bluuebunny/crossref_metadata_2025 huggingface dataset:
Direct Measurements of Vortex-Induced Vibrations on a Horizontal Flexible Cylinder in Water
Authors: Cenci Fredi , de Lima Gontarski Aline Leal , Felippe Chame Maria Eduarda , Peri Machado dos Santos Cláudia Martins , Trentin Gonçalves Rodolfo , Condino Fujarra André Luís
Published: January 2024

This work presents experimental results on vortex-induced vibration (VIV) of a horizontal cylinder with 2.69 m in length, 21 mm in diameter, and a mass ratio equal to 3.9. The experiments were carried out in a towing tank, where the flexible cylinder was towed under constant current profiles. Twenty reduced velocities were tested in a range of 1.75 < Vr < 15.79, corresponding to Reynolds numbers from 1000 to 10,000. The displacements were measured directly through a submerged optical system that simultaneously acquired the in-line and cross-flow movements of 18 reflective targets along the length. The extensive monitored section of the model enabled the application of a modal decomposition technique, which is a methodology that is still relatively unexplored through direct measurements. A comprehensive analysis was applied and the VIV responses related to amplitudes, frequencies, phase angles, and trajectories are shown and discussed in depth. The overall dynamics exhibits a multimodal response behavior, and the modal decomposition procedure has conclusively confirmed that the total amplitude comprises several modes vibrating at the dominant mode frequency. Root mean square values of response amplitude were up to 0.77D and 0.23D in the cross-flow and in-line directions, respectively. The cross-flow synchronization had a significant impact on the in-line excitation modes, and the trajectories showed coherence between motions.
A Hybrid Computer Vision and Machine Learning Approach for Robust Vortex Core Detection in Fluid Mechanics Applications
Authors: Amran Abolholl Hazem Ashor , Teschner Tom-Robin , Moulitsas Irene
Published: January 2024

Vortex core detection remains an unsolved problem in the field of experimental and computational fluid dynamics. Available methods such as the Q, delta, and swirling strength criterion are based on a decomposed velocity gradient tensor but detect spurious vortices (false positives and false negatives), making these methods less robust. To overcome this, we propose a new hybrid machine learning approach in which we use a convolutional neural network to detect vortex regions within surface streamline plots and an additional deep neural network to detect vortex cores within identified vortex regions. Furthermore, we propose an automatic labeling approach based on K-means clustering to preprocess our input images. We show results for two classical test cases in fluid mechanics: the Taylor–Green vortex problem and two rotating blades. We show that our hybrid approach is up to 2.6 times faster than a pure deep neural network-based approach and furthermore show that our automatic K-means clustering labeling approach achieves within 0.45% mean square error of the more labour-intensive, manual labeling approach. At the same time, by using a sufficient number of samples, we show that we are able to reduce false positives and negatives entirely and thus show that our hybrid machine learning approach is a viable alternative to currently used vortex detection tools in fluid mechanics applications.
Androgen exposure impairs neutrophil maturation and function within the infected kidney
Authors: Hreha Teri N. , Collins Christina A. , Cole Elisabeth B. , Jin Rachel J. , Hunstad David A.
Published: January 2024

Although urinary tract infections (UTIs) predominantly occur in women, male UTIs carry an increased risk of morbidity and mortality. Pyelonephritis in androgen-exposed mice features robust neutrophil recruitment and abscess formation, while bacterial load remains consistently high. Here, we demonstrate that during UTI, neutrophils infiltrating the urinary tract of androgen-exposed mice exhibit reduced maturation, and those that have infiltrated the kidney have reduced phagocytic and degranulation functions, limiting their ability to effectively control infection. This work helps to elucidate mechanisms by which androgens enhance UTI susceptibility and severity, illuminating why male patients may be predisposed to severe outcomes of pyelonephritis.
Paucisymptomatic post COVID-19 myocarditis in a young athlete during return to play workflow: possible usefulness of global longitudinal strain analysis
Authors: De Masi De Luca Gabriele , Papadia Paola , Palamà Zefferino , Coluccia Giovanni
Published: January 2024

A young competitive athlete undergoes the diagnostic investigations protocol before returning to competitive practice (return to play protocol) after COVID-19 infection. Despite the paucisymptomatic presentation of COVID-19 infection and the absence of relevant anomalies in standard first-level diagnostic investigations, echocardiographic examination findings especially speckle tracking analysis (global longitudinal strain) along with some clinical aspects suggested further second-level investigations eventually allowing the identification of inflammatory myocardial damage.
Ceftriaxone-induced encephalopathy in a patient with a normal renal function
Authors: Zarauskas Audrius , Rodrigues Bruno , Alvarez Vincent
Published: January 2024

