bluuebunny/crossref_metadata_2025 huggingface dataset:
Approaches to prioritising primary health research: a scoping review
Authors: Fadlallah Racha , Daher Najla , El-Harakeh Amena , Hammam Rima , Brax Hneine , Bou Karroum Lama , Lopes Luciane Cruz , Arnous Ghida , Kassamany Inas , Baltayan Stephanie , Harb Aya , Lotfi Tamara , El-Jardali Fadi , Akl Elie A
Published: May 2022

Objective

To systematically identify and describe approaches to prioritise primary research topics in any health-related area.

Methods

We searched Medline and CINAHL databases and Google Scholar. Teams of two reviewers screened studies and extracted data in duplicate and independently. We synthesised the information across the included approaches by developing common categorisation of relevant concepts.

Results

Of 44 392 citations, 30 articles reporting on 25 approaches were included, addressing the following fields: health in general (n=9), clinical (n=10), health policy and systems (n=10), public health (n=6) and health service research (n=5) (10 addressed more than 1 field). The approaches proposed the following aspects to be addressed in the prioritisation process: situation analysis/ environmental scan, methods for generation of initial list of topics, use of prioritisation criteria, stakeholder engagement, ranking process/technique, dissemination and implementation, revision and appeal mechanism, and monitoring and evaluation. Twenty-two approaches proposed involving stakeholders in the priority setting process. The most commonly proposed stakeholder category was ‘researchers/academia’ (n=17, 77%) followed by ‘healthcare providers’ (n=16, 73%). Fifteen of the approaches proposed a list of criteria for determining research priorities. We developed a common framework of 28 prioritisation criteria clustered into nine domains. The criterion most frequently mentioned by the identified approaches was ‘health burden’ (n=12, 80%), followed by ‘availability of resources’ (n=11, 73%).

Conclusion

We identified and described 25 prioritisation approaches for primary research topics in any health-related area. Findings highlight the need for greater participation of potential users (eg, policy-makers and the general public) and incorporation of equity as part of the prioritisation process. Findings can guide the work of researchers, policy-makers and funders seeking to conduct or fund primary health research. More importantly, the findings should be used to enhance a more coordinated approach to prioritising health research to inform decision making at all levels.
Oncological outcome of single-port insufflation endoscopic nipple-sparing mastectomy versus open mastectomy in early breast cancer patients: a study protocol for a randomised controlled trial
Authors: Wang Zi-han , Ding Guoqian , Wu Shanshan , Song Jian-ning , Ge Zhi-cheng , Zhang Huiming , Yuan Zhu , Gao Yin-guang , Gang Tian-ran , Zhang Zhongtao , Qu Xiang
Published: May 2022

Introduction

Breast cancer is the most prevalent cancer and the leading cause of cancer-related death in women. Conventional open mastectomy (C-OM) is one of the most common procedures for breast cancer, which involves the removal of the nipple-areola complex and a large proportion of the breast skin, leading to poor cosmetic effect and restriction of upper extremity function. Single-port insufflation endoscopic nipple-sparing mastectomy (SIE-NSM) could conceal the incision along the wrinkles in the axilla, preserve all the breast skin and nipple-areola complex and provide a better cosmetic outcome and quality of life. This trial aims to investigate the oncological safety between SIE-NSM and C-OM in early breast cancer patients.

Methods and analysis

This is a single centre, non-blinded, randomised controlled trial (RCT) and will be conducted at Beijing Friendship Hospital. Patients will be enrolled in the inpatient ward. Breast surgeons will notify patients who meet the inclusion and exclusion criteria with the instruction of this RCT. Patients will be randomly assigned to C-OM or SIE-NSM with a 3:1 allocation as per a computer-generated randomisation schedule. Patients will be followed-up for 12 months for analysing surgical outcomes. The primary outcome is the local recurrence rate at a 12-month follow-up. The secondary outcome is the distant metastasis rate, cosmetic satisfaction score and psychosocial well-being score after a 12-month follow-up. To ensure the accuracy of the cosmetic satisfaction score and psychosocial well-being score, the standard scale, Breast-Q score, will be applied.

Ethics and dissemination

This study will be conducted according to the medical ethics committee of the Beijing Friendship Hospital and according to the principles of the Declaration of Helsinki. All patients will receive clear instruction of their disease and treatment plan. Informed consent will be obtained from all patients when they agree to comply with our research plan. The results will be disseminated at academic presentations and publications in peer-reviewed journals. The raw data will be confidentially stored in our electronic data capture database. Data will not be shared unless an appropriate data request is submitted after the trial completion and peer-review journal publication.

