bluuebunny/crossref_metadata_2025 huggingface dataset:
On Jump Discontinuities in Internal Forces of Flexible Structures Carrying Moving Subsystems
Authors: Yang Bingen , Gao Hao
Published: May 2023

Combined systems, which are flexible structures carrying moving subsystems, are seen in various applications. Due to structure–subsystem interactions, the structure in a combined system encounters jump discontinuities in its internal forces (such as the bending moment and shear force of a beam). Accurate estimation of such jump discontinuities is important to the performance, safety, and longevity of a combined system. Because of the time-varying nature and complexity of structure–subsystem interactions, conventional series solution methods experience slow convergence, and the Gibbs phenomenon in computation and the improved series expansion methods are limited to certain proportionally damped continua under moving forces and moving oscillators. In this paper, a novel modified series expansion method (MSEM) is proposed to resolve the aforementioned issues with the existing series solution methods. Through the introduction of a jump influence function, the proposed method produces fast-convergent series solutions and accurately predicts the jump discontinuities without the Gibbs phenomenon. The MSEM is applicable to structures with nonproportional damping and subject to arbitrary boundary conditions, and it can easily manage general M-DOF moving subsystems having multiple contact points with a supporting structure. As an important result of this investigation, a mathematical proof of the convergence of the MSEM-based solutions is given for the first time. Additionally, two numerical examples are presented to demonstrate the accuracy, efficiency, and versatility of the proposed MSEM in modeling and analysis of combined systems.
Enhanced the Stability and Storage Capability of Sulfide-Based Material With the Incorporation of Carbon Nanotube for High-Performance Supercapattery Device
Authors: Yasmeen Aneeqa , Afzal Amir Muhammad , Iqbal Muhammad Waqas , Zaka Asma , Hassan Haseeb ul , Abbas Tasawar , Usman Muhammad , Wang Liang , Alanazi Yousef Mohammed , Mumtaz Sohail
Published: May 2023

Supercapattery is a recently developed energy storage device that includes the properties of a supercapacitor and a rechargeable battery. A hydrothermal method is used to synthesize the sulfide-based materials. The structural morphology, elemental composition, and electrochemical properties are measured using X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, and potentiostat system. The specific capacitance is enhanced up to 1964.2 F/g by making the composite with carbon nanotubes (CNTs), which is higher than the reference sample (MnS). In the case of a real device, the obtained value of specific capacity in manganese sulfide/CNTs/activated carbon is 240 C/g which is much improved compared to the previously reported values. In a supercapattery device, an excellent energy density of 53.3 Wh/Kg and a high power density of 7995 W/kg are obtained. The stability of the device is measured up to 1000 cycles and achieved the specific capacity retention of 86% with columbic efficiency of 97%. Electrochemical impedance spectroscopy (EIS) and Brunauer–Emmett–Teller (Lee et al., 2012, Self-standing Positive Electrodes of Oxidized few-Walled Carbon Nanotubes for Light-Weight and High-Power Lithium Batteries,” Energy Environ. Sci., 5(1), pp. 5437–5444) measurements confirm the improvement in surface area and electrochemical properties. Our results show that a 50/50 weight ratio of manganese sulfide and CNTs are more suitable and provide opportunities to design high-performance energy storage devices.
A Variable Stiffness Sole for Biped Robot and Its Experimental Verification
Authors: Namgung Junyeon , Han Yun-Ho , Cho Baek-Kyu
Published: May 2023

The foot sole of the biped robot is an important factor for stable walking. In this study, the limitations of existing bipedal robot soles are introduced and the necessity for the development of a new sole mechanism is presented. Inspired by a robot sole based on the granular jamming effect, we have developed a variable stiffness sole (VSS), which adapts to the shape of obstacles on the ground in compliant mode and provides robust support in stiff mode. Finally, the performance of the VSS is verified by several experiments integrating the VSS with the real humanoid robot platform RoK-3. The experimental results verified that the VSS is advantageous in uneven terrain walking.
Intelligent Control Strategy of Fresh Air Volume in Cabin under Heating Condition and Evaluation of Its Energy Saving Effect
Authors: Liang Yuanqing , Wang Jianbin , Wang Shuang
Published: May 2023

The problem of “range anxiety” of electric vehicles is gradually highlighted. Especially when the onboard air conditioner is used by electric vehicles in winter, its actual range will be significantly shortened. This paper comprehensively analyzes the impact of fresh air demand on the energy consumption of the PTC(Positive Temperature Coefficient)heating system, so that the intelligent control strategy of fresh air under heating conditions is proposed, and the energy-saving effect of the intelligent control scheme is analyzed. It is found that the intelligent control strategy of fresh air can significantly reduce the energy consumption of the system. At the same time, a certain electric vehicle with a battery energy of 100kW·h was selected to analyze its range. It is found that the range of the intelligent control scheme is increased by about 32.34% when the ambient temperature is 0°C.
A Cooling Efficiency Model and Numerical Research of Multiparameter Film Cooling
Authors: Xu Zhexuan , Xu Zheyao , Chen Yukun
Published: May 2023