Ceftriaxone-induced encephalopathy is an exceptionally rare adverse effect of this commonly used cephalosporin and is generally observed in patients undergoing haemodialysis or suffering from severe renal failure. We present a case of a fit woman in her mid-80s with a normal renal function who developed severe fluctuating neurological symptoms (aphasia, loss of contact, chorea-like tongue movements) while being treated with ceftriaxone for a urinary tract infection with bacteraemia. The symptoms began on day 4 of treatment and an adverse drug reaction was suspected on day 7, after exhaustive investigations failed to reveal another cause. A complete recovery was observed 3 days after discontinuing ceftriaxone. Our case highlights the need to consider the diagnosis of ceftriaxone encephalopathy, even if the traditional risk factors are lacking. In this article, we also provide a brief overview of the pathophysiology as well as a literature review concerning the subject.
Perinatal depression and risk of mortality: nationwide, register based study in Sweden
Authors: Hagatulah Naela , Bränn Emma , Oberg Anna Sara , Valdimarsdóttir Unnur A , Shen Qing , Lu Donghao
Published: January 2024

Objective

To determine whether women with perinatal depression are at an increased risk of death compared with women who did not develop the disorder, and compared with full sisters.

Design

Nationwide, register based study.

Setting

Swedish national registers, 1 January 2001 to 31 December 2018.

Participants

86 551 women with a first ever diagnosis of perinatal depression ascertained through specialised care and use of antidepressants, and 865 510 women who did not have perinatal depression were identified and matched based on age and calendar year at delivery. To address familial confounding factors, comparisons were made between 270 586 full sisters (women with perinatal depression (n=24 473) and full sisters who did not have this disorder (n=246 113)), who gave at least one singleton birth during the study period.

Main outcome measures

Primary outcome was death due to any cause. Secondary outcome was cause specific deaths (ie, unnatural and natural causes). Multivariable Cox regression was used to estimate hazard ratios of mortality comparing women with perinatal depression to unaffected women and sisters, taking into account several confounders. The temporal patterns of perinatal depression and differences between antepartum and postpartum onset of perinatal depression were also studied.

Results

522 deaths (0.82 per 1000 person years) were reported among women with perinatal depression diagnosed at a median age of 31.0 years (interquartile range 27.0 to 35.0) over up to 18 years of follow-up. Compared with women who did not have perinatal depression, women with perinatal depression were associated with an increased risk of death (adjusted hazard ratio 2.11 (95% confidence interval 1.86 to 2.40)); similar associations were reported among women who had and did not have pre-existing psychiatric disorder. Risk of death seemed to be increased for postpartum than for antepartum depression (hazard ratio 2.71 (95% confidence interval 2.26 to 3.26)
v
1.62 (1.34 to 1.94)). A similar association was noted for perinatal depression in the sibling comparison (2.12 (1.16 to 3.88)). The association was most pronounced within the first year after perinatal depression but remained up to 18 years after start of follow up. An increased risk was associated with both unnatural and natural causes of death among women with perinatal depression (4.28 (3.44 to 5.32)
v
(1.38 (1.16 to 1.64)), with the strongest association noted for suicide (6.34 (4.62 to 8.71)), although suicide was rare (0.23 per 1000 person years).

Conclusions

Even when accounting for familial factors, women with clinically diagnosed perinatal depression were associated with an increased risk of death, particularly during the first year after diagnosis and because of suicide. Women who are affected, their families, and health professionals should be aware of these severe health hazards after perinatal depression.
Undisclosed financial conflicts of interest in DSM-5-TR: cross sectional analysis
Authors: Davis Lauren C , Diianni Alexa T , Drumheller Sydney R , Elansary Noha N , D’Ambrozio Gianna N , Herrawi Farahdeba , Piper Brian J , Cosgrove Lisa
Published: January 2024

Objective

To assess the extent and types of financial ties to industry of panel and task force members of the American Psychiatric Association’s
Diagnostic and Statistical Manual of Mental Disorders
, fifth edition, text revision (DSM-5-TR), published in 2022.

Design

Cross sectional analysis.

Setting

Open Payments database, USA.

Participants

92 physicians based in the US who served as members of either a panel (n=86) or task force (n=6) on the DSM-5-TR with information recorded in the Centers for Medicare and Medicaid Services Open Payments database during 2016-19. This period was chosen to include the year that development of the DSM-5-TR began and the three years preceding, a time consistent with previous research on conflicts of interest and consistent with the American Psychiatric Association’s disclosure requirements for the fifth revision (DSM-5) of the manual.

Main outcome measures

Type and amount of compensation the panel and task force members of DSM-5-TR received during 2016-19.

Results

After duplicate names had been removed, 168 individuals were identified who served as either panel or task force members of the DSM-5-TR. 92 met the inclusion criteria of being a physician who was based in the US and therefore could be included in Open Payments. Of these 92 individuals, 55 (60%) received payments from industry. Collectively, these panel members received a total of $14.2m (£11.2m; €13m). One third (33.3%) of the task force members had payments reported in Open Payments.