Trial registration number

NCT04461847.
Chinese Neonatal Network: a national protocol for collaborative research and quality improvement in neonatal care
Authors: Hei Mingyan , Li Xiaoying , Shi Yuan , Cao Yun , Sun Jianhua , Wu Hui , Jiang Siyuan , Ma Xiaolu , Wang Yanchen , Sun Huiqing , Zhang Huayan , Du Li-Zhong , Zhou Wenhao , Lee Shoo K , Chen Chao
Published: May 2022

Introduction

The objective of the Chinese Neonatal Network (CHNN) is to provide a platform for collaborative research, outcomes evaluation and quality improvement for preterm infants with gestational age less than 32 weeks in China. The CHNN is the first national neonatal network and has the largest geographically representative cohort from neonatal intensive care units (NICUs) in China.

Methods and analysis

Individual-level data from participating NICUs will be collected using a unique database developed by the CHNN on an ongoing basis from January 2019. Data will be prospectively collected from all infants <32 weeks gestation or <1500 g birth weight at 58 participating NICUs. Infant outcomes and inter-institutional variations in outcomes will be examined and used to inform quality improvement measures aimed at improving outcomes. Information about NICU environmental and human resource factors and processes of neonatal care will also be collected and analysed for association with outcomes. Clinical studies, including randomised controlled trials will be conducted using the CHNN data platform.

Ethics and dissemination

This study was approved by the ethics review board of Children’s Hospital of Fudan University, which was recognised by all participating hospitals. Waiver of consent were granted at all sites. Only non-identifiable patient level data will be transmitted and only aggregate data will be reported in CHNN reports and publications.
Predictors for the prognosis and recurrence of ischaemic stroke among young Chinese patients: a cohort study
Authors: Wang Lijuan , Ge Jiaojiao , Chen Ying , Liu Ying , Li Cong , Dong Yanan , Liu Li , Xing Yingqi
Published: May 2022

Objective

To identify predictors of young Chinese patients with ischaemic stroke outcome and recurrence of stroke.

Design

Through considered the risk factors, neuroimaging findings, distribution, vascular stenosis, and the post-stroke treatment of young Chinese patients with ischemic stroke.

Setting

The First Hospital of Jilin University.

Participants

We enrolled 579 patients (median age 45 years, range 15–49, men 81.0%) treated for the first occurrence of ischaemic stroke between January 2014 and December 2017.

Main outcome measures

We assessed stroke outcome based on the modified Rankin Scale (mRS) scores and recurrence of cerebrovascular events at 12 months. Multivariate logistic regression was used to identify the independent predictors of unfavourable outcomes (mRS score ≥2) and recurrence.

Results

We observed stenosis in 295 patients (50.9%)—middle cerebral artery stenosis was the most common (18.1%). Of all 579 included patients, normal or mild, moderate and severe stenoses or occlusions were observed in 51.8%, 6.0% and 42.1% of patients, respectively. Unfavourable outcomes were observed in 91 patients (15.7%), including 10 patients (1.7%) who died. The rate of stroke recurrence was 7.9%. Independent predictors of unfavourable outcomes included a high National Institutes of Health Stroke Scale score (OR 1.151, 95% CI 1.094 to 1.210; p<0.001) and severe vascular stenosis or occlusion (OR 1.867, 95% CI 1.181 to 2.952; p=0.008). Predictors of recurrence included age of ≥45 years (OR 2.072, 95% CI 1.066 to 4.025; p=0.032) and atrial fibrillation (OR 15.207, 95% CI 4.273 to 54.120; p<0.001).

Conclusions

Our research shows that when developing prevention strategies for young people, measures that focus on mitigating risk factors should be considered. In addition, vascular screening of young populations is also of vital importance for stroke prevention and poor prognosis prediction.
Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy
Authors: Galli Maria Giulia , Djuric Olivera , Besutti Giulia , Ottone Marta , Amidei Lucia , Bitton Lee , Bonilauri Carlotta , Boracchia Luca , Campanale Sergio , Curcio Vittoria , Lucchesi Davide Maria Francesco , Mulas Cesare Salvatore , Santi Francesca , Ferrari Anna Maria , Giorgi Rossi Paolo , Luppi Francesco
Published: May 2022

Objective

We aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalisation/death, in patients with COVID-19 previously discharged from the ED. We also developed a disease progression velocity index.

Design and setting

Retrospective cohort study of prospectively collected data. The charts of consecutive patients with COVID-19 discharged from the Reggio Emilia (Italy) ED (2 March 2 to 31 March 2020) were retrospectively examined. Clinical, laboratory and CT findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.

Participants

450 patients discharged from the ED with diagnosis of COVID-19.

Main outcome measure

ED readmission within 14 days, followed by hospitalisation/death.

