bluuebunny/crossref_metadata_2025 huggingface dataset:
Maintaining essential health services during COVID-19 in Ghana: a qualitative study
Authors: Yeboah Isaac , Dwomoh Duah , Ndejjo Rawlance , Kabwama Steven Ndugwa , Ohemeng Fidelia , Takyi Sylvia Akpene , Issah Ibrahim , Bawuah Serwaa Akoto , Wanyenze Rhoda Kitti , Fobil Julius
Published: March 2024

Introduction

Evidence suggests that non-pharmaceutical interventions such as lockdown policies, restriction of movement and physical distancing to control the novel COVID-19 contributed to the decline in utilisation of essential health services. We explored healthcare providers’ and policy-makers’ experiences of the barriers, interventions and response actions that contributed to ensuring the continuity of essential health services during the COVID-19 pandemic in Ghana to help inform future practice and policy.

Methods

We used a qualitative study approach. Data were analysed using thematic analysis. Thirty Four participants composed of 20 healthcare providers and 14 policy-makers who worked across regions with low and high recorded COVID-19 cases in Ghana during the COVID-19 pandemic were involved in this study.

Results

Participants reported that essential health services including maternal, reproductive and child health services, communicable and non-communicable disease care, and elective surgeries were disrupted during the COVID-19 pandemic. Barriers to the utilisation of essential services were constructed into three subthemes: (1) fear, (2) poor quality of care at the facility and (3) financial limitation. These barriers were mitigated with population-based interventions underpinned by the socioecological model at the individual and interpersonal level (including psychosocial care for families and home visits), institutional and community levels (such as allocation of funds, training of health workers, public education, triage stations, provision of logistics, appointment scheduling, telemedicine and redeployment of health workers) and public policy level (tax relief packages, transportation arrangements and provision of incentives), which helped in maintaining essential health services during COVID-19.

Conclusion

Disruption of essential health services during COVID-19 in Ghana instigated population-based interventions which aided in expanding the populations’ continuous access to essential health services and strengthened health service delivery.
RE-OPEN: Randomised trial of biosimilar TNK versus TPA during endovascular therapy for acute ischaemic stroke due to large vessel occlusions
Authors: Bhatia Rohit , Srivastava MV Padma , Fatima Saman , Sarkar Risha , Longkumer Imnameren , Gaikwad Shailesh , Devaranjan Leve S Joseph , Garg Ajay , Durai Pandian Jeyaraj , Khurana Dheeraj , Sylaja PN , Jain Shweta , Arora Deepti , Dhasan Aneesh , Aaron Sanjith , Miraclin Angel T , Vijaya Pamidimukkala , Rajendran Srijithesh P , Roy Jayanta , Ray Biman Kanti , Nambiar Vivek , Alapatt Paul J , Sharma Meenakshi
Published: March 2024

Rationale

Rapid and timely treatment with intravenous thrombolysis and endovascular treatment (EVT) in patients with acute ischaemic stroke (AIS) and large vessel occlusion (LVO) significantly improves patient outcomes. Bridging therapy is the current standard of care in these patients. However, an incompletely answered question is whether one thrombolytic agent is better than another during bridging therapy.

Aim

The current study aims to understand if one thrombolytic agent is superior to the other during bridging therapy in the treatment of AIS and LVO.

Sample size estimates

Using 80% power and an alpha error of 5 %, presuming a 10% drop out rate, a total of 372 patients will be recruited for the study.

Methods and design

This study is a prospective, randomised, multicentre, open-label trial with blinded outcome analysis design.

Study outcomes

Theprimary outcomesinclude proportion of patients who will be independent at 3 months (modified Rankin score (mRS) ≤2 as good outcome) and proportion of patients who achieve recanalisation modified thrombolysis in cerebral infarction grade 2b/3 at first angiography run at the end of EVT.Secondary outcomesinclude proportion of patients with early neurological improvement, rate of symptomatic intracerebral haemorrhage (ICH), rate of any ICH, rate of any systemic major or minor bleeding and duration of hospital stay.Safety outcomesinclude any intracranial bleeding or symptomatic ICH.

Discussion

This trial is envisioned to confirm the theoretical advantages and increase the strength and quality of evidence for use of tenecteplase (TNK) in practice. Also, it will help to generate data on the efficacy and safety of biosimilar TNK.

Trial registration number

CTRI/2022/01/039473.
Internalisation of neutrophils extracellular traps by macrophages aggravate rheumatoid arthritis via Rab5a
Authors: Ye Haixin , Yang Qian , Guo Huaxia , Wang Xing , Cheng Lifang , Han Bingqi , Hong Mukeng , Ma Fopei , Li Meng , Wu Xianghui , Chen Feilong , Zhu Junqing , Chen Shixian , Zheng Songyuan , Li Juan
Published: March 2024

Objectives

Although elevated levels of neutrophil extracellular traps (NETs) have been reported in patients with rheumatoid arthritis (RA), the role of NETs in RA and the relationship between NETs and macrophages in the pathogenesis of RA requires further research. Here, we sought to determine the role of NETs in RA pathogenesis and reveal the potential mechanism.