Conclusions

Conflicts of interest among panel members of DSM-5-TR were prevalent. Because of the enormous influence of diagnostic and treatment guidelines, the standards for participation on a guideline development panel should be high. A rebuttable presumption should exist for the
Diagnostic and Statistical Manual of Mental Disorders
to prohibit conflicts of interest among its panel and task force members. When no independent individuals with the requisite expertise are available, individuals with associations to industry could consult to the panels, but they should not have decision making authority on revisions or the inclusion of new disorders.
Assessment of urine colour using a wallet card: a randomised study of a novel patient self-care tool during chemoradiation for oesophageal cancer
Authors: Preti Beatrice , Wang Cathy , Dindial Nicole , Malik Shiraz , Rieger Susan , Black Morgan , Sachdeva Robin , Sanatani Michael
Published: January 2024

Background

Patients undergoing neoadjuvant chemoradiation for oesophageal cancer often experience dehydration from decreased fluid intake and increased losses. Despite frequent clinical visits during treatment, patients can still present with dehydration, suggesting the need for increased patient awareness and engagement around adverse event management at home. Evidence for benefits of self-monitoring may help motivate patients to engage proactively in their own care to improve their treatment experience.

Methods

We performed a randomised single-centre study of a urine colour self-monitoring card (UCC) during chemoradiation therapy for oesophageal cancer, compared with standard dietitian counselling. Primary outcome was self-efficacy as determined by the Self-Management Resource Centre Self-Efficacy for Managing Chronic Disease Scale (SMCD). Secondary outcomes included Burge thirst scores, Edmonton Symptom Assessment System scores (ESAS), patient-initiated hydrations, creatinine rise and satisfaction with the UCC.

Results

Thirty-five patients were randomised. UCC use was not associated with improved SMCD or ESAS scores compared with standard counselling. The card was highly rated by patients as a welcome tool for self-monitoring.

Conclusions

No beneficial effect on self-efficacy or dehydration markers with UCC use was demonstrated. The study nonetheless drew attention to several factors potentially hindering its use for effective self-care: the unexpected severity of other symptoms consuming patients’ attention, reduced sensitivity of urine colour due to chemotherapy, absence of active inquiry by the healthcare team and the inconvenient location of the UCC in wallet/purse. Urine colour monitoring in patients with oesophageal cancer to improve the patient experience during treatment warrants further study but supported by active healthcare provider inquiry, more accessible format of the card, and possibly home vital checks to increase its sensitivity in the clinical context.
Heated and humidified high flow therapy (HHHFT) in extreme and very preterm neonates with respiratory distress syndrome (RDS): a retrospective cohort from a tertiary care setting in Pakistan
Authors: Kessani Vikram Kumar , Hemani Fatima , Ali Iqrar , Memon Sana , Soomro Albar , Zaheer Rija , Das Jai K , Haque Khalid N , Ali Syed Rehan
Published: January 2024

Objective

To determine the role of heated humidified high flow therapy (HHHFT) as primary respiratory support in spontaneously breathing moderate-late, very and extreme preterm neonates with respiratory distress syndrome (RDS) at a tertiary care hospital from a developing country.

Design

Retrospective cohort study.

Setting

Neonatal intensive care unit of Indus Hospital and Health Network, Karachi, Pakistan.

Patients

All preterm neonates with RDS and who received HHHFT as primary respiratory support were included retrospectively, while neonates with orofacial anomalies, congenital heart and lung diseases other than RDS, abdominal wall defects, encephalopathy, congenital pneumonia and received continuous positive airway pressure or invasive ventilation were excluded.

Interventions

HHHFT as primary respiratory support for RDS.

Main outcome measures

Effectiveness, duration, failure rate and complications of HHHFT as a primary respiratory support in moderate-late, very and extremely preterm neonates were evaluated.

Results

The cohort included 138 neonates during a period of 12 months. The median gestational age was 32 weeks, and the median birth weight was 1607 g. Grade 1–2 RDS was seen in 97%, surfactant instillation was done in 10.8% and HHHFT was provided in all the neonates as primary respiratory support. The total duration of HHHFT support was <1 week in 94% of neonates. Bronchopulmonary dysplasia and pneumothorax until discharge or death were observed in one neonate, haemodynamically significant Patent Ductus Artriosus (HsPDA) in two neonates and intraventricular haemorrhage Grade ≥2 in five neonates, while only one neonate died.

Conclusion

This study appears to show that HHHFT is a simple, safe, efficient and cheap mode of primary respiratory support that can be given to spontaneously breathing moderate-late, very and extremely preterm neonates with RDS, especially in low- or middle-income countries.
Flow diverters in the treatment of intracranial dissecting aneurysms: a systematic review and meta-analysis of safety and efficacy
Authors: Amoukhteh Melika , Hassankhani Amir , Valizadeh Parya , Jannatdoust Payam , Ghozy Sherief , Kobeissi Hassan , Kallmes David F
Published: January 2024

Background

Intracranial dissecting aneurysms present clinical challenges, demanding immediate intervention due to their high bleeding risk. While traditional treatments have limitations, the potential of flow diverters shows promise but remains a subject of ongoing debate for optimal management. The aim of this study was assess the safety and efficacy of flow diverters in the treatment of intracranial dissecting aneurysms.

