bluuebunny/crossref_metadata_2025 huggingface dataset:
Interventions to mitigate reproductive concerns in individuals with cancer: A systematic review
Authors: Hu Li , Xu Binbin , Zhang Linruo , Chau Pui Hing , Wu Chanchan , Choi Edmond Pui Hang
Published: November 2024

Background

Individuals of reproductive age with cancer may experience reproductive concerns (RCs) due to impaired fertility and disrupted family planning, which can negatively impact their quality of life and psychological well‐being. There is limited research on interventions that mitigate the negative effects of RCs among individuals with cancer.

Objective

This systematic review aimed to identify and evaluate the effectiveness of interventions developed to mitigate RCs among individuals with cancer.

Methods

This systematic review was conducted following the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement. A systematic search of nine English and Chinese electronic databases including PubMed, Cochrane Library, CINAHL Plus, Embase, PsycINFO, Web of Science, Wan Fang Data, Chinese National Knowledge Infrastructure (CNKI), and SinoMed, was conducted for relevant studies from inception to November 2023. Intervention studies designed to mitigate RCs among individuals with cancer were included. Two reviewers independently performed study selection, data extraction, and quality appraisal where JBI Critical appraisal tools were used. Narrative syntheses were conducted to summarize the characteristics and effectiveness of interventions due to high heterogeneity across studies.

Results

Nine studies were included. Interventions were categorized into psychoeducational interventions (n = 6), couple‐based interventions facilitating open communication and intimate relationships (n = 2), and mindfulness‐based interventions (n = 1). A statistically significant reduction in RCs was observed in five psychoeducational interventions, two couple‐based interventions, and one mindfulness‐based stress reduction intervention. The effect sizes (Cohen's d) of the interventions on RCs varied substantially from 0.08 to 5.66.

Linking Evidence to Action

Psychoeducation, couple‐based, and mindfulness‐based interventions demonstrated promising findings in mitigating RCs among individuals with cancer. However, more randomized controlled trials with larger sample sizes and rigorous designs are warranted to strengthen the current evidence.
On the degrees of irreducible characters fixed by some field automorphism in finite groups
Authors: Grittini Nicola
Published: November 2024

We prove a variant of the theorem of Ito–Michler, investigating the properties of finite groups where a prime number does not divide the degree of any irreducible character left invariant by some Galois automorphism of order .
Multirow Film-Cooling Effectiveness of Vertically Oriented Slot Cross-Section Diffusion Holes on a Turbine Nozzle Guide Vane Suction Surface
Authors: Hu Jia-Jun , An Bai-Tao
Published: November 2024

In this article, a multirow film-cooling effectiveness experiment was conducted on a turbine nozzle guide vane suction surface using the pressure-sensitive paint (PSP) measurement technique. Five rows of film holes were designed at the suction side with a streamwise staggered arrangement and a 6D spanwise hole spacing. Two diffusion holes constructed based on a vertically oriented slot cross-section with 14-deg and 20-deg exit expansion angles were tested. A fan-shaped hole and a horizontally oriented slot cross-section diffusion hole were chosen as the baselines, and both had a 14-deg exit expansion angle. The experiment was carried out in a linear cascade with a mainstream Reynolds number of 62000, a mainstream turbulence intensity of 3.7%, and a coolant-to-mainstream density ratio of 1.5. The four averaged blowing ratios ranged from 0.5 to 2.0. The results showed that, regardless of the blowing ratio, under intense multirow film interactions, the vertically oriented slot cross-section diffusion holes can always produce uniform and consistent film coverage on the vane suction surface. The vertically oriented slot cross-section widens the distance between the up- and downstream walls and narrows the lateral width, thereby weakening the influence of the entrance “jetting effect.” The resulting flow pattern can adapt to a larger exit expansion angle. In addition to the smaller difference under a low blowing ratio, two vertically oriented slot cross-section diffusion holes yield prominently greater multirow effectiveness than do the horizontally oriented slot cross-section diffusion hole and conventional fan-shaped hole, in which the case with a 20-deg expansion angle performs better.
Scale-Resolving Simulations of Turbulent Flow Retaining the Exact Blade Count With the Time-Inclined Method
Authors: Montiel Miguel , Corral Roque
Published: November 2024

Simulations of the T106 low-pressure turbine linear cascade with incoming wakes have been conducted to demonstrate the suitability of the time-inclined method for retaining the exact blade count in scale-resolving simulations. The time-inclined method has been implemented into a high-order unstructured time-marching code based on the flux reconstruction method. The pitch ratio between the upstream incoming wakes and the cascade is not a small integer, and the time-inclined method is used to reduce simulations to a single-passage computational domain. Three-way comparisons have been generated: the solution of the direct periodic full domain, spanning several blade passages, is compared to the single-passage time-inclined solution and to a single-passage solution that approximates the pitch ratio to the nearest integer. Two sets of virtual experiments, which differ in modeling of the incoming perturbations, are reported. First, the immersed boundary approach is used to introduce a cascade of moving bars that act as the trailing edges of the preceding row and generate incoming wakes. The second set of simulations introduces wake-like perturbations at the inlet section of the computational domain. The present work shows that the time-inclined method, retaining the exact blade count, can produce very accurate results for turbulence-resolving simulations. Moreover, the time-inclined method outperforms the standard single-passage methodology in most of the variables of interest and is equivalent in the rest.
Robustness Analysis of Asymmetric Nonlinear Energy Sink via Nonlinear Normal Modes
Authors: Motato Eliot
Published: November 2024