Based on the validated simulation method of film cooling and multiphase flow simulation method, a multi-level three-dimensional simulation of forward-leaning fan-shaped film hole, cylindrical film hole with different injection angles, and film hole containing water vapor are established to discuss the effects of film hole structure parameters, hole distance, blowing ratio, injection angle, and water vapor volume on film cooling efficiency. The cooling efficiency of forward-leaning fan-shaped film hole increases as the exit length of film hole increases. After adding water vapor, the cooling efficiency of fan-shaped film hole decreases, and the influence of hole axis length and exit length on cooling efficiency is weak. For the cylindrical film hole, the larger the injection angle of film hole, the larger the film coverage area under the same blowing ratio. After adding water vapor, with the increase of the blowing ratio, the film coverage area increases first and then decreases. However, the film coverage area decreases with the increase of cooling injection angle for film holes containing water vapor. The cooling efficiency of the film hole with and without water vapor is related to the vapor velocity in the rising direction and the velocity in the mainstream direction, respectively. A model of film cooling efficiency with air blowing ratio and injection angle is established and verified with experimental data, based on the law that the average cooling efficiency in the main flow direction grows exponentially with the sine of the injection angle.
Tryhard With a Vengeance: Meaning Making and Boundary Keeping on Twitch
Authors: Evans Sarah , Llano Stephen M.
Published: May 2023

Tryhard is a derogatory term aimed to insult players who are “trying too hard” in video games. In the past few years, there have been notable controversies surrounding tryhard, most insidiously in a memetic permutation of the term via the TriHard emote on Twitch. We will use the case of the meme “tryhard” and the racist deployments of the emote “TriHard” to show how the meme generates meaning from circulation, enabling a variety of interpretations. We argue that claims about banning particular memes or emotes on Twitch are part of this battle over the stability of the meaning of the utterance, not a solution to the potential for memes to carry negative or harmful meanings. The implication is that decisions like banning a meme or restricting its circulation will eliminate the problem of inappropriate meaning. We argue that such policies remove this reconstitutive ability from communities, eliminating the opportunity for rearticulation and, therefore, new definitions.
Switching off the bat signal
Authors: Wong Wei
Published: May 2023

Bat ASC2 inhibits inflammasome activation by preventing ASC speck formation.
Rapid Detection of Extensive Drug Resistance by Xpert MTB/XDR Optimizes Therapeutic Decision-Making in Rifampin-Resistant Tuberculosis Patients
Authors: Chen Xinchang , Li Rong , Ge Shijia , Li Yang , Cai Cui , Weng Taoping , Zhang Yilin , Jiang Jingwen , Feng Zhen , Chen Yuanyuan , Zhang Yungui , Ma Jian , Persing David H. , Chen Jiazhen , Tang Yi-Wei , Sun Feng , Zhang Wenhong
Published: May 2023

The Xpert MTB/XDR assay met the critical need for etiologic diagnosis of tuberculosis and rifampin resistance in previous studies. However, its benefits in tailoring the treatment regimen and improving the outcome for patients with rifampin-resistant tuberculosis (RR-TB) require further investigation.
Draft Genome Sequence of Lactiplantibacillus plantarum DMR09, Isolated from Spontaneously Fermented
Dahi
Authors: Tirwa Ranjan Kaushal , Bhattarai Anil , Tamang Buddhiman
Published: May 2023

The draft genome sequence of
Lactiplantibacillus plantarum
DMR09, which was isolated from spontaneously fermented homemade cow
dahi
, was analyzed. The genome profile of
L. plantarum
DMR09 revealed 3,315,630 bp and a GC content of 44.5%, with 3,036 protein-encoding genes, 72 rRNA genes, and 65 tRNA genes.
Evidence of ACL healing on MRI following ACL rupture treated with
rehabilitation alone may be associated with better patient-reported
outcomes: a secondary analysis from the KANON trial
Authors: Filbay Stephanie Rose , Roemer Frank W , Lohmander L Stefan , Turkiewicz Aleksandra , Roos Ewa M , Frobell Richard , Englund Martin
Published: May 2023

Objectives

Evaluate the natural course of anterior cruciate ligament (ACL)
healing on MRI within 5 years of acute ACL rupture and compare 2-year
and 5-year outcomes based on healing status and treatment
group.

Methods

Secondary analysis of 120 Knee Anterior Cruciate Ligament
Nonsurgical vs Surgical Treatment (KANON) trial participants randomised
to rehabilitation and optional delayed ACL reconstruction (ACLR) or
early ACLR and rehabilitation. ACL continuity on MRI (Anterior Cruciate
Ligament OsteoArthritis Score 0–2) was considered evidence of ACL
healing. Outcomes included Knee Injury and Osteoarthritis Outcome Score
(KOOS), KOOS patient acceptable symptomatic state (PASS) and treatment
failure criteria. Linear mixed models were used to estimate adjusted
mean differences (95% CIs) in patient-reported sport and recreational
function (KOOS-Sport/Rec) and quality of life (KOOS-QOL) at 2 and 5
years, between participants with MRI evidence of ACL healing and those
who had (1) no evidence of ACL healing, (2) delayed ACLR or (3) early
ACLR.