Methods

A systematic review and meta-analysis was performed following established guidelines. The search encompassed PubMed, Scopus, Web of Science, and Embase databases up to July 20, 2023. Eligible studies reporting outcomes of interest were included, and relevant data were extracted and analyzed using R software.

Results

The analysis, based on data pooled from 20 included studies involving 329 patients, revealed a favorable functional outcome rate of 89.7% at the last follow-up. The mortality rate during the follow-up period was 2.4%, decreasing to 0.9% when excluding the outlier study. In the final angiographic follow-up, a complete occlusion rate of 71.7% and an adequate occlusion rate of 88.3% were observed. Notably, studies with longer angiographic follow-up times exhibited lower rates of complete (P=0.02) and adequate (P<0.01) occlusion. A minimal aneurysm recurrence/rebleeding rate of 0.1% was noted, while in-stent stenosis/thrombosis occurred at a rate of 1.14%. Additionally, ischemic events/infarctions were seen in 3.3% of cases. The need for retreatment was minimal, with a rate of 0.9%, and the technical success rate was impressively high at 99.1%.

Conclusion

This study highlights the safety and efficacy of flow diverters in treating intracranial dissecting aneurysms. Further research, encompassing larger multicenter studies with extended follow-up periods, is crucial for comprehending occlusion dynamics, refining treatment strategies, improving long-term outcomes, and addressing methodological limitations.
Clinical trajectories of hand function impairment in systemic sclerosis: an unmet clinical need across disease subsets
Authors: De Lorenzis Enrico , Kakkar Vishal , Di Donato Stefano , Wilson Michelle , Barnes Theresa , Denton Chris , Derrett-Smith Emma , Douglas Karen , Helliwell Philip , Herrick Ariane L , Saleem Benazir , Nisar Muhammad , Morley Catherine , Green Lorraine , Alcacer-Pitarch Begonya , Del Galdo Francesco
Published: January 2024

Background

Hand involvement is an early manifestation of systemic sclerosis (SSc), culprit of diagnosis and classification, and recognised major driver of disability. Impairment of hand function burdens both limited and diffuse cutaneous subsets and therefore could be targeted as ‘basket’ endpoint in SSc. Nevertheless, its natural history in current standard of care is not well characterised, limiting the design of targeted trials. The aim of this study is to describe prevalence, natural history and clinical factors associated with hand function deterioration in a longitudinal, multicentre, observational SSc cohort.

Methods

Hand function was captured through the validated Cochin Hand Function Scale in patients consecutively enrolled in a multicentre observational study and observed over 24 months. Minimal clinically important differences and patient acceptable symptom state were analysed as previously described.

Results

Three hundred and ninety-six consecutive patients were enrolled from 10 centres; 201 with complete follow-up data were included in the analysis. Median (IQR) disease duration was 5 (2–11) years. One hundred and five (52.2%) patients reported clinically significant worsening. Accordingly, the proportion of patients reporting unacceptable hand function increased over 2 years from 27.8% to 35.8% (p<0.001). Least absolute shrinkage and selection operator analysis identified male gender, disease subset, Raynaud’s Condition Score, tenosynovitis and pain, as some of the key factors associated with worsening hand involvement.

Conclusions

Hand function deteriorates over time in more than 50% of SSc patients despite available therapies. The analysis of factors associated with hand function worsening supports the involvement of both inflammation, vascular and fibrotic processes in hand involvement, making it a hallmark clinical manifestation of SSc. Our data are poised to inform the design of intervention studies to target this major driver of disability in SSc.
Frequency of irritable bowel syndrome in spondyloarthritis: a multicentric cross-sectional study and meta-analysis
Authors: Bernard Jessika , Barnetche Thomas , Amory Charlotte , Despres Jerome , Vandersmissen Maxime , Landrin Justine , Gaujoux-Viala Cecile , Lukas Cédric , Ruyssen-Witrand Adeline , Truchetet Marie-Elise , Vergne-Salle Pascale , Mathieu Sylvain , Tournadre Anne
Published: January 2024

Objective

To evaluate the prevalence of symptoms and factors associated with irritable bowel syndrome (IBS) in axial spondyloarthritis (ax-SpA).

Methods

In a cross-sectional multicentric study, consecutive patients with ax-SpA treated with biologics in five rheumatology departments were asked for IBS Rome IV criteria. Demographic data, lifestyle behaviours and disease characteristics were recorded. Second, a systematic literature review and meta-analysis were performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

Of the 500 patients with ax-SpA included, 124 reported IBS symptoms (25%). Female gender, unemployment, higher Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and worse Bath Ankylosing Spondylitis Functional Index scores, multiple lines of biologics, fibromyalgia, anxiety, depression and lower physical activity were associated with IBS symptoms. In multivariate model, the risk of IBS was associated with anxiety and physical inactivity. From the literature review, the prevalence of IBS in patients with SpA was 15.4% (8.8% to 23.3%). Meta-analysis of the five studies comparing the presence of IBS in patients with SpA (323/7292) and healthy controls (484/35587) showed a significant increase of IBS in patients with SpA (OR=1.59 (1.05 to 2.40)).