Nonlinear energy sink (NES) absorbers excel compared to traditional tuned mass dampers (TMD) due to their superior efficiency over a wider range of frequencies. However, despite the advantages of NES over TMDs, their susceptibility to variations in system energy poses a challenge, particularly in applications where the main structure is subjected to random excitations. A modified NES, referred to as Asymmetric NES, significantly enhances NES robustness, expanding its potential uses. This study investigates the factors contributing to the superior robustness of asymmetric nonlinear energy sink (ANES) in comparison to the performance of traditional cubic NES. Through an analysis utilizing nonlinear normal modes (NNM) and frequency energy plots (FEP), this research shows that the improved effectiveness of asymmetric NESs is due to the intricate interaction between their NNM and the primary structure's kinetic energy FEP, leading to more robust targeted energy transfer (TET). In this work, the NNMs of tuned ANES and NES with similar mass are plotted alongside the kinetic energy FEP of a three-story shear building subjected to various earthquake ground accelerograms. The findings obtained here reveal that the NNM of the ANES absorber intersects more effectively with the system's energy FEP inducing a more effective modal energy redistribution, explaining the superior robustness of the ANES against variations in the input spectrum power.
Scattering shadows
Authors: Curtright Thomas , Verma Gaurav
Published: November 2024

We discuss the regions forbidden to classical scattering trajectories by repulsive potentials. We give explicit results for the asymptotic form of these regions, far from the scattering center, in terms of the scattering angle function. We account for divergent total cross sections on the basis of these results.
Comment on “Determining the difference between local acceleration and local gravity: Applications of the equivalence principle to relativistic trajectories” [Am. J. Phys. 92(6), 444–449 (2024)]
Authors: Wang Frank
Published: November 2024

Editor's Note: A straightforward approximate algebraic procedure for introducing locally flat coordinates in a Schwarzschild spacetime is undertaken in this Comment on the recent leading order differential approach elucidated by Steven Balbus in the June 2024 issue. It is very likely that instructors even in special relativity could employ this technique and thereby obtain the correct deflection angle for light in an accelerated frame of reference.
2024 Jackson Award for Excellence in Graduate Physics Education lecture: Physics graduate education for the 21st century
Authors: Singh Chandralekha
Published: November 2024

This article is a summary of my talk for the John David Jackson Excellence in Graduate Physics Education Award. In this article, I reflect upon the challenges and opportunities for physics graduate education in the 21st century that I believe physics educators should embrace.
A broadband modeling method for range-independent underwater acoustic channels using physics-informed neural networks
Authors: Huang Ziwei , An Liang , Ye Yang , Wang Xiaoyan , Cao Hongli , Du Yuchong , Zhang Meng
Published: November 2024

Accurate broadband modeling of underwater acoustic channels is vital for underwater acoustic detection, localization, and communication. Conventional modeling methodologies, based on methods such as the finite element method, finite difference method, and boundary element method, generally facilitate computation for only a single frequency at a time. However, in broadband modeling, this characteristic presents limitations, requiring multiple computations across frequencies, thereby leading to significant time challenges. To solve this problem, we propose a rapid broadband modeling approach using physics-informed neural networks. By integrating the modal equation of normal modes as a regularization term within the neural network's loss function, the method can achieve rapid broadband modeling of underwater acoustic channel with a sparse set of frequency sampling points. Operating in range-independent underwater environments with a liquid semi-infinite seabed, the method proficiently predicts the channel response across the frequency band from 100 to 300 Hz. Compared to the results obtained from KRAKEN, our method improves computational speed by a factor of 25 at a propagation distance of 20 km, while maintaining a mean absolute error of 0.15 dB for the acoustic channel response.
Population connectivity shapes the distribution and complexity of chimpanzee cumulative culture
Authors: Gunasekaram Cassandra , Battiston Federico , Sadekar Onkar , Padilla-Iglesias Cecilia , van Noordwijk Maria A. , Furrer Reinhard , Manica Andrea , Bertranpetit Jaume , Whiten Andrew , van Schaik Carel P. , Vinicius Lucio , Migliano Andrea Bamberg
Published: November 2024

Although cumulative culture is a hallmark of hominin evolution, its origins can be traced back to our common ancestor with chimpanzees. Here, we investigated the evolutionary origins of chimpanzee cumulative culture and why it remained incipient. To trace cultural transmission among the four chimpanzee subspecies, we compared population networks based on genetic markers of recent migration and shared cultural traits. We show that limited levels of group connectivity favored the emergence of a few instances of cumulative culture in chimpanzees. As in humans, cultural complexification likely happened in steps, with transmission between populations, incremental changes, and repurposing of technologies. We propose that divergence in social patterns led to increased mobility between groups in the genus
Homo
, resulting in irreversible dependence on cultural exchange and complexification.
Chemically induced proximity reveals a Piezo-dependent meiotic checkpoint at the oocyte nuclear envelope
Authors: Liu Chenshu , Dernburg Abby F.
Published: November 2024