Results

MRI evidence of ACL healing at 2-year follow-up was observed in 16
of 54 (30%, 95% CI 19 to 43%) participants randomised to optional
delayed ACLR. Excluding participants who had delayed ACLR, 16 of 30
(53%, 36–70%) participants managed with rehabilitation-alone displayed
MRI evidence of ACL healing. Two-year outcomes were better in the healed
ACL group (n=16) compared with the non-healed (n=14) (mean difference
(95% CI) KOOS-Sport/Rec: 25.1 (8.6–41.5); KOOS-QOL: 27.5 (13.2–41.8)),
delayed ACLR (n=24) (KOOS-Sport/Rec: 24.9 (10.2–39.6); KOOS-QOL: 18.1
(5.4–30.8)) and early ACLR (n=62) (KOOS-Sport/Rec: 17.4 (4.1–30.7);
KOOS-QOL: 11.4 (0.0–22.9)) groups. Five-year KOOS-QOL was better in the
healed versus non-healed group (25.3 (9.4–41.2)). Of participants with
MRI evidence of ACL healing, 63–94% met the PASS criteria for each KOOS
subscale, compared with 29–61% in the non-healed or reconstructed
groups.

Conclusions

MRI appearance of ACL healing after ACL rupture occurred in one in
three adults randomised to initial rehabilitation and one in two who did
not cross-over to delayed ACLR and was associated with favourable
outcomes. The potential for spontaneous healing of the ACL to facilitate
better clinical outcomes may be greater than previously
considered.

Trial registration number

NCT84752559.
Hospital-treated serious and invasive aspergillosis and candidiasis infections during the COVID-19 pandemic: a retrospective analysis of Hospital Episode Statistics data from England
Authors: Sung Anita H , Kiely Gillian , Singh Jyotika K , Thomas Stephen , Lough Jemma , Smith Matthew
Published: May 2023

Objectives

To investigate the impact of COVID-19 on the burden of hospital-treatedAspergillusandCandidainfections in England.

Design

A retrospective study using Hospital Episodes Statistics data to estimate the burden of serious and invasive fungal infections (SIFIs) in all patients admitted in England during March 2018–February 2020 (pre-COVID-19) and during March 2020–October 2021 (the COVID-19 period).

Setting

Hospitals in England.

Population

All patients with codes corresponding to serious and invasive aspergillosis and candidiasis in any diagnosis position during their admission pre-COVID-19 and during the COVID-19 period.

Outcome measures

Age, spells, patient counts, mean length of stay, admission to critical care unit (CCU), length of stay in CCU, 30-day readmissions, failed discharges (readmission within 7 days) and comorbidities.

Results

During the COVID-19 period, hospitalisation spells with an invasive candidiasis code fell by 3.2% and spells with an aspergillosis code by 24.8%. Mean length of stay was higher for patients with aspergillosis with or without COVID-19 and candidiasis with or without COVID-19 during the pandemic than before the pandemic. During the pandemic, mean length of stay was higher for patients with aspergillosis with COVID-19 than those with aspergillosis alone but slightly lower for patients with candidiasis with COVID-19 than for those with candidiasis alone. Of patients with a diagnosis of COVID-19, 52.5% with aspergillosis and 60.0% with candidiasis were treated in CCU compared with 13.2% and 37.1%, respectively, without a COVID-19 diagnosis. The percentage of 30-day readmissions and failed discharges for patients with SIFI was higher for those with COVID-19 than for those without.

Conclusions

The burden of aspergillosis and candidiasis has been affected by COVID-19. Aspergillosis diagnoses fell among hospitalised patients during the pandemic, while candidiasis continued to fluctuate in patterns similar to pre-COVID-19. A higher burden for patients with SIFI was observed, whether or not they also had a diagnosis of COVID-19. Our findings highlight extra considerations and burden on management of serious SIFI as a result of the COVID-19 pandemic.
Psychometric properties of Hamilton Depression Rating Scale among people with epilepsy in Jimma University Medical Center, Neurology Clinic, Southwest Ethiopia, 2020: cross-sectional study.
Authors: Yusuf Muhammednur , Abera Mubarek , Getnet Masrie , Dawud Badiru
Published: May 2023

Objective

It is crucial to use clinically validated instruments to detect and treat depression in people with epilepsy. Therefore, this study aimed to describe the psychometric properties of the Afaan Oromo version of the Hamilton Depression Rating Scale 17-item (HAMD-17) among these individuals in Ethiopia.

Design

A cross-sectional validation study.

Setting

A tertiary hospital, Southwest Ethiopia.

Participants

A total of 133 people with epilepsy were included in this study using a consecutive sampling technique from 1 September 2020 to 30 September 2020.

Outcome

The psychometric property of the HAMD-17. Criterion validity was assessed using the Mini-International Neuropsychiatric Interview (MINI). The internal consistency was determined using Cronbach’s alpha. The receiver operating characteristic (ROC) analysis was used in determining the cut-off score, sensitivity, specificity and positive predictive value (PPV) and negative predictive value (NPV). The tools were translated into the local language (Afaan Oromo) and back into English and pretested before the data collection.

Results

The mean age of the participants was 31.7 years, SD±10.7. Eighty-six (64.7%), 55 (48.1%), 68 (51.1%) and 62 (46.6%) of them were male, unmarried, urban residents and completed primary school, respectively. The internal consistency of HAMD-17 was α=0.74. The Pearson’s correlation coefficient for criterion validity was r=0.88. The ROC analysis showed 0.92 and 0.91 sensitivity and specificity of the HAMD-17, respectively. It also revealed α=0.96 NPV and α=0.87 PPV at a cut score of ≥9. The area under the ROC curve was 0.96.

