current densities can be achieved by intense contact between the gas and liquid phase with the catalytically active surfaces. An essential asset for the knowledge-based design of tailored electrodes is therefore in-depth information on electrolyte distribution and intrusion into the electrode’s porous structure. Lab-based and synchrotron radiography allow for monitoring this process operando. Herein, we describe the development of a cell design that can be modularly adapted and successfully used to monitor both the oxygen reduction reaction and the electrochemical reduction of CO2 as exemplary and currently very relevant examples of gas-liquid reactions by only minor modifications to the cell set-up. With the reported cell design, we were able to observe the electrolyte distribution within the gas diffusion electrode during cell operation in realistic conditions.
The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer
Authors: Huang Ying-Tzu , Tsai Yi-Shan , Lin Peng-Chan , Yeh Yu-Min , Hsu Ya-Ting , Wu Pei-Ying , Shen Meng-Ru
Published: March 2022

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.
Brachybacterium epidermidis Sp. Nov., a Novel Bacterial Species Isolated from the Back of the Right Hand, in a 67-Year-Old Healthy Woman
Authors: Boxberger Manon , Magnien Sibylle , Antezack Angéline , Rolland Clara , Makoa Marine , La-Scola Bernard , Cassir Nadim
Published: March 2022

Knowledge on human skin microbiota composition has been expanding in recent years. Its role in human health and disease represents an active area of investigation. As part of our culturomics project that consists of exploring the human microbiota by isolating bacteria through innovative culture-dependent methods, we isolated a new bacterial strain from the back of the right hand, in a 67-year-old healthy woman. Here, we characterize the strain Marseille-Q2903 by the taxonogenomic approach. Marseille-Q2903 exhibits a 99.5% 16S rRNA sequence similarity with Brachybacterium murisT but with only 92% of coverage. The closest species based on a 100% coverage of the 16S sequence is Brachybacterium timonenseT with an identity similarity of 97.63%. Furthermore, digital DNA-DNA hybridization reveals a maximum identity similarity of only 31.5% and an OrthoANI parameter provided a value of 86.95% between Marseille-Q2903 and Brachybacterium murisT. Marseille-Q2903 is a yellowish-pigmented, Gram-positive, coccoid shaped, and facultative aerobic bacterium, and belonging to the Dermabacteraceae family. The major fatty acids detected are 12-methyl-tetradecanoic acid (69%), 14-methyl-hexadecanoic acid (16%), and 14-methyl-pentadecanoic acid (7%). Marseille-Q2903 genome size is of 3,073,790 bp, with a 70.43% G + C content. Taken altogether, these results confirm the status of this strain as a new member of the Brachybacterium genus for which the name of Brachybacterium epidermidis sp. strain Marseille-Q2903T is proposed (=CSURQ2903T = CECT30363).
Bank Green Credit Risk Assessment and Management by Mobile Computing and Machine Learning Neural Network under the Efficient Wireless Communication
Authors: Feng Yuan
Published: March 2022

The study is aimed at assessing and managing the green credit risk of banks, reduces the systemic risk in the financial industry, and improves the efficiency of the use of bank funds. With the development and evolution of efficient wireless data communication and transmission technology, the study combines theoretical and empirical green credit analysis to analyze listed companies in different industries quantitatively. The index system of credit risk assessment is established through wireless data transmission technology combined with mobile computing and machine learning neural networks. A back-propagation neural network (BPNN) model is confirmed by principal component analysis and factor analysis, and the performance of the model is verified with example data. The results show that the BPNN-based credit risk assessment model can provide 95% accuracy. In addition, 99% of the sample companies have low risk and no green credit risk. However, most companies in the coal industry are at greater risk. Overall, medium and high-risk companies accounted for 11.5%. Compared with other state-of-the-art models, the machine learning neural network adopted here has better data fitting and prediction accuracy, higher learning efficiency, and higher accuracy. The model established inefficient wireless communication is suitable for bank credit risk assessment and has good reference value and practical significance for bank credit risk assessment and management in different industries.
Nonsyndromic Generalized Radiculomegaly of Permanent Dentition: A Rare Case Report
Authors: Alhussain Mohammed , Almosa Naif , Alnofaie Hourya
Published: March 2022