Sexual reproduction relies on robust quality control during meiosis. Assembly of the synaptonemal complex between homologous chromosomes (synapsis) regulates meiotic recombination and is crucial for accurate chromosome segregation in most eukaryotes. Synapsis defects can trigger cell cycle delays and, in some cases, apoptosis. We developed and deployed a chemically induced proximity system to identify key elements of this quality control pathway in
Caenorhabditis elegans
. Persistence of the polo-like kinase PLK-2 at pairing centers—specialized chromosome regions that interact with the nuclear envelope—induced apoptosis of oocytes in response to phosphorylation and destabilization of the nuclear lamina. Unexpectedly, the Piezo1/PEZO-1 channel localized to the nuclear envelope and was required to transduce this signal to promote apoptosis in maturing oocytes.
Hidden state inference requires abstract contextual representations in the ventral hippocampus
Authors: Mishchanchuk Karyna , Gregoriou Gabrielle , Qü Albert , Kastler Alizée , Huys Quentin J. M. , Wilbrecht Linda , MacAskill Andrew F.
Published: November 2024

The ability to use subjective, latent contextual representations to influence decision-making is crucial for everyday life. The hippocampus is hypothesized to bind together otherwise abstract combinations of stimuli to represent such latent contexts, to support the process of hidden state inference. Yet evidence for a role of the hippocampus in hidden state inference remains limited. We found that the ventral hippocampus is required for mice to perform hidden state inference during a two-armed bandit task. Hippocampal neurons differentiate the two abstract contexts required for this strategy in a manner similar to the differentiation of spatial locations, and their activity is essential for appropriate dopamine dynamics. These findings offer insight into how latent contextual information is used to optimize decisions, and they emphasize a key role for the hippocampus in hidden state inference.
NREM sleep improves behavioral performance by desynchronizing cortical circuits
Authors: Kharas Natasha , Chelaru Mircea I. , Eagleman Sarah , Parajuli Arun , Dragoi Valentin
Published: November 2024

Sleep improves cognitive performance, yet little is known about the neural mechanisms of this improvement. We performed multielectrode recording in macaque visual and dorsolateral prefrontal cortex while animals performed a visual discrimination task before and after non–rapid eye movement (NREM) sleep. Although sleep induces synchronized fluctuations in population activity across cortical areas, the post-sleep population activity became more desynchronized relative to the pre-sleep state. The changes after sleep were correlated with an increase in information encoded in population activity in each area and improved behavioral performance. Electrically stimulating visual cortex at 4 hertz emulated the beneficial effects of sleep on network and perceptual performance. A large-scale neural network model indicated that asymmetric depression of local intracortical synapses is consistent with the observed changes in neural activity after sleep.
Culture in humans and other animals
Authors: Richerson Peter J. , Boyd Robert T.
Published: November 2024

Migration provides evidence for cumulative cultural evolution in chimpanzees
In Science Journals
Authors: Vignieri Sacha , Stern Peter , Szuromi Phil , Maroso Mattia , Suleymanov Yury , Isles Hannah M. , Kelly Priscilla N. , Ray L. Bryan , Jiang Di , Funk Michael A. , Simonti Corinne , Ferrarelli Leslie K. , Smith Orla
Published: November 2024

Highlights from the
Science
family of journals
Genome sequences of antimicrobial-resistant
Campylobacter coli
and
Campylobacter jejuni
, isolated from poultry in Ukraine
Authors: Halka Ihor , Shchur Natalia , Bortz Eric , Mandyhra Svitlana , Nedosekov Vitalii , Katsaraba Orest , Goodfellow Ian , Drown Devin M. , Kovalenko Ganna
Published: November 2024

Genomes of a
Campylobacter coli
OR12-like strain ChP2023 (1,713,995 bp) isolated from broiler chicken and
Campylobacter jejuni
subsp.
jejuni
NCTC 11168R-like strain KF2023 (1,729,995 bp) isolated from turkey, from poultry production facilities in Ukraine in 2023, were sequenced using Oxford Nanopore Technologies. Both genomes included antibiotic resistance genes and other virulence factors.
MAGE-A4 pMHC-targeted CAR-T cells exploiting TCR machinery exhibit significantly improved in vivo function while retaining antigen specificity
Authors: Liu Meiou , Akahori Yasushi , Imai Naoko , Wang Linan , Negishi Kohei , Kato Takuma , Fujiwara Hiroshi , Miwa Hiroshi , Shiku Hiroshi , Miyahara Yoshihiro
Published: November 2024

Background

The development of chimeric antigen receptor (CAR)-T cell therapies for solid tumors has attracted considerable attention, yet their clinical efficacy remains limited. Therefore, various efforts have been made to improve the efficacy of CAR-T cell therapy. As one promising strategy, incorporating the T-cell receptor (TCR) machinery into CAR structures has been reported to improve the efficacy of CAR-T cells in studies using conventional CARs targeting such as EGFR. However, in the case of peptide/major histocompatibility complex (pMHC)-targeted CARs, the advantages of exploiting TCR machinery have not been fully elucidated. We recently developed MAGE-A4-derived pMHC (MAGE-A4 pMHC)-targeted CAR-T cells (MA-CAR-T cells) using a highly specific human scFv antibody against MAGE-A4p230-239/HLA-A*02:01. We aimed to determine whether MAGE-A4 pMHC-targeted CAR-T cells using the TCR machinery (Hybrid MA-TCR-T cells) exhibit superior functionality without compromising antigen specificity.