Conclusions

The Afaan Oromo version of the HAMD-17 was considered valid and reliable for assessing depression in people with epilepsy, and the tool is not difficult to understand. The diagnostic performance using the gold standard MINI showed a good discriminatory capacity of the instrument. It can be used by any health professional to screen depression in people with epilepsy.
Immunogenicity and safety of mixed COVID-19 vaccine regimens in patients with immune-mediated inflammatory diseases: a single-centre prospective cohort study
Authors: Hitchon Carol A , Mesa Christine , Bernstein Charles N , Marrie Ruth Ann , Card Catherine , O’Brien Sheila F , Kim John
Published: May 2023

Objective

Among persons with immune-mediated inflammatory diseases (IMIDs) who received SARS-CoV-2 vaccines, we compared postvaccine antibody responses and IMID disease activity/states.

Design

Single-centre prospective cohort study.

Setting

Specialty ambulatory clinics in central Canada.

Participants

People with inflammatory arthritis (n=78; 77% rheumatoid arthritis), systemic autoimmune rheumatic diseases (n=84; 57% lupus), inflammatory bowel disease (n=93; 43% Crohn’s) and multiple sclerosis (n=72; 71% relapsing-remitting) (female 79.4%, white 84.7%, mean (SD) age 56.0 (14.3) years) received COVID-19 vaccinations between March 2021 and September 2022.

Primary outcome

Postvaccination anti-spike, anti-receptor binding domain (anti-RBD) and anti-nucleocapsid (anti-NC) IgG antibodies tested by multiplex immunoassays compared across vaccine regimens and with responses in 370 age-matched and sex-matched vaccinated controls.

Secondary outcomes

COVID-19 infection and self-reported IMID disease activity/state.

Results

Most (216/327, 66.1%) received homologous messenger RNA (mRNA) (BNT162b2 or mRNA1273) vaccines, 2.4% received homologous ChAdOx1 and 30.6% received heterologous vaccines (23.9% ChAdOx1/mRNA, 6.4% heterologous mRNA) for their first two vaccines (V1, V2). Seroconversion rates were 52.0% (91/175) for post-V1 anti-spike and 58.9% (103/175) for anti-RBD; 91.5% (214/234) for post-V2 anti-spike and 90.2% (211/234) for anti-RBD; and were lower than controls (post-V2 anti-spike 98.1% (360/370), p<0.0001). Antibody titres decreased 3 months after V2 but increased 1 month after the third vaccine (V3) and 1 month after the fourth vaccine (V4) (BAU/mL median (IQR), anti-spike 1835 (2448) 1 month post-V2, 629.1 (883.4) 3 months post-V2, 4757.5 (7033.1) 1 month post-V3 and 4356.0 (9393.4) 1 month post-V4; anti-RBD 1686.8 (2199.44) 1 month post-V2, 555.8 (809.3) 3 months post-V2, 4280.3 (6380.6) 1 month post-V3 and 4792.2 (11 673.78) 1 month post-V4). If primed with a vector vaccine, an mRNA vaccine increased antibody titres to those comparable to homologous mRNA vaccines. Anti-RBD and anti-spike titres were higher in anti-NC seropositive (n=31; 25 participants) versus seronegative samples (BAU/mL median (IQR) anti-RBD 11 755.3 (20 373.1) vs 1248.0 (53 278.7); anti-spike 11 254.4 (15 352.6) vs 1313.1 (3106.6); both p<0.001). IMID disease activity/state and rates of self-reported moderate or severe IMID flare were similar across vaccinations.

Conclusion

Heterologous COVID-19 vaccination improves seroconversion rates following a vector vaccine and does not lead to IMID disease flare. IMIDs benefit from at least three vaccines.
Choice of anaesthesia in microelectrode recording-guided deep-brain stimulation for Parkinson’s disease (CHAMPION): study protocol for a single-centre, open-label, non-inferiority randomised controlled trial
Authors: Xie Sining , Shi Lin , Xiong Wei , Chen Liang , Li Xiangjiahui , Tong Yuanyuan , Yang Wanning , Wang Anxin , Zhang Jianguo , Han Ruquan
Published: May 2023

Introduction

Deep brain stimulation (DBS) implantation under general anaesthesia (GA) has been applied to patients with Parkinson’s disease (PD) with severe comorbidities or disabling off-medication symptoms. However, general anaesthetics may affect intraoperative microelectrode recording (MER) to varying degrees. At present, there are few studies on the effects of sedatives or general anaesthetics on multiunit activity characteristics performed by MER in patients with PD during DBS. Therefore, the effect of the choice of anaesthesia on MER remains unclear.

Methods/design

This is a prospective randomised controlled, non-inferiority study that will be carried out at Beijing Tiantan Hospital, Capital Medical University. Patients undergoing elective bilateral subthalamic nucleus (STN)-DBS will be enrolled after careful screening for eligibility. One hundred and eighty-eight patients will be randomised to receive either conscious sedation (CS) or GA at a 1:1 ratio. The primary outcome is the proportion of high normalised root mean square (NRMS) recorded by the MER signal.

Ethics and dissemination

The study was approved by the Ethics Committee of Beijing Tiantan Hospital of Capital Medical University (KY2022-147-02). Negative study results will indicate that GA using desflurane has a non-inferior effect on MER during STN-DBS compared with CS. The results of this clinical trial will be presented at national or international conferences and submitted to a peer-reviewed journal.