Conclusion

The prevalence of IBS symptoms was high in the ax-SpA population and should therefore be considered in the presence of gastrointestinal disorders. The presence of IBS symptoms was associated with anxiety and low physical activity in multivariate analysis. Patients with IBS symptoms tended to have more difficult to manage disease characterised by higher activity, worse functional score and multiple lines of treatment in univariate analysis.
Tracheostomies for respiratory failure are associated with a high inpatient mortality: a potential trigger to reconsider goals of care
Authors: Colbert Cameron , Streblow Aaron D , Sherry Scott P , Dobbertin Konrad , Cook Mackenzie
Published: January 2024

Introduction

Acute care surgeons are frequently consulted for tracheostomy placement in the intensive care unit (ICU). Tracheostomy may facilitate ventilator weaning and improve physical comfort. Short-term outcomes after tracheostomy are not well studied. We hypothesize that a high proportion of ICU patients who underwent tracheostomy died prior to discharge. These data will help guide clinical decision-making at a key pivot point in care.

Methods

We identified 177 mixed ICU patients who received a tracheostomy for respiratory failure between January 2013 and December 2018. We excluded patients with trauma. Patient information was collected and comparisons made with univariable and multivariable statistics.

Results

Of the 177 patients who underwent a tracheostomy for respiratory failure, 45% were women, median age was 63 (51–71) years. Of this group 18% died prior to discharge, 63% were discharged to a care facility and only 16% discharged home. Compared with survivors, patients with tracheostomies who died during their admission were older, age 69 (64–76) versus 61 (49–71) years (p<0.01) on univariable analysis. In this model, no single comorbid condition or length of stay (LOS) variable was predictive of death before discharge. A multivariable model controlling for covariation similarly identified age, as well as a longer ICU LOS of 34 (20–49) versus 23 (16–31) days (p=0.003) as factors associated with increased likelihood of death before discharge.

Conclusions

Tracheostomy placement in a mixed ICU population is associated with a nearly 20% inpatient mortality and the vast majority of surviving patients were discharged to a care facility. This suggests that the need for tracheostomy could be considered a trigger for re-evaluation of patient goals. The high risk of death due to underlying illness and high intensity care after their hospitalization emphasize the need for clear advanced care planning discussions around the time of tracheostomy placement.

Level of Evidence

Level IV, Retrospective cohort study.
Impact of spatial and temporal resource distribution on rabies dynamics in the Arctic
Authors: Allibert Agathe , Viard Francois , Sauvé Caroline C. , Simon Audrey , Rees Erin E. , Leighton Patrick A.
Published: January 2024

In the Arctic, rabies is endemic in the Arctic fox ( Vulpes lagopus), posing a significant and ongoing health risk for people and domestic animals. The mechanisms by which rabies is maintained within the low-density fox populations in the Arctic remain unclear. In this study, we developed a spatially explicit individual-based stochastic epidemiological model and performed an uncertainty analysis to better understand Arctic fox rabies dynamics. Rabies persisted in 25.68% of model simulations, with several variables having significant impact on rabies persistence: probability of rabies transmission, spatial and temporal distribution food resources, mean litter size and variability of rabies incubation periods. Where rabies is endemic, we identified 5 key parameters for rabies dynamics: spatiotemporal resource distribution, probability of birth for adult females, mean and standard deviation of litter size, and incubation period of rabies. Our study demonstrates that Arctic rabies can persist in its primary host under conditions consistent with existing empirical data in the literature and showed the important role played by the spatial and temporal distribution of resources. Finally, our results suggest that the ecological impacts of rapid climate warming could decrease the overall persistence of rabies in the Arctic and the associated health risk in Arctic communities.
Teaching to extract spectral densities from lattice correlators to a broad audience of learning-machines
Authors: Buzzicotti Michele , De Santis Alessandro , Tantalo Nazario
Published: January 2024

We present a new supervised deep-learning approach to the problem of the extraction of smeared spectral densities from Euclidean lattice correlators. A distinctive feature of our method is a model-independent training strategy that we implement by parametrizing the training sets over a functional space spanned by Chebyshev polynomials. The other distinctive feature is a reliable estimate of the systematic uncertainties that we achieve by introducing several ensembles of machines, the broad audience of the title. By training an ensemble of machines with the same number of neurons over training sets of fixed dimensions and complexity, we manage to provide a reliable estimate of the systematic errors by studying numerically the asymptotic limits of infinitely large networks and training sets. The method has been validated on a very large set of random mock data and also in the case of lattice QCD data. We extracted the strange-strange connected contribution to the smeared R-ratio from a lattice QCD correlator produced by the ETM Collaboration and compared the results of the new method with the ones previously obtained with the HLT method by finding a remarkably good agreement between the two totally unrelated approaches.
Derivation of effective theories for thin 3D nonlinearly elastic rods with voids
Authors: Friedrich Manuel , Kreutz Leonard , Zemas Konstantinos
Published: January 2024