Methods

We constructed a retroviral vector expressing Hybrid MA-TCR where MAGE-A4 pMHC-specific scFv are fused to human TCR constant chains.

Results

Hybrid MA-TCR-T cells demonstrated superior in vitro functions compared with MA-CAR-T cells, while maintaining strict antigen specificity. In addition, functional superiority of Hybrid MA-TCR-T cells to MA-CAR-T cells became more pronounced on repetitive antigen stimulation. In particular, Hybrid MA-TCR-T cells significantly inhibited tumor growth in an immunodeficient mouse model more effectively than MA-CAR-T cells. Ex vivo analyses indicated that their enhanced therapeutic efficacy might result from higher infiltration of functionally active, less differentiated Hybrid MA-TCR-T cells in tumor tissues.

Conclusions

These findings suggest that leveraging the TCR machinery is a promising strategy for enhancing pMHC-targeted CAR-T cell therapy for solid tumors, potentially leading to more effective treatments.
Role of intravenous immunoglobulins in the management of systemic lupus erythematosus: a single-centre experience
Authors: Kaya Mehmet Nur , Kılıç Özlem , Canbaş Muhammet , Özgünen Merve Sungur , Güneş Ezgi Çimen , Yılmaz Sedat
Published: November 2024

Objectives

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown aetiology that can affect almost any organ in the body. Although there are no specific guidelines for the use of intravenous immunoglobulin (IG) in the treatment of patients with SLE, it is thought to be an effective treatment. Our study aimed to evaluate the effectiveness and safety of intravenous IG and to describe the possible profile of patients with SLE who are candidates for intravenous IG treatment.

Methods

This study was designed to retrospectively analyse patients with SLE treated with 2 g/kg/month of intravenous IG (divided across 5 consecutive days). We collected demographic, clinical, laboratory and treatment data from the patient files. The side effects of the intravenous IG treatment, changes in the immunosuppressive therapy used and changes in the clinical and laboratory parameters after the intravenous IG treatment were evaluated.

Results

This study included 31 patients with SLE. The main indication for intravenous IG treatment was haematological involvement (20, 64.5%) and thrombocytopenia in particular (8, 25.8%). Intravenous IG was initiated mainly for refractory disease. At the end of the treatment, the acute phase values, proteinuria, complement levels and anti-double-stranded DNA decreased significantly (p<0.001). In most cases, the side effects were mild and usually manifested as myalgia or a fever.

Conclusion

Despite its high cost, intravenous IG has demonstrated effectiveness in treating refractory SLE, especially when there is haematological involvement. Specific clinical features at baseline may identify the patients who are more likely to respond to this therapy.
Prevalence and risk factors for chronic postamputation pain requiring analgesia or nerve interventions: a population-based study in East Asia
Authors: Liu Wen-Chih , Su Fu-Wei , Su Sheng-You , Chiang Chen-Hao , Yao Shu-Hsin , Shih Chia-Lung , Eberlin Kyle R
Published: November 2024

Background

Postamputation pain (PAP) is underexplored in East Asian populations, with most data originating from Western countries. This study aimed to investigate the prevalence and risk factors of PAP in East Asian individuals who underwent surgical amputation, particularly those requiring subsequent analgesia or nerve interventions.

Materials and methods

The study used data from the Longitudinal Health Insurance Database, encompassing claims from 2 million randomly selected individuals out of 23.5 million enrolled in Taiwan’s National Health Insurance Research Database from 2000 to 2019. The risk factors for PAP were analyzed using multivariable regression, considering criteria such as chronic postamputation pain, symptomatic neuroma treatment, chronic analgesia use, and nerve interventions.

Results

Among the 7287 amputees (mean age 59.5 years; 65.1% male), 18.1% (95% CI 17.2%, 19.0%) (1318 patients) experienced PAP requiring intervention. Significant risk factors included cancer (OR 2.20 (95% CI 1.27, 3.68)), peripheral artery disease (OR 2.11 (95% CI 1.66, 2.69)), infection (OR 1.93 (95% CI 1.55, 2.40)), diabetes mellitus (OR 1.85 (95% CI 1.45, 2.37)), and a higher Elixhauser Comorbidity Index (OR 1.04 (95% CI 1.02, 1.06)). Prior radiculopathy (OR 1.30 (95% CI 1.13, 1.51)) and myelopathy (OR 1.33 (95% CI 1.14, 1.55)) also increased pain odds, while age showed a slight inverse association (OR 0.99 (95% CI 0.98, 0.99)).

Conclusion

The prevalence of PAP requiring subsequent analgesia or nerve interventions in this East Asian population is 18.1%. Identified risk factors include multiple medical comorbidities and pre-existing radiculopathy or myelopathy. These findings highlight the important considerations for both clinical practice and future research directions.
0.23-Tesla MRI to differentiate between ischaemic and haemorrhagic strokes within 24 hours of onset: a combined experimental–clinical study
Authors: Xie Xuewei , Jiang Qianmei , Suo Yue , Han Chong , Wang Zhaobin , Zhang Zhe , Wang Ning , Wang Yihuai , Zhang Chunguang , Xue Bingshan , Liu Tao , Wang David , Jing Jing , Wang Yongjun
Published: November 2024

Background and purpose

The low-field MRI is a promising tool to accurately diagnose strokes. We here report our study on the accuracy of a 0.23-Tesla (0.23-T) MRI using the haematoma enhanced inversion recovery (HEIR) sequence to detect acute ischaemic stroke (AIS) and intracerebral haemorrhage (ICH) within 24 hours of symptom onset.