Results

Of the discharged patients, 84 (18.7%) were readmitted to the ED, 61 (13.6%) were hospitalised and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03 to 1.08), Charlson Comorbidity Index 3 versus 0 (OR=11.61; 95% CI 1.76 to 76.58), days from symptom onset (OR for 1-day increase=0.81; 95% CI 0.73 to 0.90) and CT extension (OR for 1% increase=1.03; 95% CI 1.01 to 1.06) were associated in a multivariable model for readmission with hospitalisation/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08 to 1.36); the model including this index resulted in less information loss.

Conclusions

A velocity index combining CT extension and days from symptom onset predicts disease progression in patients with COVID-19. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.
Stillbirth, neonatal and maternal mortality among caesarean births in Kenya and Uganda: a register-based prospective cohort study
Authors: Ghosh Rakesh , Santos Nicole , Butrick Elizabeth , Wanyoro Anthony , Waiswa Peter , Kim Eliana , Walker Dilys
Published: May 2022

Objective

To investigate the interaction of risks for adverse maternal and perinatal outcomes (stillbirth, predischarge neonatal and maternal mortality) among caesarean section (CS) compared with vaginal deliveries (VD).

Design

Prospective cohort study.

Setting

10 CS-capable facilities in Busoga Region, East-Central Uganda and Migori County, Kenya.

Participants

Individual birth data were extracted from maternity registers between October 2016 and April 2019. There were a total of 77 242 livebirths and 3734 stillbirths. Overall, 24% of deliveries were by CS with a range of 9%–49% across facilities.

Primary outcome measures

Stillbirth, predischarge neonatal mortality and maternal mortality.

Results

The adjusted ORs for stillbirth, predischarge neonatal mortality and maternal mortality after a CS were 1.3 (95% CI 1.1 to 1.6), 1.9 (95% CI 1.6 to 2.2) and 3.3 (95% CI 2.2 to 4.9), respectively, compared with a VD. The association between maternal mortality and CS was 3.9 (95% CI 2.8 to 5.5) when the delivery was a live birth and 1.7 (95% CI 1.0 to 3.0) when it was a stillbirth. Post hoc analyses showed that mothers who received a CS had a lower risk of stillbirth if they were documented as a referral.

Conclusion

In this context, CS births were at higher risk for worse outcomes compared with VD. Better understanding of CS use and associated adverse outcomes within the mother–baby dyad is necessary to identify opportunities to improve quality of intrapartum care.

Trial registration number

NCT03112018.
NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data
Authors: Patel Rashmi , Wee Soon Nan , Ramaswamy Rajagopalan , Thadani Simran , Tandi Jesisca , Garg Ruchir , Calvanese Nathan , Valko Matthew , Rush A John , Rentería Miguel E , Sarkar Joydeep , Kollins Scott H
Published: May 2022

Purpose

NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression—Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health.

Participants

As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months).

Findings to date

The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data.

Future plans

The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.
Association between household context and emergency hospital use in older people: a retrospective cohort study on indicators for people living alone or living with somebody with frailty, developed from routine healthcare data in England
Authors: Lloyd Therese , Crellin Elizabeth , Brine Richard James , Shen Julia Y , Wolters Arne Timon
Published: May 2022

Objectives

To derive two household context factors - living alone and living in a two-person household with a person who is frail - from routine administrative health data and to assess their association with emergency hospital use in people aged 65 or over.

Design

Retrospective cohort study using national pseudonymised hospital data and pseudonymised address data derived from a minimised version of the Master Patient Index, a central database of all patient registrations in England.

Setting

England-wide.

Participants

4 876 285 people aged 65 years or older registered at GP practices in England on 16 December 2018 who were living alone or in a household of up to six people, and with at least one hospital admission in the last 3 years.

Outcomes

Rates of accident and emergency (A&E) attendance and inpatient emergency admissions over a 1-year follow-up period.

Results

Older people living alone had higher rates of A&E attendances (adjusted rate ratio 1.09, 95% CI 1.09 to 1.10) and emergency admissions (1.14, 95% CI 1.14 to 1.15) than older people living in households of 2–6 people. Older people living with someone with frailty in a two-person household had higher rates of A&E attendance (adjusted rate ratio 1.09, 95% CI 1.08 to 1.10) and emergency admissions (1.10, 95% CI 1.09 to 1.11) than other older people living in a two-person household.

Conclusions

We show that household context factors can be derived from linked routine administrative health data and that these are strongly associated with higher emergency hospital use in older people. Using household context factors can improve analyses, as well as support in the understanding of local population needs and in population health management.
mHealth apps delivering early intervention to support parents of children with autism: a scoping review protocol
Authors: Bharat Rahul , Uzaina Uzaina , Yadav Tribhuvanesh , Niranjan Sanjay , Kurade Palomi
Published: May 2022

Objective

This review aims to identify the mhealth apps delivering early intervention to support parents of children with autism spectrum disorders (ASD). We aim to explore the concept, context and methodology of implementation that is, theoretical framework, feasibility, quality of evidence, for such apps.