We derive a dimension-reduction limit for a three-dimensional rod with material voids by means of [Formula: see text]-convergence. Hereby, we generalize the results of the purely elastic setting [M. G. Mora and S. Müller, Derivation of the nonlinear bending-torsion theory for inextensible rods by [Formula: see text]-convergence, Calc. Var. Partial Differential Equations 18 (2003) 287–305] to a framework of free discontinuity problems. The effective one-dimensional model features a classical elastic bending–torsion energy, but also accounts for the possibility that the limiting rod can be broken apart into several pieces or folded. The latter phenomenon can occur because of the persistence of voids in the limit, or due to their collapsing into a discontinuity of the limiting deformation or its derivative. The main ingredient in the proof is a novel rigidity estimate in varying domains under vanishing curvature regularization, obtained in [M. Friedrich, L. Kreutz and K. Zemas, Geometric rigidity in variable domains and derivation of linearized models for elastic materials with free surfaces, preprint (2021), arXiv:2107.10808].
Petal grid diagrams of torus knots
Authors: Lee Eon-Kyung , Lee Sang-Jin
Published: January 2024

A petal diagram of a knot is a projection with a single multi-crossing such that there are no nested loops. The petal number [Formula: see text] of a knot [Formula: see text] is the minimum number of loops among all petal diagrams of [Formula: see text]. Let [Formula: see text] denote the [Formula: see text]-torus knot for relatively prime integers [Formula: see text]. Recently, Kim et al. proved that [Formula: see text] whenever [Formula: see text]. They conjectured that the inequality holds without the assumption [Formula: see text]. They also showed that [Formula: see text] whenever [Formula: see text] and [Formula: see text]. Their proofs construct petal grid diagrams for those torus knots. In this paper, we prove the conjecture that [Formula: see text] holds for any [Formula: see text]. We also show that [Formula: see text] holds for any [Formula: see text]. Our proofs construct petal grid diagrams for any torus knots.
Hilbert coefficients of good I-filtrations of modules
Authors: Dung Le Xuan
Published: January 2024

Let [Formula: see text] be a finitely generated module of dimension [Formula: see text] over a Noetherian local ring [Formula: see text] and [Formula: see text] an [Formula: see text]-primary ideal. Let be a pair of good [Formula: see text]-filtrations [Formula: see text] and [Formula: see text] of [Formula: see text]. We show that the Hilbert coefficients [Formula: see text] are bounded below and above in terms of [Formula: see text], [Formula: see text], and reduction numbers of [Formula: see text] and [Formula: see text], for all [Formula: see text].
Non-Synchronous Measurements Acoustic Imaging Method Based on Capped Nuclear Norm Minimization
Authors: Wei Juan , He Yutao , Ning Weichen
Published: January 2024

In this work, the difference convex function based on capped nuclear norm minimization (CNNM-DC) method, a cross-spectral matrix (CSM) completion iterative algorithm with excellent noise immunity, is proposed to realize acoustic imaging of non-synchronous measurements (NSM) under the condition of low signal-to-noise ratio (SNR). Compared with the CSM completion algorithm based on the nuclear norm minimization (NNM) model, the truncated nuclear norm regularization (TNNR) model, and the weighted nuclear norm minimization (WNNM) model, the proposed method can obtain the acoustic image with narrow main lobes and no sidelobe under the condition of low SNR. The simulation results show that, under conditions of low SNR, the proposed method effectively reduces the width of the main lobe, suppresses the side lobes, and achieves more accurate imaging results than the previous method. Finally, experiments were conducted to verify the feasibility of CNNM-DC. The experimental results show that the proposed method is an accurate acoustic imaging algorithm for NSM under low SNR, which lays a foundation for acoustic imaging in industrial occasions with strong background noise interference.
Unobtrusive Air Leakage Estimation for Earables with In-ear Microphones
Authors: Demirel Berken Utku , Dang Ting , Al-Naimi Khaldoon , Kawsar Fahim , Montanari Alessandro
Published: January 2024

Earables (in-ear wearables) are gaining increasing attention for sensing applications and healthcare research thanks to their ergonomy and non-invasive nature. However, air leakages between the device and the user's ear, resulting from daily activities or wearing variabilities, can decrease the performance of applications, interfere with calibrations, and reduce the robustness of the overall system. Existing literature lacks established methods for estimating the degree of air leaks (i.e., seal integrity) to provide information for the earable applications. In this work, we proposed a novel unobtrusive method for estimating the air leakage level of earbuds based on an in-ear microphone. The proposed method aims to estimate the magnitude of distortions, reflections, and external noise in the ear canal while excluding the speaker output by learning the speaker-to-microphone transfer function which allows us to perform the task unobtrusively. Using the obtained residual signal in the ear canal, we extract three features and deploy a machine-learning model for estimating the air leakage level. We investigated our system under various conditions to validate its robustness and resilience against the motion and other artefacts. Our extensive experimental evaluation shows that the proposed method can track air leakage levels under different daily activities.

"The best computer is a quiet, invisible servant."