Trial registration number

NCT05550714.
Identify and classify interprofessional primary care performance indicators: a scoping review protocol
Authors: Yapi Sopie Marielle , Poitras Marie-Eve , Donnelly Catherine , Ashcroft Rachelle , Greiver Michelle , Couturier Yves , Nikiema Jean Noël , Breton Mylaine , Layani Géraldine , Kaczorowski Janusz , Bergman Howard , Lussier Marie-Thérèse , Aggarwal Monica , Fernainy Pamela , McGraw Monica , Milius Djims , Mehta Kavita , Samson Kevin , Sourial Nadia
Published: May 2023

Introduction

Measuring the performance of interprofessional primary care is needed to examine whether this model of care is achieving its desired outcomes on patient care and health system effectiveness as well as to guide quality improvement initiatives. The aim of this scoping review is to map the literature on primary care performance measurement indicators to determine the extent to which current indicators capture or could be adapted to capture processes, outputs and outcomes that reflect interprofessional primary care.

Methods and analysis

The review will be guided by the six-stage framework by Arksey and O’Malley (2005). MEDLINE, Embase, CINAHL, grey literature and the reference list of key studies will be searched to identify any study, published in English or French between 2000 and 2022, related to the concepts of performance indicators, frameworks, interprofessional teams and primary care. Two reviewers will independently screen all abstracts and full-text studies for inclusion. Eligible indicators will be classified according to process, output and outcome domains proposed by two validated frameworks. This study started in November 2022 and is expected to be completed by July 2023.

Ethics and dissemination

This review does not require ethical approval. The results will be disseminated through a peer-reviewed publication, conference presentations and presentations to stakeholders.
Tailored machine learning for evaluating the long-term diabetes risk in older individuals: findings from the Irish Longitudinal Study on Ageing (TILDA)
Authors: Xu Xuezhong , Mingyang Xue , Yang Jie , Zheng Hailong , Che Zhifei
Published: May 2023

Objectives

The prevalence of diabetes has increased globally, leading to a significant disease burden and financial cost. Early prediction is crucial to control its prevalence.

Design

A prospective cohort study.

Setting

National representative study on Irish.

Participants

8504 individuals aged 50 years or older were included.

Primary and secondary outcome measures

Surveys were conducted to collect over 40 000 variables related to social, financial, health, mental and family status. Feature selection was performed using logistic regression. Different machine/deep learning algorithms were trained, including distributed random forest, extremely randomised trees, a generalised linear model with regularisation, a gradient boosting machine and a deep neural network. These algorithms were integrated into a stacked ensemble to generate the best model. The model was tested using various metrics, such as the area under the curve (AUC), log loss, mean per classification error, mean square error (MSE) and root MSE (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the established model.

Results

After 2 years, 105 baseline features were identified as major contributors to diabetes risk, including sex, low-density lipoprotein cholesterol and cirrhosis. The best model achieved high accuracy, robustness and discrimination in predicting diabetes risk, with an AUC of 0.854, log loss of 0.187, mean per classification error of 0.267, RMSE of 0.229 and MSE of 0.052 in the independent test set. The model was also shown to be well calibrated. The SHAP algorithm provided insights into the decision-making process of the model.

Conclusions

These findings could help physicians in the early identification of high-risk patients and implement targeted interventions to reduce diabetes incidence.
International implementation of a care review process for mortality reviews: improving quality and safety virtually across the world
Authors: Cullinan Susan M , Yubeta Toni R , Al-Qadi Lara S , Bukhari Moatasiem I , Morgenthaler Timothy I
Published: May 2023

Mortality reviews are intended to produce transparent, non-punitive personal and organisational learning that leads to systematic improvement in care. Mayo Clinic has a well-established care review process that has accomplished that objective within our system. The establishment of a new hospital, a joint venture between Mayo Clinic and Sheikh Shakhbout Medical City (SSMC) in Abu Dhabi, provided a unique opportunity to share this care review process internationally.

During a baseline measurement period, only 78.3% of mortality reviews at SSMC were completed within 45 days, 16.7 percentage points below the target of 95%. A collaboration between SSMC and Mayo Clinic aimed to accelerate the design and implementation of a care review process system. Collaboration was constrained by travel restrictions imposed by COVID-19, language barriers, legal privacy concerns, and differing electronic health records.

Mayo Clinic facilitated a 12-week virtual engagement with SSMC using weekly video meetings, education and training regarding Mayo Clinic’s care review process.

The engagement led to implementation of weekly mortality review huddles, restructuring of the mortality review committee to be multidisciplinary, use of a standardised taxonomy to characterise opportunities to improve care and creation of an education/communication plan regarding identified improvement opportunities using change management strategies.

After the care review process for mortality reviews was instituted, SSMC achieved and sustained a target of 100% of mortality reviews completed within 45 days. The new process resulted in improved mortality review indicators and provided quality feedback to staff with engagement in performance improvement efforts.