Background

To improve outcomes for children with autism, early intervention has been found to be promising. Parental training, parent psychoeducation and parent-mediated intervention are regarded as the gold standard, to achieve early childhood development goals. Digital health technologies like tele-health, web-based services, have been used to deliver this at a reduced cost. There is little evidence about their use and efficacy in empowering parents of children with ASD.

Inclusion criteria

The studies reporting the use of mhealth apps to support parents of children with ASD, in community settings, school settings, special schools, clinics, hospitals or child development centres. There will be no exclusion based on region, gender or sociocultural factors. The types of studies included will be quantitative, qualitative, mixed-methods study designs, case reports, grey literature, systematic reviews, clinical trials and studies reporting feasibility of digital mhealth applications.

Method

Using the NICE Healthcare Databases Advanced Search, we will search the following databases: MEDLINE, PUBMED, CINAHL, EMBASE, PsycINFO, Cochrane Library, EbscoHost, Sabinet, SAGE Journals, Directory of Open Access Journals, BioMed Central, Scopus, ScienceDirect. Furthermore, grey literature will be searched through Google Scholar, ShodhGanga, JSTOR, CORE, EBSCO, DOAJ, BASE. The searches will be limited to the age range of children between 2 and 6 years with ASD, and the date range is from the inception of the database to the current date. The terms for the ASD will be combined with terms for parent, early intervention and digital mhealth to identify eligible studies.
Targeted neonatal echocardiography training: a survey of trainees in a region of England
Authors: Cleator Alexander J , Subhedar Nimish V
Published: May 2022

Targeted neonatal echocardiography (TNE) is an important skill to advise diagnosis and management. Training in TNE is currently optional for neonatal subspecialty (grid) trainees and accessing training is often challenging. We disseminated a survey, asking neonatal grid trainees for their views on TNE training. 48 out of 91 trainees (53%) completed the survey. 96% of trainees (n=48) wanted to learn TNE with similar numbers eager to access a formal training package, using a variety of teaching media. Identified barriers to TNE training included time, access to supervision and the perceived complexity of the skill. These findings will influence the design and delivery of a regional TNE training programme.
Optic neuritis with potential for poor outcome
Authors: Cooper Sarah A , Leddy Sara Geraldine , Skipper Nicholas Tom , Barrett Victoria J M , Plant Gordon T
Published: May 2022

The Optic Neuritis Treatment Trial previously reported that corticosteroids accelerated visual recovery in optic neuritis (ON) without improving outcome. This finding related largely to multiple sclerosis (MS), and subsequently neurologists tended to await spontaneous recovery in ON. Since then, non-MS cases of ON have been identified with antibodies to aquaporin-4 (AQP4) or myelin oligodendrocyte glycoprotein (MOG). These disorders can closely mimic multiple sclerosis-associated or idiopathic demyelinating optic neuritis (MS/IDON) initially but risk a worse visual outcome. Scrutinising the clinical features and neuroimaging often enables differentiation between MS/IDON and other causes of ON. Early treatment with high-dose corticosteroids is an important determinant of visual outcome in non-MS/IDON. Prompt use of plasma exchange may also save sight. In this review, we contrast the presentations of myelin oligodendrocyte glycoprotein associated optic neuritis (MOG-ON) and aquaporin 4 associated optic neuritis (AQP4-ON) with MS/IDON and provide an approach to acute management while awaiting results of antibody testing.
Fibrocartilaginous embolism: an under-recognised cause of young spinal stroke
Authors: Nagaratnam Sai Adithya , Lee Raymond Lam Kwong , Sturm Jonathan
Published: May 2022

Fibrocartilaginous embolism is a rarely reported cause of spinal cord infarction. Seemingly innocuous activities may be associated with nucleus pulposus material embolising to the spinal cord vasculature. We describe a 36-year-old woman presenting with bilateral arm paraesthesia and chest pain that evolved into an acute cord syndrome. Initial MR scan of spine showed central spinal cord T2-weighted hyperintensity over several vertebral levels, suggesting transverse myelitis. Repeat MR scan after her symptoms persisted showed an acute cord infarction from a presumed fibrocartilaginous embolus. Clinicians should consider fibrocartilaginous embolism in patients presenting with an acute cord syndrome with supportive radiological findings.
Mechanism and effects of artesunate on the liver function of rats with type 1 diabetic periodontitis
Authors: Bai Shuoqiu , Zhou Jingjing , Nong Xiaolin , Shi Rongkang , Yuan Zhong , Ma Chubin , Li Jiaquan
Published: May 2022