Methods

Neutrophil elastase (NE) and myeloperoxidase (MPO)-DNA were measured in human serum and synovium. NETs inhibitor GSK484 was used to examine whether NETs involved with RA progression. We stimulated macrophages with NETs and detected internalisation-related proteins to investigate whether NETs entry into macrophages and induced inflammatory cytokines secretion through internalisation. To reveal mechanisms mediating NETs-induced inflammation aggravation, we silenced GTPases involved in internalisation and inflammatory pathways in vivo and in vitro and detected downstream inflammatory pathways.

Results

Serum and synovium from patients with RA showed a significant increase in NE and MPO, which positively correlated to disease activity. Inhibiting NETs formation alleviated the collagen-induced arthritis severity. In vitro, NETs are internalised by macrophages and located in early endosomes. Rab 5a was identified as the key mediator of the NETs internalisation and inflammatory cytokines secretion. Rab 5a knockout mice exhibited arthritis alleviation. Moreover, we found that NE contained in NETs activated the Rab5a-nuclear factor kappa B (NF-κB) signal pathway and promoted the inflammatory cytokines secretion in macrophages.

Conclusions

This study demonstrated that NETs-induced macrophages inflammation to aggravate RA in Rab 5a dependent manner. Mechanically, Rab5a mediated internalisation of NETs by macrophages and NE contained in NETs promoted macrophages inflammatory cytokines secretion through NF-κB-light-chain-enhancer of activated B cells signal pathway. Therapeutic targeting Rab 5a or NE might extend novel strategies to minimise inflammation in RA.
Risk of serious infection associated with different classes of targeted therapies used in psoriatic arthritis: a nationwide cohort study from the French Health Insurance Database (SNDS)
Authors: Bastard Léa , Claudepierre Pascal , Penso Laetitia , Sbidian Emilie , Pina Vegas Laura
Published: March 2024

Objective

To assess the risk of serious infection associated with different targeted therapies for psoriatic arthritis (PsA) in real-world settings.

Methods

This nationwide cohort study used the administrative healthcare database of the French health insurance scheme linked to the hospital discharge database to identify all adults with PsA who were new users of targeted therapies (adalimumab, etanercept, golimumab, certolizumab pegol, infliximab, secukinumab, ixekizumab, ustekinumab, and tofacitinib) from 1 January 2015 to 30 June 2021. The primary outcome was a serious infection (ie, requiring hospitalisation), in a time-to-event analysis using propensity score-weighted Cox models, with adalimumab as the comparator, estimating weighted HRs (wHRs) and their 95% CIs.

Results

A total of 12 071 patients were included (mean age 48.7±12.7 years; 6965 (57.7%) women). We identified 367 serious infections (3.0% of patients), with a crude incidence rate of 17.0 per 1000 person-years (95% CI, 15.2 to 18.7). After inverse propensity score weighting and adjustment for time-dependent covariates and calendar year, risk of serious infection was significantly lower for new users of etanercept (wHR 0.72; 95% CI, 0.53 to 0.97) or ustekinumab (wHR, 0.57; 95% CI, 0.35 to 0.93) than adalimumab new users. This risk was not statistically modified with the other targeted therapies.

Conclusions

The incidence of serious infection was low for PsA patients who were new users of targeted therapies in real-world settings. Relative to adalimumab new users, this risk was lower among new users of etanercept and ustekinumab and unmodified for the other molecules.
Serum cytokine biosignatures for identification of tuberculosis among HIV-positive inpatients
Authors: Zhang Huihua , Li LingHua , Liu YanXia , Xiao Wei , Xu RuiYao , Lu MengRu , Hao WenBiao , Gao YuChi , Tang Xiaoping , Dai Youchao
Published: March 2024

Background

Serum cytokines correlate with tuberculosis (TB) progression and are predictors of TB recurrence in people living with HIV. We investigated whether serum cytokine biosignatures could diagnose TB among HIV-positive inpatients.

Methods

We recruited HIV-positive inpatients with symptoms of TB and measured serum levels of inflammation biomarkers including IL-2, IL-4, IL-6, IL-10, tumour necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ). We then built and tested our TB prediction model.