~Mark Weiser
Aragorn
Authors: Venugopalan Hari , Din Zainul Abi , Carpenter Trevor , Lowe-Power Jason , King Samuel T. , Shafiq Zubair
Published: January 2024

Mobile app developers often rely on cameras to implement rich features. However, giving apps unfettered access to the mobile camera poses a privacy threat when camera frames capture sensitive information that is not needed for the app's functionality. To mitigate this threat, we present Aragorn, a novel privacy-enhancing mobile camera system that provides fine grained control over what information can be present in camera frames before apps can access them. Aragorn automatically sanitizes camera frames by detecting regions that are essential to an app's functionality and blocking out everything else to protect privacy while retaining app utility. Aragorn can cater to a wide range of camera apps and incorporates knowledge distillation and crowdsourcing to extend robust support to previously unsupported apps. In our evaluations, we see that, with no degradation in utility, Aragorn detects credit cards with 89% accuracy and faces with 100% accuracy in context of credit card scanning and face recognition respectively. We show that Aragorn's implementation in the Android camera subsystem only suffers an average drop of 0.01 frames per second in frame rate. Our evaluations show that the overhead incurred by Aragorn to system performance is reasonable.
Semantic Loss
Authors: Arrotta Luca , Civitarese Gabriele , Bettini Claudio
Published: January 2024

Deep Learning models are a standard solution for sensor-based Human Activity Recognition (HAR), but their deployment is often limited by labeled data scarcity and models' opacity. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate these issues by infusing knowledge about context information into HAR deep learning classifiers. However, existing NeSy methods for context-aware HAR require computationally expensive symbolic reasoners during classification, making them less suitable for deployment on resource-constrained devices (e.g., mobile devices). Additionally, NeSy approaches for context-aware HAR have never been evaluated on in-the-wild datasets, and their generalization capabilities in real-world scenarios are questionable. In this work, we propose a novel approach based on a semantic loss function that infuses knowledge constraints in the HAR model during the training phase, avoiding symbolic reasoning during classification. Our results on scripted and in-the-wild datasets show the impact of different semantic loss functions in outperforming a purely data-driven model. We also compare our solution with existing NeSy methods and analyze each approach's strengths and weaknesses. Our semantic loss remains the only NeSy solution that can be deployed as a single DNN without the need for symbolic reasoning modules, reaching recognition rates close (and better in some cases) to existing approaches.
Effects of Uncertain Trajectory Prediction Visualization in Highly Automated Vehicles on Trust, Situation Awareness, and Cognitive Load
Authors: Colley Mark , Speidel Oliver , Strohbeck Jan , Rixen Jan Ole , Belz Jan Henry , Rukzio Enrico
Published: January 2024

Automated vehicles are expected to improve safety, mobility, and inclusion. User acceptance is required for the successful introduction of this technology. One essential prerequisite for acceptance is appropriately trusting the vehicle's capabilities. System transparency via visualizing internal information could calibrate this trust by enabling the surveillance of the vehicle's detection and prediction capabilities, including its failures. Additionally, concurrently increased situation awareness could improve take-overs in case of emergency. This work reports the results of two online comparative video-based studies on visualizing prediction and maneuver-planning information. Effects on trust, cognitive load, and situation awareness were measured using a simulation (N=280) and state-of-the-art road user prediction and maneuver planning on a pre-recorded real-world video using a real prototype (N=238). Results show that color conveys uncertainty best, that the planned trajectory increased trust, and that the visualization of other predicted trajectories improved perceived safety.
TextureSight
Authors: Wang Xue , Zhang Yang
Published: January 2024

Objects engaged by users' hands contain rich contextual information for their strong correlation with user activities. Tools such as toothbrushes and wipes indicate cleansing and sanitation, while mice and keyboards imply work. Much research has been endeavored to sense hand-engaged objects to supply wearables with implicit interactions or ambient computing with personal informatics. We propose TextureSight, a smart-ring sensor that detects hand-engaged objects by detecting their distinctive surface textures using laser speckle imaging on a ring form factor. We conducted a two-day experience sampling study to investigate the unicity and repeatability of the object-texture combinations across routine objects. We grounded our sensing with a theoretical model and simulations, powered it with state-of-the-art deep neural net techniques, and evaluated it with a user study. TextureSight constitutes a valuable addition to the literature for its capability to sense passive objects without emission of EMI or vibration and its elimination of lens for preserving user privacy, leading to a new, practical method for activity recognition and context-aware computing.
Wall Matters
Authors: Xie Binbin , Cui Minhao , Ganesan Deepak , Xiong Jie
Published: January 2024