Methods

A novel HEIR sequence based on fluid-attenuated inversion recovery T1-weighted, with a scanning time of 1 min and 17 s, was developed using an ICH and AIS pig model on a 0.23-T MRI. Images of the pig model were obtained hourly for 24 hours in order to monitor value changes on T1/T2 and verify the differential diagnosis of AIS and ICH. Then, 30 patients with AIS and 30 patients with ICH with confirmed diagnoses by 3T-MRI/CT were included. Diagnostic criteria on a 0.23-T MRI for ICH was the hyperintensity signal on both the diffusion-weighted imaging (DWI) and HEIR sequence, while for AIS was the hyperintensity on DWI and isointensity on the HEIR sequence. Two blinded raters independently assessed the images obtained by the 0.23-T MRI for the presence of ICH/AIS.

Results

In the pig model, setting the inversion time to 800 ms enabled clear differentiation of ICH from brain parenchymal tissue and AIS. In real patients, a correct 0.23-T MRI diagnosis of either an AIS or ICH was made in all 60 patients within 24 hours of symptom onset (100% overall accuracy). No adverse events occurred.

Conclusions

The 0.23-T MRI may have the potential to differentiate cerebral haemorrhage from cerebral infarction with both speed and accuracy, making brain MRI scans easier, faster and cheaper. It might be possible to improve the screening imaging process for strokes in the emergency room. Further multicentre studies are needed to validate our findings.
Can Bell inequalities be tested via scattering cross-section at colliders ?
Authors: Li Song , Shen Wei , Yang Jin Min
Published: November 2024

In current studies for testing Bell inequalities at colliders, the reconstruction of spin correlations from scattering cross-sections relies on the bilinear form of the spin correlations, but not all local hidden variable models (LHVMs) have such a property. To demonstrate that a general LHVM cannot be rule out via scattering cross-section data, we propose a specific LHVM, which can exactly duplicate the same scattering cross-section for particle production and decay as the standard quantum theory, making it indistinguishable at colliders in principle. Despite of this, we find that reconstructing spin correlations through scattering cross-sections can still exclude a broad class of LHVMs, e.g., those models employing classical spin correlations as a surrogate for quantum spin correlations.
Generalized Chern–Pontryagin models
Authors: Nascimento J. R. , Petrov A. Yu. , Porfírio P. J. , da Silva Ramires N.
Published: November 2024

We formulate a new class of modified gravity models, that is, generalized four-dimensional Chern–Pontryagin models, whose action is characterized by an arbitrary function of the Ricci scalar R and the Chern–Pontryagin topological term $$ ^*RR$$

∗

R
R

, i.e., $$f(R, ^*RR)$$

f
(
R

,
∗

R
R
)

. Within this framework, we derive the gravitational field equations and solve them for the particular models, $$f(R, ^*RR)=R+\beta ( ^*RR)^2$$

f

(
R

,
∗

R
R
)

=
R
+
β

(
∗

R
R
)

2

and $$f(R, ^*RR)=R+\alpha R^2+\beta ( ^*RR)^2$$

f

(
R

,
∗

R
R
)

=
R
+
α

R
2

+
β

(
∗

R
R
)

2

, considering two ansatzes: the slowly rotating Schwarzschild metric and first-order perturbations of Gödel-type metrics. For the former, we find a first-order correction to the frame-dragging effect boosted by the parameter L, which characterizes the departures from general relativity results. For the latter, Gödel-type metrics hold unperturbed, for specific sort of perturbed metric functions. We conclude this paper by displaying that generalized four-dimensional Chern–Pontryagin models admit a scalar-tensor representation, whose explicit form presents two scalar fields: $$\Phi $$
Φ
, a dynamical degree of freedom, while the second, $$\vartheta $$
ϑ
, a non-dynamical degree of freedom. In particular, the scalar field $$\vartheta $$
ϑ
emerges coupled with the Chern–Pontryagin topological term $$ ^*RR$$

∗

R
R

, i.e., $$\vartheta ^*RR$$

ϑ
∗

R
R

, which is nothing more than Chern–Simons term.
FRACTAL MODEL FOR HETEROGENEOUS EFFECTIVE DIFFUSION AND ITS APPLICATION IN COMPOSITIONAL SIMULATIONS OF CO2 FLOODING IN TIGHT RESERVOIRS
Authors: CAI MINGYU , YANG SHUGANG , LIU SHUANGXING , ZHANG KUNFENG , XUE MING , LI XINGCHUN , SU YULIANG , LI LEI
Published: November 2024