Periodontitis is an inflammatory disease of the gums. Periodontitis in diabetic patients can aggravate insulin resistance; however, its molecular and biological mechanism remains unclear. This study aimed to explore the effects of diabetic periodontitis on liver function and determine the mechanism by which artesunate improves liver function. Rats with streptozotocin-induced diabetes were divided into five groups: normal control (NC), diabetic periodontitis (DM + PD), artesunate intervention (ART), insulin intervention (INS), and combined medication intervention (ART + INS) groups. Drug interventions were then administered to the rats in each group as follows: 50 mg/kg artesunate to the ART group, 6 U/kg insulin to the INS group, and 50 mg/kg artesunate + 6 U/kg insulin to the ART + INS group. Blood samples, liver tissues, and the maxillary alveolar bone were collected postsacrifice. ART was found to significantly ameliorate hyperglycemia, blood lipid concentrations, and liver function. The levels of inflammatory factors reduced; the effect was more pronounced in the ART + INS group. Artesunate presumably inhibits the TLR4/NF-κB signaling pathway and expression of downstream inflammatory factors, thereby exerting a protective effect on diabetes-related liver function. This offers a fresh approach to treat diabetes mellitus.
The effective potential of gluodynamics in the background of Polyakov loop and colormagnetic field
Authors: Bordag M. , Skalozub V.
Published: May 2022

In SU(N) gluodynamics, above the de-confinement temperature, the effective potential has minima at non-zero $$A_0$$

A
0

-background fields in the two-loop approximation. Also, it has a minimum at non-zero chromomagnetic background field, known as ’Savvidy’-vacuum, which shows up on the one-loop level. In this paper, we join these two approaches. We formulate, at finite temperature, the effective action, or the free energy, in SU(2) gluodynamics on the two-loop level, with both, $$A_0$$

A
0

background and magnetic background present at the same time, which was not done so far. We provide the necessary representations for both, effective numerical calculation and high-temperature expansions. The results are represented as a 3D plot of the real part of the effective potential. Also, we reproduce for zero either, the $$A_0$$

A
0

-background or the magnetic background, the known minima and compare them. The imaginary part is, on the two-loop level, still present. We mention that, as is known from literature for the case without $$A_0$$

A
0

-background, the imaginary part is compensated by the ring (’daisy’) diagrams. However, in our two-loop approximation, the results reveal an unnatural, singular behavior of the real part of the effective potential in the region, where the imaginary part sets in. Our conclusion is that one has to go beyond the two-loop approximation and its ring improved version, in order to investigate the minimum of the effective action as a function of $$A_0$$

A
0

and chromomagnetic field, and its stability, at least in the approximation of super daisy diagrams, i.e., the Hartree approximation in the CJT formalism.
Federated Learning for Electronic Health Records
Authors: Dang Trung Kien , Lan Xiang , Weng Jianshu , Feng Mengling
Published: May 2022

In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy, and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.
Tangible and intangible spaces of community connectivity
Authors: de Castro Leal Débora , Correa Teles Elaine Cristina
Published: May 2022

This forum is dedicated to exploring the notion of meaningfulness in design processes, taking the perspectives of community groups, nongovernmental organizations, and those who are marginalized in society as starting points. Authors will reflect conceptually and methodologically on practical engagements.
--- Rosanna Bellini and Angelika Strohmayer, Editors
Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender Systems
Authors: Alhejaili Abdullah , Fatima Shaheen
Published: May 2022

The traditional matrix factorisation (MF)-based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This article introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable recommender system framework utilises these meaningful user and item latent features to explain the recommendation without relying on private or outer data. The recommendations are explained to the user using text message and bar chart. Our proposed model has been evaluated in terms of the rating prediction accuracy and the reasonableness of the explanation using six real-world benchmark datasets for movies, books, video games, and fashion recommendation systems. The results show that the proposed model can produce accurate explainable recommendations.
Rethinking the participatory design conference experience
Authors: Clarke Rachel
Published: May 2022

The
Interactions
website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Analysis of Evolved Response Thresholds for Decentralized Dynamic Task Allocation
Authors: Mathias H. David , Wu Annie S. , Dang Daniel
Published: May 2022

In this work, we investigate the application of a multi-objective genetic algorithm to the problem of task allocation in a self-organizing, decentralized, threshold-based swarm. We use a multi-objective genetic algorithm to evolve response thresholds for a simulated swarm engaged in dynamic task allocation problems: two-dimensional and three-dimensional collective tracking. We show that evolved thresholds not only outperform uniformly distributed thresholds and dynamic thresholds but achieve nearly optimal performance on a variety of tracking problem instances (target paths). More importantly, we demonstrate that thresholds evolved for some problem instances generalize to all other problem instances, eliminating the need to evolve new thresholds for each problem instance to be solved. We analyze the properties that allow these paths to serve asuniversal training instancesand show that they are quite natural.