Wireless sensing has demonstrated its potential of utilizing radio frequency (RF) signals to sense individuals and objects. Among different wireless signals, LoRa signal is particularly promising for through-wall sensing owing to its strong penetration capability. However, existing works view walls as a "bad" thing as they attenuate signal power and decrease the sensing coverage. In this paper, we show a counter-intuitive observation, i.e., walls can be used to increase the sensing coverage if the RF devices are placed properly with respect to walls. To fully understand the underlying principle behind this observation, we develop a through-wall sensing model to mathematically quantify the effect of walls. We further show that besides increasing the sensing coverage, we can also use the wall to help mitigate interference, which is one well-known issue in wireless sensing. We demonstrate the effect of wall through two representative applications, i.e., macro-level human walking sensing and micro-level human respiration monitoring. Comprehensive experiments show that by properly deploying the transmitter and receiver with respect to the wall, the coverage of human walking detection can be expanded by more than 160%. By leveraging the effect of wall to mitigate interference, we can sense the tiny respiration of target even in the presence of three interferers walking nearby.
Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations
Authors: Tabatabaie Mahan , He Suining
Published: January 2024

Driver maneuver interaction learning (DMIL) refers to the classification task with the goal of identifying different driver-vehicle maneuver interactions (e.g., left/right turns). Existing conventional studies largely focused on the centralized collection of sensor data from the drivers' smartphones (say, inertial measurement units or IMUs, like accelerometer and gyroscope). Such a centralized mechanism might be precluded by data regulatory constraints. Furthermore, how to enable an adaptive and accurate DMIL framework remains challenging due to (i) complexity in heterogeneous driver maneuver patterns, and (ii) impacts of anomalous driver maneuvers due to, for instance, aggressive driving styles and behaviors.

To overcome the above challenges, we propose AF-DMIL, an Anomaly-aware Federated Driver Maneuver Interaction Learning system. We focus on the real-world IMU sensor datasets (e.g., collected by smartphones) for our pilot case study. In particular, we have designed three heterogeneous representations for AF-DMIL regarding spectral, time series, and statistical features that are derived from the IMU sensor readings. We have designed a novel heterogeneous representation attention network (HetRANet) based on spectral channel attention, temporal sequence attention, and statistical feature learning mechanisms, jointly capturing and identifying the complex patterns within driver maneuver behaviors. Furthermore, we have designed a densely-connected convolutional neural network in HetRANet to enable the complex feature extraction and enhance the computational efficiency of HetRANet. In addition, we have designed within AF-DMIL a novel anomaly-aware federated learning approach for decentralized DMIL in response to anomalous maneuver data. To ease extraction of the maneuver patterns and evaluation of their mutual differences, we have designed an embedding projection network that projects the high-dimensional driver maneuver features into low-dimensional space, and further derives the exemplars that represent the driver maneuver patterns for mutual comparison. Then, AF-DMIL further leverages the mutual differences of the exemplars to identify those that exhibit anomalous patterns and deviate from others, and mitigates their impacts upon the federated DMIL. We have conducted extensive driver data analytics and experimental studies on three real-world datasets (one is harvested on our own) to evaluate the prototype of AF-DMIL, demonstrating AF-DMIL's accuracy and effectiveness compared to the state-of-the-art DMIL baselines (on average by more than 13% improvement in terms of DMIL accuracy), as well as fewer communication rounds (on average 29.20% fewer than existing distributed learning mechanisms).
JoulesEye
Authors: Adhikary Rishiraj , Sadeh Maite , Batra Nipun , Goel Mayank
Published: January 2024

Smartphones and smartwatches have contributed significantly to fitness monitoring by providing real-time statistics, thanks to accurate tracking of physiological indices such as heart rate. However, the estimation of calories burned during exercise is inaccurate and cannot be used for medical diagnosis. In this work, we present JoulesEye, a smartphone thermal camera-based system that can accurately estimate calorie burn by monitoring respiration rate. We evaluated JoulesEye on 54 participants who performed high intensity cycling and running. The mean absolute percentage error (MAPE) of JoulesEye was 5.8%, which is significantly better than the MAPE of 37.6% observed with commercial smartwatch-based methods that only use heart rate. Finally, we show that an ultra-low-resolution thermal camera that is small enough to fit inside a watch or other wearables is sufficient for accurate calorie burn estimation. These results suggest that JoulesEye is a promising new method for accurate and reliable calorie burn estimation.
Learning from User-driven Events to Generate Automation Sequences
Authors: Song Yunpeng , Bian Yiheng , Wang Xiaorui , Cai Zhongmin
Published: January 2024

Enabling smart devices to learn automating actions as expected is a crucial yet challenging task. The traditional Trigger-Action rule approach for device automation is prone to ambiguity in complex scenarios. To address this issue, we propose a data-driven approach that leverages recorded user-driven event sequences to predict potential actions users may take and generate fine-grained device automation sequences. Our key intuition is that user-driven event sequences, like human-written articles and programs, are governed by consistent semantic contexts and contain regularities that can be modeled to generate sequences that express the user's preferences. We introduce ASGen, a deep learning framework that combines sequential information, event attributes, and external knowledge to form the event representation and output sequences of arbitrary length to facilitate automation. To evaluate our approach from both quantitative and qualitative perspectives, we conduct two studies using a realistic dataset containing over 4.4 million events. Our results show that our approach surpasses other methods by providing more accurate recommendations. And the automation sequences generated by our model are perceived as equally or even more rational and useful compared to those generated by humans.