The extremely low porosity and permeability in tight reservoirs make diffusion effects significant and non-negligible. Accurately estimating diffusion coefficients is critical for predicting CO2-enhanced oil recovery and CO2 storage (CCUS–EOR) in such reservoirs. However, predicting effective vapor–liquid phase diffusion in porous media and incorporating it into large-scale compositional simulations remains a major challenge. This study combines fractal theory with Fick and Maxwell–Stefan diffusion frameworks, proposing a simplified fractal diffusion model that incorporates fractal parameters and porosity to better characterize CO2 and in situ oil diffusion in heterogeneous tight reservoirs. Numerical models were developed using MATLAB to simulate both immiscible and miscible CO2 flooding within multiphase, multicomponent flow equations while considering diffusion effects. The results reveal that diffusion significantly increases the area influenced by CO2 and improves the uniformity of CO2 concentration along the flow path. Confined phase behavior in micro- and nanopores enhances diffusion, increasing CO2 mole fractions in displacement zones. In immiscible flooding, CO2 extraction of lighter oil components results in higher residual oil density compared to models that neglect diffusion. This fractal-based model for heterogeneous effective diffusion, combined with insights into the role of diffusion in CO2 flooding in tight reservoirs, offers important implications for developing more effective CCUS–EOR strategies.
Size Matters: Characterizing the Effect of Target Size on Wi-Fi Sensing Based on the Fresnel Zone Model
Authors: Wang Zhu , Geng Chendi , Sun Zhuo , Chen Zhen , Liu Sicong , Guo Bin , Yu Zhiwen
Published: November 2024

The model-based Wi-Fi sensing approach has shown advantages on facilitating the development of more robust sensing systems, due to its capability of revealing the physical and mathematical sensing mechanisms without the requirement of a large set of training data. Existing models usually treat the sensing target as a particle and characterize the propagation of Wi-Fi signals accordingly, i.e., the effect of target size is ignored. However, in most real-world scenarios, the sensing targets are non-particle and different targets usually have different sizes. Considering that the size difference may have a significant impact on the sensing performance, it is necessary to develop a size-aware model to enrich the Wi-Fi sensing theory. To fill this gap, we propose a non-particle target oriented Wi-Fi sensing model, aiming to describe the relationship between the Wi-Fi signal and the target size. Specifically, by extending the classical particle target oriented Wi-Fi Fresnel zone model, we characterize and quantify the reflected signals from different parts of the non-particle target in a more fine-grained manner. We find the amplitude of the reflected Wi-Fi signals increases and decreases periodically along with the changing of the target size, which is called as the oscillation phenomenon. To validate the proposed model, we implement two sensing applications with a pair of transceivers, including a respiration detection system and a target size measurement system. Extensive experiments demonstrate the correctness and the usefulness of the proposed size-aware model. To the best of our knowledge, this is the first theoretical model that reveals the effect of target size on Wi-Fi sensing.
Perfat: Non-contact Abdominal Obesity Assessment System
Authors: Pan Chaoyi , Xu Dan , Xu Shiyuan , Wang Fuwei , Zheng Chao , Liu Bingyue , Chen Xiaojiang
Published: November 2024

Extensive research has revealed a strong correlation between excess abdominal fat and chronic diseases, such as insulin resistance, hypertension and hypercholesterolemia. Therefore, the measurement of abdominal fat in daily life is significant. Many household body fat scales can provide abdominal (trunk) fat value for users, however, these scales require users to stand barefoot, gripping the electrodes with their arms straight, which is not friendly for those users who are unable to stand. Specialized equipment in hospitals, such as computed tomography (CT) and magnetic resonance imaging (MRI) machines, can provide accurate abdominal fat value with less restriction on user's posture, but those machines are costly, bulky and professional, making them impractical for home use. In this paper, we present Perfat, a system that aims to estimate the abdominal fat percentage accurately. The key insight of Perfat is that, as signals pass through the human body, the energy attenuation of signals at different frequencies varies, and the abdominal fat percentage can be calculated based on such attenuation variation. To evaluate the performance of Perfat, we developed a prototype using software-defined radio and conducted experiments with 50 participants. We compared Perfat with the Inbody S10 body composition analyzer and found a correlation of over 96.7%.
SynthCAT: Synthesizing Cellular Association Traces with Fusion of Model-Based and Data-Driven Approaches
Authors: Lyu Feng , Zhang Jie , Lu Huali , Wu Huaqing , Wu Fan , Zhang Yongmin , Zhang Yaoxue
Published: November 2024

The scarcity of publicly available cellular association traces hinders user location-based research and various data-driven services, highlighting the importance of data synthesis in this field. In this paper, we investigate the cellular association trace synthesis (CATS) problem, aiming to generate diverse and realistic cellular association traces based on road segment-based trajectories and corresponding departure times. To substantiate our research, we first gather substantial data, including road segment-based trajectories, base station (BS) distribution, and ground truths of cellular association traces. We then perform systematic data analysis to reveal technical challenges such as disparity in geographic spaces, complex and dynamic BS handover, and poor performance of single-dimension approaches. To address these challenges, we propose SynthCAT, a novel scheme that fuses model-based and data-driven approaches. Specifically, SynthCAT includes: i) A model-based coarse-grained cellular association trace generation component, encompassing GPS reference generation, weighted historical average time generation, Bayesian decision, and time mapping modules. This component establishes a unified GPS space to map road and BS spaces, generates initial time information, synthesizes coarse-grained spatial cellular association traces by following explicit BS handover rules, and maps the corresponding arrival time for each trace point; ii) A fine-grained cellular association trace generation component, which combines model-based and data-driven approaches. This employs a two-stage Autoencoder Generative Adversarial Network (AEGAN) to refine cellular association traces based on the coarse-grained ones. Extensive field experiments validate the efficacy of SynthCAT in terms of trace similarity to ground truths and its efficiency in supporting practical downstream applications.
Facial Landmark Detection Based on High Precision Spatial Sampling via Millimeter-wave Radar
Authors: Li Yi , Wang Chuyu , Xie Lei , Jin Qiancheng , Fan Long , Ning Jingyi , Lu Sanglu
Published: November 2024