A virtual collaboration led to successful implementation of a care review process and substantial gains in the effectiveness of the quality programme at SSMC. This could serve as a model to assist other organisations, even if in-person engagement is hindered.
An experimental comparison of stand management approaches to sudden oak death: prevention vs. restoration
Authors: Quiroga Gissella B. , Simler-Williamson Allison B. , Frangioso Kerri M. , Frankel Susan J. , Rizzo David M. , Cobb Richard C.
Published: May 2023

Many coastal forests stretching from central California to southwest Oregon are threatened or have been impacted by the invasive forest pathogen Phytophthora ramorum, the cause of sudden oak death. We analyzed a set of stand-level forest treatments aimed at preventing or mitigating disease impacts on stand composition, biomass, and fuels using a before–after-control-intervention experiment with a re-evaluation after 5 years. We compared the effects of restorative management for invaded stands and preventative treatments for uninvaded forests with two stand-level experiments. The restorative treatments contrasted two approaches to mastication, hand-crew thinning, and thinning with pile burning with untreated controls replicated at three distinct sites ( N = 30), while the preventative treatments were limited to hand-crew thinning ( N = 10) conducted at a single site. Half of the restoration treatments had basal sprouts removed 2 and 4 years after treatment. All treatments significantly reduced stand density and increased average tree size without significantly decreasing total basal area, both immediately and 5 years after treatments. Preventative treatments did not reduce the basal area or density of timber species not susceptible to P. ramorum, suggesting the relative dominance of these species increased in accordance with host removal. Follow-up basal sprout removal in the restoration experiment appears to maintain treatment benefits for average tree size and may be associated with small decreases in stand density 5 years after initial treatment. Our study demonstrates that for at least 5 years, a range of common stand management practices can improve forest conditions threatened or impacted by sudden oak death.
The Essence of University Entrepreneurial Orientation and its Measurement
Authors: Pacheco Ana , Ferreira João , Simões Jorge
Published: May 2023

Despite progress in recognising the popularity of explaining a university’s entrepreneurial orientation (EO), the literature still lacks clear and systematic means of evaluating and measuring university EO. This study seeks to assess the different ways of measuring university EO and develop recommendations for future research to address this gap. Based on a systematic EO review of empirical research were identified three thematic groups: (i) EO and academic entrepreneurship, (ii) EO and entrepreneurial universities, and (iii) EO and academic spinoffs. These results allow for a greater understanding of the research topics and develop an integrated framework, thus helping to measure and refine operationalisations of EO that can help formulate future quantitative studies and increase the empirical accuracy of the field.
PACMMOD V1 N1 Editorial - Welcome to PACMMOD
Authors: Agrawal Divyakant , Amer-Yahia Sihem , Candan K. Selcuk
Published: May 2023

We are excited to welcome you to Volume 1 of a brand new journal - Proceedings of the ACM on Management of Data, PACMMOD. This new journal is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data and will include articles reporting cutting-edge data management, data engineering, and data science research. Articles published at PACMMOD address data challenges at various stages of the data lifecycle, from modeling, acquisition, cleaning, integration, indexing, querying, analysis, exploration, visualization, interpretation, and explanation. They focus on data-intensive components of data pipelines; and solve problems in areas of interest to our community (e.g., data curation, optimization, performance, storage, systems), operating within accuracy, privacy, fairness, and diversity constraints. Articles reporting deployed systems and solutions to data science pipelines and/or fundamental experiences and insights from evaluating real-world data engineering problems are especially encouraged.
A New Sparse Data Clustering Method Based On Frequent Items
Authors: Huang Qiang , Luo Pingyi , Tung Anthony K. H.
Published: May 2023

Large, sparse categorical data is a natural way to represent complex data like sequences, trees, and graphs. Such data is prevalent in many applications, e.g., Criteo released a terabyte size click log data of 4 billion records with millions of dimensions. While most existing clustering algorithms like k-Means work well on dense, numerical data, there exist relatively few algorithms that can cluster sets of sparse categorical features.

In this paper, we propose a new method called k-FreqItems that performs scalable clustering over high-dimensional, sparse data. To make clustering results easily interpretable, k-FreqItems is built upon a novel sparse center representation called FreqItem which will choose a set of high-frequency, non-zero dimensions to represent the cluster. Unlike most existing clustering algorithms, which adopt Euclidean distance as the similarity measure, k-FreqItems uses the popular Jaccard distance for comparing sets.

Since the efficiency and effectiveness of k-FreqItems are highly dependent on an initial set of representative seeds, we introduce a new randomized initialization method, SILK, to deal with the seeding problem of k-FreqItems. SILK uses locality-sensitive hash (LSH) functions for oversampling and identifies frequently co-occurred data in LSH buckets to determine a set of promising seeds, allowing k-FreqItems to converge swiftly in an iterative process. Experimental results over seven real-world sparse data sets show that the SILK seeding is around 1.1\sim3.2× faster yet more effective than the state-of-the-art seeding methods. Notably, SILK scales up well to a billion data objects on a commodity machine with 4 GPUs. The code is available at https://github.com/HuangQiang/k-FreqItems.
Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries
Authors: Liu Shang , Cong Gao , Feng Kaiyu , Gu Wanli , Zhang Fuzheng
Published: May 2023

Geo-textual objects with both geographical location and textual description are gaining in prevalence. Over the past decades, substantial research has been conducted on spatial keyword queries, which integrate location into keyword-based querying of geo-textual content. However, existing proposals mostly focus on efficiency for processing spatial keyword queries, and little effort was made to address the effectiveness perspectives.