Introduction. Radiculomegaly (marked elongation of dental roots) is a distinct dental abnormality with a major clinical significance that is closely related to oculofaciocardiodental syndrome (OFCD). Since OFCD syndrome was first identified in 1996, only a few cases of nonsyndromic/nonfamilial radiculomegaly have been reported. We report a new nonsyndromic/nonfamilial radiculomegaly case and the association of OFCD syndrome with the dental challenges. Case Presentation. 18-year-old medically free male presented to the screening dental clinics complaining of teeth malposition. Panoramic radiograph incidentally revealed extremely long and wide roots of almost all dentition. Apical radiographs and cone-beam computed tomography (CBCT) were taken to confirm this anomaly and to rule out any artifact. The images confirmed the excessive length of the roots. A cephalometric X-ray was performed on the patient to rule out any dentofacial deformity. History and physical examinations were negative for ocular or cardiac anomalies. Due to lack of evidence of physical signs and symptoms consistent with OFCD syndrome, genetic testing was not performed. Conclusion. Dentists need to be informed of the clinical and radiographic features of different dental anomalies, especially radiculomegaly, as it is considered one of the significant features of OFCD syndrome. Challenges related to radiculomegaly face dentists while the current literature has not yet provided a solid baseline for managing such patients. The challenges include repairing the root canals, extracting and/or moving the teeth orthodontically. Early diagnosis of the syndrome is crucial to prevent dental challenges and provide the best dental care services.
Investigation of Multibeam Forming Algorithm for Cylindrical Conformal Array Antenna
Authors: Zhao Ke , Liu Lizhe , Mei Lirong
Published: March 2022

A multibeam forming algorithm based on cylindrical conformal array antenna is proposed in this paper. A comprehensively optimized algorithm of Taylor weights is used to reduce sidelobes. Multiple beams pointed at different azimuth angles are formed based on a reconfigurable overlapping array division method of subarrays, therefore each beam can get the maximum excitation of subarrays and ensure the pointing accuracy of the main lobe. Finally the cylindrical array antenna realizes the multibeam scanning and area coverage. The experimental results show that the sidelobe level after comprehensive optimization is 7.37 dB lower than that without optimization and 1.71 dB lower than that of traditional optimization when a single beam is formed. The comprehensive optimization can eliminate the grating lobe between two beams and reduce the sidelobe to -15 ∼ -20 dB when the two beams are 30°. The left sidelobe is reduced by 2.75 dB and the right sidelobe is reduced by 1.69 dB after comprehensive optimization when the two beams are separated by 40°.
Clinical Performance Study of a New Fully Automated Red Blood Cell Permeability Fragility Analyzer
Authors: Zheng Shiwen , Li Qiuyan , Ou Tong , Li Yuqing , Wu Song
Published: March 2022

In order to verify the applicability of the erythrocyte fragility test (EFT) carried out by the new fully automatic erythrocyte permeability fragility analyzer RA-800 for thalassemia screening, a total of 100 cases of suspected thalassemia patients who underwent pregnancy examinations at Luohu District People’s Hospital are included. The results of a new automatic erythrocyte permeability fragility analyzer RA-800 are compared with the results of the detection system composing of the KOFA erythrocyte fragility test kit currently used in clinical laboratories. The diagnosis confirmed by genetic testing is used as the gold standard to evaluate the applicability of RA-800. The sensitivity, specificity, and accuracy of the new automatic erythrocyte permeability fragility analyzer RA-800 screening for thalassemia were 66.67%, 92.86%, and 85.00%. The KOFA direct colorimetries are 76.67%, 81.43%, and 80.00%. The kappa value for the screening of thalassemia was 0.558, which concludes that the consistency was moderate. The ROC curve indicates that both two methods had diagnostic significance for the diagnostic value of thalassemia. The new automatic erythrocyte permeability fragility analyzer RA-800 is suitable for thalassemia screening, and the performance indexes meet the clinical requirements.
Bone Mineral Density and Trabecular Bone Score in Predicting Vertebral Fractures in Male Employees of the Electricity Generating Authority of Thailand
Authors: Sakulpisuti Chaninart , Sritara Chanika , Kositwattanarerk Arpakorn , Fuangfa Praman , Suppasilp Chaiyawat , Vathesatogkit Prin , Matchariyakul Dujrudee , Ongphiphadhanakul Boonsong , Sritara Piyamitr
Published: March 2022