Facial landmark has become one of the most widely-used and intuitive feature of the face. Traditional methods for Facial Landmark Detection (FLD) are primarily based on cameras, which are limited by their sensitivity to light conditions, inability to penetrate obstructions, and potential privacy leaks. In this paper, we propose mmFLD to estimate the facial landmark positions using millimeter-wave (mmWave) radar with mm-level accuracy. By simultaneously applying the range estimation capability and angle estimation capability of mmWave radar, we are able to spatially sample face reflection signals with high resolution. In particular, we propose a velocity-based method for head detection and tracking, and then we build two generalized models to extract effective facial motion features from different facial regions. Moreover, we design an end-to-end neural network to extract the face structure and the motion coherence implicit in mmWave data. Experiment results show that mmFLD can estimate the facial landmark positions with high accuracy, e.g., the average Mean Absolute Error (MAE) is 2.81 mm with eight kinds of different facial expressions, and extended experiment also demonstrates the generalizability and robustness of mmFLD for different experiment conditions.
Uranus: Empowering Generalized Gesture Recognition with Mobility through Generating Large-scale mmWave Radar Data
Authors: Ling Yue , Zhao Dong , Deng Kaikai , Yin Kangwen , Zheng Wenxin , Ma Huadong
Published: November 2024

Millimeter-wave radar shows great sensing capabilities for pervasive and privacy-preserving gesture recognition. However, the lack of large-scale, dynamic radar datasets hinders the advancement of deep learning models for generalized gesture recognition in dynamic scenes. To address this problem, we opt for designing a system that employs wealthy dynamic 2D videos to generate realistic radar data, but it confronts two challenges including i) simulating the complex signal reflection characteristics of humans and the background and ii) extracting elusive gesture-relevant features from dynamic radar data. To this end, we design Uranus with two key components: (i) a dynamic data generation network (DDG-Net) combines several key modules, human reflection model, background reflection extractor, and data fitting model to simulate the signal reflection characteristics of humans and the background, followed by fitting the number and global distribution of points in point clouds to generate realistic radar data; (ii) a dynamic gesture recognition network (DGR-Net) combines two modules, spatial feature extraction and global feature fusion, to extract spatial and global features of points in point clouds, respectively, to achieve generalized gesture recognition. We implement and evaluate Uranus with dynamic video data from public video sources and self-collected radar data, demonstrating that Uranus outperforms the state-of-the-art approaches for gesture recognition in dynamic scenes.
DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge
Authors: Yang Bufang , Jiang Siyang , Xu Lilin , Liu Kaiwei , Li Hai , Xing Guoliang , Chen Hongkai , Jiang Xiaofan , Yan Zhenyu
Published: November 2024

Large language models (LLMs) have the potential to transform digital healthcare, as evidenced by recent advances in LLM-based virtual doctors. However, current approaches rely on patient's subjective descriptions of symptoms, causing increased misdiagnosis. Recognizing the value of daily data from smart devices, we introduce a novel LLM-based multi-turn consultation virtual doctor system, DrHouse, which incorporates three significant contributions: 1) It utilizes sensor data from smart devices in the diagnosis process, enhancing accuracy and reliability. 2) DrHouse leverages continuously updating medical knowledge bases to ensure its model remains at diagnostic standard's forefront. 3) DrHouse introduces a novel diagnostic algorithm that concurrently evaluates potential diseases and their likelihood, facilitating more nuanced and informed medical assessments. Through multi-turn interactions, DrHouse determines the next steps, such as accessing daily data from smart devices or requesting in-lab tests, and progressively refines its diagnoses. Evaluations on three public datasets and our self-collected datasets show that DrHouse can achieve up to an 31.5% increase in diagnosis accuracy over the state-of-the-art baselines. The results of a 32-participant user study show that 75% medical experts and 91.7% test subjects are willing to use DrHouse.
MmECare: Enabling Fine-grained Vital Sign Monitoring for Emergency Care with Handheld MmWave Radars
Authors: Chang Zhaoxin , Zhang Fusang , Ma Xujun , Wang Pei , Chen Weiyan , Zhang Duo , Jouaber Badii , Zhang Daqing
Published: November 2024

Fine-grained vital sign monitoring in emergency care is crucial for accurately assessing patient conditions, predicting disease progression, and formulating effective rescue plans. In non-hospital settings, limited equipment often necessitates manual observation of respiration and heartbeat, which can lead to significant errors. Contactless monitoring using wireless signals offers a promising alternative. Unlike traditional systems that require stationary devices for contactless sensing, handheld devices are more practical for rescuers during emergency care. However, sensing performance can be severely compromised by involuntary hand movements. Previous research has achieved respiration monitoring with handheld devices, but the randomness of hand motion still prevents reliable heartbeat monitoring. In this paper, we first demonstrate that the key to mitigating the effects of device motion lies in accurately estimating the motion direction. We then introduce a novel method that uses two static objects, i.e., corner reflectors, to precisely estimate the random motion direction of the device. These reflectors can be quickly and easily deployed by the rescuer before initiating vital sign monitoring, enabling a more thorough elimination of device motion effects. Comprehensive experiments validate the effectiveness of our solution using mmWave radar. Real-world tests demonstrate that our system can accurately monitor both respiration and heartbeat with handheld devices, significantly enhancing emergency medical response by improving the accuracy and feasibility of vital sign monitoring in urgent situations.
KneeGuard: A Calibration-free Wearable Monitoring System for Knee Osteoarthritis Gait Re-training via Effortless Wearing
Authors: Yang Baichen , Zhang Xinyi , Zhang Jiaxi , Huang Zirui , Lu Qiqi , Zhang Jin , Hu Hai , Zhang Qian
Published: November 2024