In this work, using two datasets with ground truth query results, we evaluate the effectiveness of standard spatial keyword queries. Our evaluation results show that the TkQ query that ranks objects by a weighted combination of spatial proximity and text relevance is the most effective. Motivated by the finding, we propose a Deep relevance with Weight learning (DrW) model to further improve the effectiveness of the retrieval ranking. DrW is featured with two novel ideas: First, we propose a neural network architecture to learn the text relevance matching over the local interaction between the query and geo-textual objects. Second, we find that a query-dependent weight to balance text relevance and spatial proximity in ranking can improve effectiveness, and we develop a learning-based method to learn the query-dependent weight. Experimental results reveal that our model outperforms state-of-the-art methods on effectiveness, with improvements up to 32.15%, 32.34%, and 33.00% in terms of NDCG@3, NDCG@5, and MRR.
Composite Object Normal Forms: Parameterizing Boyce-Codd Normal Form by the Number of Minimal Keys
Authors: Zhang Zhuoxing , Chen Wu , Link Sebastian
Published: May 2023

We parameterize schemata in Boyce-Codd Normal Form (BCNF) by the number n of minimal keys they exhibit. We show that n quantifies a trade-off between access variety and update complexity. Indeed, access variety refers to the number of different ways by which every entity over the schema is represented uniquely, while update complexity refers to the number of attribute sets for which uniqueness needs to be preserved during updates. As normalization aims at minimizing the level of effort required to preserve data consistency during updates, we establish an algorithm that returns a lossless, dependency-preserving 3NF decomposition where the subset of output schemata not in BCNF is minimized and redundant BCNF schemata are eliminated from the highest to the lowest n exhibited. In particular, if a lossless, dependency-preserving BCNF decomposition exists, our algorithm returns one where the maximum n across all output schemata is minimized. Experiments with synthetic and real-world data quantify the impact of n on the update and query performance over schemata in BCNF with n minimal keys, and show insight into the efficacy of our algorithm suite.
EAR-Oracle: On Efficient Indexing for Distance Queries between Arbitrary Points on Terrain Surface
Authors: Huang Bo , Wei Victor Junqiu , Wong Raymond Chi-Wing , Tang Bo
Published: May 2023

Due to the advancement of geo-positioning technology, the terrain data has become increasingly popular and has drawn a lot of research effort from both academia and industry. The distance computation on the terrain surface is a fundamental and important problem that is widely applied in geographical information systems and 3D modeling. As could be observed from the existing studies, online computation of the distance on the terrain surface is very expensive. All existing index-based methods are only efficient under the case where the distance query must be performed among a small set of predefined points-of-interest known apriori. But, in general cases, they could not scale up to sizable datasets due to their intolerable oracle building time and space consumption.

In this paper, we studied the arbitrary point-to-arbitrary point distance query on the terrain surface in which no assumption is imposed on the query points, and the distance query could be performed between any two arbitrary points. We propose an indexing structure, namely Efficient Arbitrary Point-to-Arbitrary Point Distance Oracle (EAR-Oracle), with theoretical guarantee on the accuracy, oracle building time, oracle size and query time. Our experiments demonstrate that our oracle enjoys excellent scalability and it scales up to enormous terrain surfaces but none of the existing index-based methods could be able to. Besides, it significantly outperforms all existing online computation methods by orders of magnitude in terms of the query time.
A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
Authors: Ahuja Ritesh , Zeighami Sepanta , Ghinita Gabriel , Shahabi Cyrus
Published: May 2023

Several "data-for-good" projects [1, 5, 12] initiated by major companies (e.g., Meta, Google) release to the public spatio-temporal datasets to benefit COVID-19 spread modeling [17, 47, 64] and understand human mobility [14, 24]. Most often, spatio-temporal data are provided in the form of snapshot high resolution population density information, where the released statistics capture population counts in small areas for short time periods. Since high resolution is required for utility (e.g., in modeling COVID hotspots) privacy risks are elevated. To prevent malicious actors from using the data to infer sensitive details about individuals, the released datasets must be first sanitized. Typically, [1, 5, 7, 12], differential privacy (DP) is employed as protection model, due to its formal protection guarantees that prevent an adversary to learn whether a particular individual's data has been included in the release or not.
Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences
Authors: Ping Haoyue , Stoyanovich Julia
Published: May 2023

It remains an open question how to determine the winner of an election when voter preferences are incomplete or uncertain. One option is to assume some probability space over the voting profile and select the Most Probable Winner (MPW) -- the candidate or candidates with the best chance of winning. In this paper, we propose an alternative winner interpretation, selecting the Most Expected Winner (MEW) according to the expected performance of the candidates.