Purpose. Osteoporotic VF is frequently asymptomatic and affects not only women but also men. Identifying patients at risk is essential for early management and prevention. BMD and the TBS are measurements of bone strength and trabecular microarchitecture, respectively. Their role in VF prediction in men is less well-studied. We determined the BMD and TBS predictive ability for osteoporotic VF in men. Methods. A total of 115 male participants of the Electricity Generating Authority of Thailand (EGAT) cohorts without a history of VF who completed the baseline BMD and TBS measurements in 2012 and a thoracolumbar spine radiograph in 2017 were recruited. The VF was assessed using the Genant semiquantitative method. Logistic regression analysis was performed to identify factors associated with the fracture. The area under the receiving operator curve (AUC) was analyzed to define VF predictive ability. Results. Forty subjects (34.78%) had VFs. The unadjusted relative risks (95% confidence interval) for VF for one standard deviation decrease in the TBS and low TBS were 1.319 (1.157–1.506) and 2.347 (1.496–3.682), respectively, and remained significant after BMD and age adjustment. For VF prediction, combined models had a greater AUC than models predicted from a single variable. The use of low TBS, femoral neck BMD, and age provided the best AUC (0.693). Conclusion. BMD and the TBS could predict osteoporotic VF in male EGAT employees. The use of both BMD and the TBS in the VF prediction process improved predictive ability.
[Retracted] Personalized Liver Cancer Risk Prediction Using Big Data Analytics Techniques with Image Processing Segmentation
Authors: Jain Anurag , Nadeem Ahmed , Majdi Altoukhi Huda , Jamal Sajjad Shaukat , Atiglah Henry kwame , Elwahsh Haitham
Published: March 2022

A technology known as data analytics is a massively parallel processing approach that may be used to forecast a wide range of illnesses. Many scientific research methodologies have the problem of requiring a significant amount of time and processing effort, which has a negative impact on the overall performance of the system. Virtual screening (VS) is a drug discovery approach that makes use of big data techniques and is based on the concept of virtual screening. This approach is utilised for the development of novel drugs, and it is a time‐consuming procedure that includes the docking of ligands in several databases in order to build the protein receptor. The proposed work is divided into two modules: image processing‐based cancer segmentation and analysis using extracted features using big data analytics, and cancer segmentation and analysis using extracted features using image processing. This statistical approach is critical in the development of new drugs for the treatment of liver cancer. Machine learning methods were utilised in the prediction of liver cancer, including the MapReduce and Mahout algorithms, which were used to prefilter the set of ligand filaments before they were used in the prediction of liver cancer. This work proposes the SMRF algorithm, an improved scalable random forest algorithm built on the MapReduce foundation. Using a computer cluster or cloud computing environment, this new method categorises massive datasets. With SMRF, small amounts of data are processed and optimised over a large number of computers, allowing for the highest possible throughput. When compared to the standard random forest method, the testing findings reveal that the SMRF algorithm exhibits the same level of accuracy deterioration but exhibits superior overall performance. The accuracy range of 80 percent using the performance metrics analysis is included in the actual formulation of the medicine that is utilised for liver cancer prediction in this study.
Network Resource Allocation Strategy Based on UAV Cooperative Edge Computing
Authors: Wang Shuo , Kong Ning
Published: March 2022

Aiming at the problem that fixed mobile edge computing (MEC) server is difficult to meet the needs of mobile users and temporary computing services, this study proposes a network resource allocation strategy based on unmanned aerial vehicle (UAV) cooperative edge computing. First, a UAV-aided MEC scene is designed, and a single UAV with an MEC server is used to provide auxiliary computing services for ground multiusers. Then, an optimization model aiming at total system delay is constructed by considering the system communication model and calculation model. Finally, Deep Q-Network is used to solve the optimization problem to obtain the best resource allocation scheme. Based on the experimental platform, the proposed strategy is demonstrated and analyzed. The results show that when the number of user equipment is 40, the total delay is about 33s, which is 35.29%, 31.25%, and 15.38% lower than other comparison strategies and effectively reduces the computing delay of users.
Research on Data Analysis Network of TCM Tongue Diagnosis Based on Deep Learning Technology
Authors: Li Zongrun , Ren Xiujuan , Xiao Lin , Qi Jing , Fu Tianli , Li Weihong
Published: March 2022