Aftera priorievolution, the response thresholds in our system are static. The problem instances solved by the swarms are highly dynamic, with schedules of task demands that change over time with significant differences in rate and magnitude of change. That the swarm is able to achieve nearly optimal results refutes the common assumption that a swarm must be dynamic to perform well in a dynamic environment.
Privacy Analysis of Query-Set-Size Control
Authors: Nussbaum Eyal , Segal Michael
Published: May 2022

The publication of user data for statistical analysis and research can be extremely beneficial for both academic and commercial uses, such as statistical research and recommendation systems. To maintain user privacy when such a publication occurs many databases employ anonymization techniques, either on the query results or the data itself. In this article, we examine and analyze the privacy offered when using the query-set-size control method for aggregate queries over a data structures representing various topologies. We focus on the mathematical queries of minimum, maximum, median, and average and show some query types that may be used to extract hidden information. We prove some combinations of these queries will maintain a measurable level of privacy even when using multiple queries. We offer a privacy probability measure, indicating the probability of an attacker to obtain information defined as sensitive by utilizing legitimate queries over such a system. Our results are mathematically proven and backed by simulations using vehicular network data based on the TAPASCologne project.
User-empowered Privacy-preserving Authentication Protocol for Electric Vehicle Charging Based on Decentralized Identity and Verifiable Credential
Authors: Parameswarath Rohini Poolat , Gope Prosanta , Sikdar Biplab
Published: May 2022

The use of Electric Vehicles (EVs) has been gaining traction in recent years due to various reasons. While charging their EVs, users expose their identity and personal details, and an adversary being able to identify and track where users charge their EVs is a potential privacy threat. In this article, we propose a user-empowered privacy-preserving authentication protocol for EV charging based on Decentralized Identifier (DID) and Verifiable Credential (VC) to provide Zero-Knowledge Proof (ZKP)-security. The proposed method gives users full control over their identities and allows them to remain anonymous while charging from any station. Giving control over their identities empowers users. At the same time, by making use of the concept of VC, other parties can verify that a user is legitimate and authenticate the user before providing charging services. Hence, the proposed protocol makes the charging service available in a secure way, while empowering users and preserving their privacy.
Hydrogen and Battery Storage Technologies for Low-Cost Energy Decarbonization in Distribution Networks
Authors: Haggi Hamed , Brooker Paul , Sun Wei , Fenton James M.
Published: May 2022

Deep energy decarbonization cannot be achieved without high penetration of renewables. At higher renewable energy penetrations, the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues with existing fossil fuel generation, requiring longer term energy storage to increase the reliability of grid operation. A proton exchange membrane electrolyzer can produce H
2 and serves as a utility controllable load. The produced H
2 can then be stored and converted back into electricity, or mixed with natural gas, or used as transportation fuel, or chemical feedstock. This paper considers the perspective of the distribution system operator that operates the distributed energy resources on a standard IEEE 33-node distribution network considering the technical and physical constraints with the goal of minimizing total investment and operation cost. Different case studies, at very high PV penetrations are considered to show the challenges and path to net-zero emission energy production using H
2 energy. Sensitivity of utility PV costs and electrolyzer capital costs on producing H
2 at $1/kg are presented showing that the distribution network could produce 100% renewable electricity and H
2 could be produced at a cost of $1/kg by 2050 with conservative cost estimates and by 2030 with accelerated cost declines.
Development of Electroactive and Stable Current Collectors for Aqueous Batteries
Authors: Bergman Gil , Nimkar Amey , Saha Arka , Gavriel Bar , Turgeman Meital , Malchik Fyodor , Fan Tianju , Tsubery Merav Nadav , Noked Malachi , Sharon Daniel , Shpigel Netanel
Published: May 2022

The need for low-cost, high-safety batteries for large-scale energy storage applications has sparked a surge in research of rechargeable aqueous batteries. While most research efforts are focused on the development of electrolyte formulations and electrode materials, it appears that the current collector impact on the battery performance is frequently overlooked. Even though the current collector is traditionally thought of as an inactive battery component, it is included in the battery energy density calculations, making its activation desirable. Furthermore, poor current collector selection can cause irreversible side reactions, resulting in rapid cell efficiency decay. Herein we propose a new approach to design current collectors that makes use of anodized Ti. The redox-active anodized Ti significantly improves the overall anode capacity and provides effective inhibition of hydrogen formation on the electrified interface. The use of TiO2 particles on an anodized Ti current collector in an aqueous electrolyte solution resulted in capacity of 130 mAh g−1 and exceptional capacity retention of 99% after 1000 cycles. Although the concept of active current collectors needs to be refined before it can be implemented in commercial cells, our findings indicate that this approach could be useful for improving overall cell performance without requiring significant changes to its configuration.
Metallic Nitride and Carbide Perovskites: History and Prospects
Authors: Coey J. M. D. , Givord D. , Fruchart D.
Published: May 2022