We separate the uncertainty in voter preferences into the generation step and the observation step, which gives rise to a unified voting profile combining both incomplete and probabilistic voting profiles. We use this framework to establish the theoretical hardness of MEW over incomplete voter preferences, and then identify a collection of tractable cases for a variety of voting profiles, including those based on the popular Repeated Insertion Model (RIM) and its special case, the Mallows model. We develop solvers customized for various voter preference types to quantify the candidate performance for the individual voters, and propose a pruning strategy that optimizes computation. The performance of the proposed solvers and pruning strategy is evaluated extensively on real and synthetic benchmarks, showing that our methods are practical.
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation
Authors: Wang Fang , Yan Xiao , Yiu Man Lung , LI Shuai , Mao Zunyao , Tang Bo
Published: May 2023

Fast query execution requires learning-based cardinality estimators to have short inference time (as model inference time adds to end-to-end query execution time) and high estimation accuracy (which is crucial for finding good execution plan). However, existing estimators cannot meet both requirements due to the inherent tension between model complexity and estimation accuracy. We propose a novel Learning-based Progressive Cardinality Estimator (LPCE), which adopts a query re-optimization methodology. In particular, LPCE consists of an initial model (LPCE-I), which estimates cardinality before query execution, and a refinement model (LPCE-R), which progressively refines the cardinality estimations using the actual cardinalities of the executed operators. During query execution, re-optimization is triggered if the estimations of LPCE-I are found to have large errors, and more efficient execution plans are selected for the remaining operators using the refined estimations provided by LPCE-R. Both LPCE-I and LPCE-R are light-weight query-driven estimators but they achieve both good efficiency and high accuracy when used jointly. Besides designing the models for LPCE-I and LPCE-R, we also integrate re-optimization and LPCE into PostgreSQL, a popular database engine. Extensive experiments show that LPCE yields shorter end-to-end query execution time than state-of-the-art learning-based estimators.
dbET: Execution Time Distribution-based Plan Selection
Authors: Li Yifan , Yu Xiaohui , Koudas Nick , Lin Shu , Sun Calvin , Chen Chong
Published: May 2023

While selecting the execution plan for a given query based on a single estimated cost is a generally-adopted strategy, it is usually error-prone and fails to comprehensively profile the plan performance. In this work, we complement existing plan selection methods by proposing a new approach named ET, which produces execution time distributions for query plans utilizing conformal predictions. We develop dbET, a framework that integrates ET into an existing DBMS, requiring no modification to the DBMS and only incurring minor overhead to query processing. Based on the execution time distribution, we design several intuitive yet fundamental query execution objectives and devise the corresponding plan selection strategies. Our experiments on several widely-adopted benchmarks showcase that our design significantly improves the capability of DBMSs in achieving the designated objectives.
LiteHST: A Tree Embedding based Method for Similarity Search
Authors: Zeng Yuxiang , Tong Yongxin , Chen Lei
Published: May 2023

Similarity search is getting increasingly useful in real applications. This paper focuses on the in-memory similarity search, i.e., the range query and k nearest neighbor (kNN) query, under arbitrary metric spaces, where the only known information is the distance function to measure the similarity between two objects. Although lots of research has studied this problem, the query efficiency of existing solutions is still unsatisfactory. To further improve the query efficiency, we are inspired by the tree embeddings, which map each object into a unique leaf of a well-structured tree solely based on the distances. Unlike existing embedding techniques (e.g., Lipschitz embeddings and pivot mapping) for similarity search, where an extra multi-dimensional index is needed to index the embedding space (e.g., Lp metrics), we directly use this tree to answer similarity search. This seems to be promising, but it is challenging to tailor tree embeddings for efficient similarity search. Specifically, we present a novel index called LiteHST, which is based on the most popular tree embedding (HST) and heavily customized for similarity search in the node structure and storage scheme. We propose a new construction algorithm with lower time complexity than existing methods and prove the optimality of LiteHST in the distance bound. Based on this new index, we also design optimization techniques that heavily reduce the number of distance computations and hence save running time. Finally, extensive experiments demonstrate that our solution outperforms the state-of-the-art in the query efficiency by a large margin.
Optimizing Tensor Programs on Flexible Storage
Authors: Schleich Maximilian , Shaikhha Amir , Suciu Dan
Published: May 2023

Tensor programs often need to process large tensors (vectors, matrices, or higher order tensors) that require a specialized storage format for their memory layout. Several such layouts have been proposed in the literature, such as the Coordinate Format, the Compressed Sparse Row format, and many others, that were especially designed to optimally store tensors with specific sparsity properties. However, existing tensor processing systems require specialized extensions in order to take advantage of every new storage format. In this paper we describe a system that allows users to define flexible storage formats in a declarative tensor query language, similar to the language used by the tensor program. The programmer only needs to write storage mappings, which describe, in a declarative way, how the tensors are laid out in main memory. Then, we describe a cost-based optimizer that optimizes the tensor program for the specific memory layout. We demonstrate empirically significant performance improvements compared to state-of-the-art tensor processing systems.
LinCQA: Faster Consistent Query Answering with Linear Time Guarantees
Authors: Fan Zhiwei , Koutris Paraschos , Ouyang Xiating , Wijsen Jef
Published: May 2023

Most data analytical pipelines often encounter the problem of querying inconsistent data that violate pre-determined integrity constraints. Data cleaning is an extensively studied paradigm that singles out a consistent repair of the inconsistent data. Consistent query answering (CQA) is an alternative approach to data cleaning that asks for all tuples guaranteed to be returned by a given query on all (in most cases, exponentially many) repairs of the inconsistent data. In this paper, we identify a class of acyclic select-project-join (SPJ) queries for which CQA can be solved via SQL rewriting with a linear time guarantee. Our rewriting method can be viewed as a generalization of Yannakakis' algorithm for acyclic joins to the inconsistent setting. We present LinCQA, a system that takes as input any query in our class and outputs rewritings in both SQL and non-recursive Datalog with negation. We show that LinCQA often outperforms the existing CQA systems on both synthetic and real-world workloads, and in some cases, by orders of magnitude.