The aim of the study is to build a tongue image intelligent analysis “end-to-end” deep learning network based on a tongue diagnosis image of traditional Chinese medicine. The tongue target region in the original image was segmented by the UNet tongue segmentation model at the front end of the network. After segmentation, the feature vector of the tongue target region was extracted by the ResNet network, and then the blood pressure on the day of shooting was fused with the feature vector extracted by the ResNet network through the convolution operation method to complete the extraction of two groups of data of tongue feature and fusion feature. Based on analyzing the data of blood pressure, tongue image, and their fusion at the end of the network, four regression analysis methods were used to predict the stage mean value. After training, the model is tested with the test set data, and the test results are evaluated with mean absolute error (MAE). The prediction error of the model based on the fusion data of tongue image and blood pressure on the day of shooting was lower than that of the other two data modes. The UNet tongue segmentation model combined with the ResNet network can realize the automatic extraction of tongue image features. The extracted features combined with machine learning modeling can be used to explore the complex hierarchical mathematical association between tongue image and clinical data. The experimental results show that the multimodal data fusion method is an important way to mine the clinical value of the TCM tongue image.
A Music Genre Classification Method Based on Deep Learning
Authors: He Qi
Published: March 2022

Digital music resources have exploded in popularity since the dawn of the digital music age. The music genre is an important classification to use when describing music. The function of music labels in discovering and separating digital music resources is crucial. In the face of a huge music database, relying on manual annotation to classify will consume a lot of cost and time, which cannot meet the needs of the times. The following are the paper’s primary research findings and innovations: to better describe the music, this article will be divided into multiple local musical instrument digital interface (MIDI) music passages, playing style close by analyzing passages, passages feature extracting, and feature sequence of passages. Extraction of note feature matrix, extraction of topic and segment division based on note feature matrix, research and extraction of effective features based on segment theme, and composition of feature sequence are all part of the process. Because of the shallow structure of standard classification methods, it is difficult for classifiers to learn temporal and semantic information about music. This research investigates recurrent neural networks (RNN) and attention using the distinctive sequence of input MIDI segments. To create data sets and conduct music categorization tests, collect 1920 MIDI files with genre labels from the Internet. The method for music classification is validated when it is combined with the experimental accuracy of equal length segment categorization.
Puppets Pulling Back: Drawing Out Wael Shawky's Histories
Authors: Taylor Damian
Published: March 2022

Recounting the first four Crusades using an evolving cast of marionettes, Wael Shawky's Cabaret Crusades trilogy is widely interpreted as exploring the construction of history. Close attention to the puppet performances, however, suggests that far more is at play than literalizing how the players on life's stage are caught up in history and swept along by external forces. For example, the identity of puppet and puppeteer is frequently destabilized, an instability achieved as much through the possibilities of video as within the material exchanges of puppet and puppeteer. The essay investigates several questions: What questions are posed regarding the interactions of human agency and matter when the marionette becomes the marionettist through the mediation of video? How might this push up against the more obvious histories that Shawky interrogates? And, ultimately, how does this revised form of representation transform understandings of that which the puppet has long represented—the very condition of the human?
Analysing Russian Reaction to 2021 U.S. Capitol Riots
Authors: Slutskiy Pavel , Gavra Dmitrii
Published: March 2022