Gait re-training is an effective approach to slow disease progression and alleviate pain in knee osteoarthritis (KOA) patients. Personalized gait re-training strategies, based on knee loading and muscle forces, have shown promise in improving rehabilitation outcomes. Laboratory systems to monitor these metrics are unsuitable for daily use owing to the complicated setup and high-cost. Recently proposed wearable solutions try to fill this gap, but their in-lab calibration requirement still hinders practical usage. This paper introduces KneeGuard, a calibration-free gait re-training monitoring system that can estimate knee loading and muscle forces via effortless wearing. We identify the main issue of current calibration-needed systems is insufficient biomechanical information retrieval and modeling. To address this, we propose a user-friendly wearable prototype incorporating inertial measurement unit (IMU) and surface electromyography (sEMG) to obtain comprehensive biomechanical information including body geometrical changes and muscle contractions. For modeling, we design a biomechanic-inspired fusion framework based on multi-task learning and cross-modality attention to capture inter-modality biomechanical correlations. Additionally, since precise sensor placement required by current sEMG-based solutions is difficult to locate, we develop a circular sEMG array and propose a spatial-aware feature extraction module, achieving effective biomechanical feature extraction under effortless wearing. We collaborate with a medical center and collect a dataset from 21 KOA patients and 17 healthy subjects at different speeds. Notably, our dataset includes six gait types for KOA gait re-training, making it the first gait dataset with comprehensive re-training strategies. Evaluation demonstrates that KneeGuard achieves an average normalized root-mean-square error (NRMSE) of 9.95% in knee loading estimation and an average NRMSE of 8.75% in the estimation of muscle forces, comparable to the with-calibration results in existing works. We have open-sourced the code and a sample dataset in https://github.com/KneeGuard/KneeGuard.
Temporal Action Localization for Inertial-based Human Activity Recognition
Authors: Bock Marius , Moeller Michael , Van Laerhoven Kristof
Published: November 2024

As of today, state-of-the-art activity recognition from wearable sensors relies on algorithms being trained to classify fixed windows of data. In contrast, video-based Human Activity Recognition, known as Temporal Action Localization (TAL), has followed a segment-based prediction approach, localizing activity segments in a timeline of arbitrary length. This paper is the first to systematically demonstrate the applicability of state-of-the-art TAL models for both offline and near-online Human Activity Recognition (HAR) using raw inertial data as well as pre-extracted latent features as input. Offline prediction results show that TAL models are able to outperform popular inertial models on a multitude of HAR benchmark datasets, with improvements reaching as much as 26% in F1-score. We show that by analyzing timelines as a whole, TAL models can produce more coherent segments and achieve higher NULL-class accuracy across all datasets. We demonstrate that TAL is less suited for the immediate classification of small-sized windows of data, yet offers an interesting perspective on inertial-based HAR - alleviating the need for fixed-size windows and enabling algorithms to recognize activities of arbitrary length. With design choices and training concepts yet to be explored, we argue that TAL architectures could be of significant value to the inertial-based HAR community. The code and data download to reproduce experiments is publicly available via github.com/mariusbock/tal_for_har.
A Comprehensive Analysis Dashboard for Detecting Similar Saudi Twitter Accounts by Using Stylometric Features
Authors: Bagies Taghreed , Alsuhaimi Rahaf , Almasre Miada , Bafail Alaa
Published: November 2024

Criminals, including terrorists, may use Twitter to communicate and share their ideologies. They often employ multiple accounts for anonymity. While the other accounts hide their identities and use it for different purposes (e.g., communication with unknown criminals), they use one to write tweets revealing their beliefs and spreading evil thoughts (e.g., racism and bullying). Since these multiple accounts will not have the same contents, stakeholders cannot rely on the contents to detect various accounts belonging to the same person. Using stylometric features may help to detect these accounts as they depend on the writing style of a person rather than the content of the words and their meaning. In this article, we build a model and use stylometric features that differ from the state-of-the-art, such as n-gram for part-of-speech tag, frequency of repeated characters, number of emojis, and more. These features distinguish our approach from existing methods. We evaluated different machine and deep learning classifiers to make comparisons. Our results highlight the effectiveness of our feature set, achieving a remarkable accuracy of 96%. Additionally, our findings indicate that machine learning classifiers exhibit superiority over their deep learning counterparts in the context of this study. Furthermore, we developed a comprehensive dashboard that offers users an in-depth analysis of different Twitter accounts and ranks their similarities. The dashboard serves as a valuable tool for gaining insights into the relationships and similarities among the analyzed Twitter accounts.