Energy-level diagrams for cubic metallic Fe4N and Mn4N were proposed by Goodenough in the late 1960s. Fe4N is ferromagnetic, but Mn4N is ferrimagnetic with a large moment on Mnc at the cube corner site and a much smaller antiparallel contribution from Mnf at the three face-centre sites. Neutron diffraction revealed noncollinear ferrimagnetism with no compensation where the Mnf moments form 120° triangular antiferromagnetic sublattices but are tilted out of the kagome (111) planes to give the small net sublattice moment. A rich variety of magnetic ordering exists in the ternary Mn3−xM′xN metallic perovskites. Partial substitution of nonmagnetic M′ on Mnc sites leads to a tunable ferrimagnetic compensation point. Two possible antiferromagnetic modes in the kagome planes are a topological Γ4g mode, and a nontopological Γ5g mode where the in-plane components of the Mnf spins lie, respectively, perpendicular and parallel to the edges if the triangles in the kagome planes . Interest in the metallic perovskites has revived with the availability of high-quality thin films that facilitate measurements of magneto-transport properties, strain effects and spin wave velocity. The range of magnetic structures, magnetotransport, magnetocaloric and magnetovolume effects is exceptionally large. The topological ferrimagnets exhibit large anomalous Hall effects. The magnetism is compared with materials where N is replaced by C.
Research and Implementation of Emotional Classification of Traditional Folk Songs Based on Joint Time-Frequency Analysis
Authors: Li Yue
Published: May 2022

Traditional folk music is built on traditional folk songs. The five types of emotions of patriotism, homesickness, affection, friendship, and love are all depicted in modern Chinese folk songs, which show the rich and colorful emotions of people of all ethnic groups in China. Modern Chinese folk songs that express these emotions have a variety of artistic features in terms of melody, rhythm, mode, and mood and provide the audience with unique aesthetic experiences. This paper focuses on JTFA- (Joint Time-Frequency Analysis-) based emotional classification of traditional folk songs, as well as PCA (principal component analysis) and KPCA (Kernel-Based Principle Component Analysis) methods for nonstationary signal feature dimension reduction. The simulation results show that the number of features in KPCA is less than in PCA for the same accuracy. When the number of features is equal to the number of principal components, the accuracy is higher than PCA, indicating that KPCA has a better effect in dimension reduction and feature extraction. Furthermore, in all categories, the double-layer classification model maintains a relatively high recall rate and accuracy rate, demonstrating the effectiveness of the double-layer multimusic emotion classification model.
The Influence of the Network Evolutionary Game Model of User Information Behavior on Enterprise Innovation Product Promotion Based on Mobile Social Network Marketing Perspective
Authors: Liu Tingting , He Xiaofei , Guo Xin , Zhao Yi
Published: May 2022

User information behavior is an important factor affecting the promotion effect of enterprises’ innovative products on mobile social networks. To provide insights as to how enterprises can better promote innovative products, this paper conducts a quantitative study of its internal function. We introduce a network evolutionary game model based on the Bass model, first to describe the background of the mobile social network, then to simulate the diffusion process of the social network and the user’s decision-making game behavior during promotion of enterprises’ innovative products, and finally to simulate the promotion effect on innovative products and solve for the conditions for the best innovative product marketing effect and the model are simulated from multiple angles. Our simulation results show that mobile social network marketing in the initial stage of product promotion has a significant impact on the promotion effect of innovative products. When user feedback is poor, advertisement diffusion has an obvious effect on product promotion. In mobile social networks, the proportion of positive feedback from users has a greater impact on the product promotion effect, while the proportion of negative feedback has a smaller impact on the product promotion effect. The impact of negative reviews is time sensitive. According to the simulation results, we propose several suggestions to improve the promotion effect of enterprises’ innovative products on mobile social networks.
A Computational Approach to Justifying Stratifin as a Candidate Diagnostic and Prognostic Biomarker for Pancreatic Cancer
Authors: Mogal Md Roman , Junayed Asadullah , Mahmod Md Rashel , Sompa Sagarika Adhikary , Lima Suzana Afrin , Kar Newton , TasminaTarin  , Khatun Marina , Zubair Md Abu , Sikder Md Asaduzzaman
Published: May 2022