On January 6, 2021, the United States Capitol in Washington, D.C. was attacked by a mob of supporters of President Donald Trump. More than 70 countries and international organisations expressed their concerns over the 2021 United States Capitol attack and condemned the violence. While governments around the world have expressed outrage and sadness over rioting that engulfed the U.S. Capitol, some media (for example, VOA) reported that ‘Russia took a different view -- namely, vindication with a bit of gloating’ under the headline ‘As US Reels From Capitol Violence, Russia Enjoys the Show’. What was Russia’s reaction to the events in Washington? Russian high-ranking politicians responses to the U.S. Capitol rioters were widely quoted in English-speaking media. But the angle offered for foreign audiences did not always coincide with the media narrative developed for domestic consumption. This paper examines media interpretation of the U.S. riots which was offered by the Russian media for Russian-speaking audiences. The paper follows the discourse of pro-Kremlin media during the period of 1 month after the protests began, analysing discursive frames which represented different interpretations of the events, particularly within the context of comparing the suppression of opposition protests in Russia with the prosecution of the U.S. Capitol rioters.
Multi-objective optimization of lateral stability strength of transversely loaded laminated composite beams with varying I-section
Authors: Soltani M , Abolghasemian R , Shafieirad M , Abbasi Z , Mehra AH Amiri , Ghasemi AR
Published: March 2022

In this research, the lateral buckling analysis and layup optimization of the laminated composite of web and flanges tapered thin-walled I-beams based on maximizing lateral-torsional stability strength and minimizing mass/cost of the structure are investigated. The classical lamination theory and Vlasov’s model for thin-walled cross-section are adopted to establish the total potential energy for thin-walled symmetric balanced laminated beams with varying I-section. By implementing the Ritz method, an explicit formulation for the lateral-torsional buckling load of a double-tapered beam subjected to transverse loading is then derived in terms of the load height parameter and stiffness quantities. Subsequently, the optimal arrangements of layer sequences are obtained using the non-dominated sorting genetic algorithm (NSGA-II) and properly defined objective function. The critical factors of fitness function as lateral buckling strength and the mass of the structure with critical limitations as ply angle, number of layers for the web and flanges, and the thickness of all section walls are considered in this study. Finally, the optimal layer arrangement for the web and flanges are separately determined and discussed. The results show that the presented optimization procedure and layups patterns lead to increasing the lateral-torsional buckling capacity about 52% compared to the conventional angle-ply and unidirectional layups for the web and flanges, respectively.
Bridging home and school in cross-border education: The role of intermediary spaces in the in/exclusion of Mainland Chinese students and their families in Hong Kong
Authors: Leung Maggi WH , Waters Johanna L
Published: March 2022

Over the last two decades the Hong Kong government has made considerable investments to develop the city into a regional education hub, with ‘diversification’ as a key aim. The vision is, however, delinked from the tens of thousands of young children residing in Shenzhen who commute to Hong Kong for school daily. These children embody differences that are considered undesired and their social exclusion has been widely reported. Taking a spatial perspective, this paper deepens our understanding of the in/exclusion processes impacting these children. Drawing on our policy analysis, interviews, observations in physical spaces and digital media, this paper analyses the role that intermediary spaces play in (re)producing differences and social relationships. Specifically, we examine the power geometries of the children’s school journey and school-related digital space, which are arenas where social differences are played out and in/exclusion is practiced and negotiated. We analyse the network of state and non-state actors at work in these intermediary spaces, showing the complex ways in which separation and integration, exclusion and inclusion intersect and constitute each other mutually. Our paper also gives some first insights into the impact of COVID-19 on the school children within this education mobility field.
The Moderating Role of Incarcerated Mothers’ Psychosocial Functioning on the Association Between Maternal Sensitivity and Their Co-Residing Toddlers’ Attachment
Authors: İşcanoğlu Zülal
Published: March 2022

The role of incarcerated mothers’ caregiving quality on their co-residing children’s development may not be fully understood without considering mothers’ psychosocial functioning. The current study aims to investigate the moderating role of incarcerated mothers’ psychosocial functioning (i.e., relationship with their outside children’s caregivers, psychological symptoms, and social support) on the associations between maternal sensitivity and their co-residing children’s attachment. The study sample consisted of 68 mothers and their 12- to 43-month-old co-residing children. Maternal sensitivity and child’s attachment were assessed based on a semi-structured mother–child interaction observation. Mothers reported their psychosocial functioning indicators that were determined based on the common distress factors that these mothers experience. Findings suggest that for the mothers who have the lowest relationship quality with their outside children’s caregivers, maternal sensitivity negatively predicts children’s disorganized attachment. Moreover, maternal sensitivity predicted children’s secure attachment positively and anxious attachment negatively only when maternal psychological symptoms were higher.