Pancreatic cancer (PC) is considered a silent killer because it does not show specific symptoms at an early stage. Thus, identifying suitable biomarkers is important to avoid the burden of PC. Stratifin (SFN) encodes the 14‐3‐3σ protein, which is expressed in a tissue‐dependent manner and plays a vital role in cell cycle regulation. Thus, SFN could be a promising therapeutic target for several types of cancer. This study was aimed at investigating, using online bioinformatics tools, whether SFN could be used as a diagnostic and prognostic biomarker in PC. SFN expression was explored by utilizing the ONCOMINE, UALCAN, GEPIA2, and GENT2 tools, which revealed that SFN expression is higher in PC than in normal tissues. The clinicopathological analysis using the ULCAN tool showed that the intensity of SFN expression is commensurate with cancer progression. GEPIA2, R2, and OncoLnc revealed a negative correlation between SFN expression and survival probability in PC patients. The ONCOMINE, UCSC Xena, and GEPIA2 tools showed that cofilin 1 is strongly coexpressed with SFN. Moreover, enrichment and network analyses of SFN were performed using the Enrichr and NetworkAnalyst platforms, respectively. Receiver operating characteristic (ROC) curves revealed that tissue‐dependent expression of the SFN gene could serve as a diagnostic and prognostic biomarker. However, further wet laboratory studies are necessary to determine the relevance of SFN expression as a biomarker.
YOLOv5-Based Vehicle Detection Method for High-Resolution UAV Images
Authors: Chen Ziwen , Cao Lijie , Wang Qihua
Published: May 2022

To solve the feature loss caused by the compression of high-resolution images during the normalization stage, an adaptive clipping algorithm based on the You Only Look Once (YOLO) object detection algorithm is proposed for the data preprocessing and detection stage. First, a high-resolution training dataset is augmented with the adaptive clipping algorithm. Then, a new training set is generated to retain the detailed features that the object detection network needs to learn. During the network detection process, the image is detected in chunks via the adaptive clipping algorithm, and the coordinates of the detection results are merged by position mapping. Finally, the chunked detection results are collocated with the global detection results and outputted. The improved YOLO algorithm is used to conduct experiments comparing this algorithm with the original algorithm for the detection of test set vehicles. The experimental results show that compared with the original YOLO object detection algorithm, the precision of our algorithm is increased from 79.5% to 91.9%, the recall is increased from 44.2% to 82.5%, and the mAP@0.5 is increased from 47.9% to 89.6%. The application of the adaptive clipping algorithm in the vehicle detection process effectively improves the performance of the traditional object detection algorithm.
Evaluation of Enterprise Financial Risk Level under Digital Transformation with Artificial Neural Network
Authors: Yang Dijie
Published: May 2022

With the continuous development of the national economy, enterprises have gradually entered the digital age from the industrial age, and digital transformation has become a must for enterprise survival. Digital transformation empowers the high-quality development of enterprises and puts forward higher requirements for financial risk management. The problem of enterprise financial risk is one that every business must deal with at some point during its operations. The digital age has ushered in a new era for enterprise financial risk management. Digital transformation is also a weather vane, which is beneficial to comprehensively avoid financial risks, do a good job in system defense and multiparty coordination, and can quickly and accurately manage corporate finances and solve risks in a timely manner. Starting from the background of digital transformation, this article clarifies the significance of preventing corporate financial risks in the new era. Combining it with artificial neural networks, this work proposes an intelligent method for assessing the financial risk level of enterprises in this context. This work proposes an EFRL-ResNet neural network, which is improved on the basis of ResNet. At the same time, the depth-wise separable convolution (DSConv) structure in Mobile-Net is combined with the ResNet network to build a lightweight deep neural network. Through the case of enterprise financial risk, it is verified that the method can reduce the training time of the model without losing the accuracy of grade evaluation. At the same time, this paper improves the loss function in the network model for the problem of an unbalanced number of data samples in the financial risk level assessment model.
Chinese Professional Basketball League Using Mobile Computing and Big Data Technology
Authors: Yan Haoming , Liu Yongfeng
Published: May 2022

The impact of big data technology on the Chinese Men’s Professional Basketball League (CBA) is studied in this study. With the growth of the CBA league in recent years, teams from the western region have emerged. Although there is a trend of “multipolarization” in the competition pattern, it overall depicts a situation in which the east is strong and the west is weak. In addition, the method of recruiting foreign aid workers has improved over time, as has the foreign aid system. The number of foreign aids has increased from one in 1995 to a high of 47, and the quality of foreign aid has improved over time. This not only strengthens the club team, but it also has a significant impact on the CBA league’s competition patterns. The basic factors that affect the changes in the competition pattern are institutional factors, coaching factors, star player factors, foreign aid factors, regional economic factors, reserve talent factors, team style factors, player security factors, and so on, according to research and analysis of the CBA league’s development and changes. Six teams have won the CBA league championship in its 22 seasons. The Bayi and Guangdong teams each won eight times, accounting for more than 70% of the total number of championships.