Results

236 HIV-positive inpatients were enrolled in the first cohort and all the inflammation biomarkers were significantly higher in participants with microbiologically confirmed TB than those without TB. A binary support vector machine (SVM) model was built, incorporating the data of four biomarkers (IL-6, IL-10, TNF-α and IFN-γ). Efficacy of the SVM model was assessed in training (n=189) and validation (n=47) sets with area under the curve (AUC) of 0.92 (95% CI 0.88 to 0.96) and 0.85 (95% CI 0.72 to 0.97), respectively. In an independent test set (n=110), the SVM model yielded an AUC of 0.85 (95% CI 0.76 to 0.94) with 78% (95% CI 68% to 87%) specificity and 85% (95% CI 66% to 96%) sensitivity. Moreover, the SVM model outperformed interferon-gamma release assay (IGRA) among advanced HIV-positive inpatients irrespective of CD4+T-cell counts, which may be an alternative approach for identifyingMycobacterium tuberculosisinfection among HIV-positive inpatients with negative IGRA.

Conclusions

The four-cytokine biosignature model successfully identified TB among HIV-positive inpatients. This diagnostic model may be an alternative approach to diagnose TB in advanced HIV-positive inpatients with low CD4+T-cell counts.
A computational investigation of the thermodynamics and kinetics of multiple single-step electron transfers of various Ni- and chalcogen-containing complexes
Authors: Broome Joshua A. , Xiang Lily , Murray Tyler , Zaliskyy Roman , Bushnell Eric A.C.
Published: March 2024

Continued increase in green house gas emissions will lead to irreversible and unpredictable damage to the environment and biosphere. CO2 alone contributes to about two-thirds of the energy imbalance causing climate change. The bulk of these CO2 emissions can be traced to fossil fuels, which presently account for 82% of this green house gas. Developing effective renewable–alternative fuel sources is crucial to mitigate the harmful effects of fossil fuels. One such alternative fuel source is molecular hydrogen, which can be produced by the photocatalytic splitting of water using metal-based catalysts. This study investigates the thermodynamics and kinetics associated with single-step protonations and reductions of Ni[(S2C2H2)(N2C2H4)], Ni[(Se2C2H2)(N2C2H4)], and Ni[(Te2C2H2)(N2C2H4)] complexes using density functional theory. Results found diselenolene to be the least endergonic for the formation of the triply reduced, doubly protonated species with a Gibbs protonation energy of 79.6 kJ mol−1. Ditellurolene and dithiolene were slightly more endergonic with Gibbs protonation energies of 87.7 and 91.6 kJ mol−1, respectively. The most thermodynamically favorable pathways were found to be the ECCEE pathway for dithiolene and diselenolene, whereas the CECEE mechanism was found to be most favorable for ditellurolene. Kinetically, it was found that there were two feasible pathways for all three complexes. The first was a CCEEE pathway and the second was a CECEE pathway. All complexes were found to have similar Gibbs activation energies.
Chemistry of plant extracts directs the silver nanostructures’ crystal structure into hexagonally close-packed: a comparative study using elecampane and blueberry extracts
Authors: Tepe Muammer , Zeybek Mehmet Sadrettin
Published: March 2024

In this study, silver nanostructures (AgNSs) were obtained using aqueous extracts of elecampane ( Inula viscosa) and blueberry ( Vaccinium arctostaphylos L.). The synthesized AgNSs were characterized using UV–visible spectrophotometers, Fourier-transformed infrared spectroscopy, scanning electron microscopy-energy dispersive X-ray analysis, high-resolution transmission electron microscopy, and X-ray photoelectron spectroscopy. The optimization studies revealed that pH and the extract/AgNO3 ratio played significant roles in the morphology and size of the AgNSs, while temperature did not show any significant role. Both the blueberry and elecampane extracts resulted in a mixture of spherical and aspherical silver nanoparticles (AgNPs). In contrast, utilization of a relatively high extract/AgNO3 ratio accompanied by an extended incubation period (i.e., 10 days) caused the formation of larger spherical (e.g., up to micron-sized) and large 1D AgNPs for the blueberry and elecampane extracts, respectively. The major difference related to the crystal structures was obtained, as the blueberry extracts resulted in face-centric cubic crystals, and the elecampane extracts allowed the formation of hexagonally close-packed (hcp) crystalline structures. The AgNPs were tested for their antibacterial activities using gram (−) and gram (+) bacterial species, where the elecampane-synthesized AgNPs possessed higher antibacterial activity that can be attributed to the hcp crystalline structure of the AgNPs.
Simulating $Z_{2}$ lattice gauge theory with the variational quantum thermalizer
Authors: Fromm Michael , Philipsen Owe , Spannowsky Michael , Winterowd Christopher
Published: March 2024

The properties of strongly-coupled lattice gauge theories at finite density as well as in real time have largely eluded first-principles studies on the lattice. This is due to the failure of importance sampling for systems with a complex action. An alternative to evade the sign problem is quantum simulation. Although still in its infancy, a lot of progress has been made in devising algorithms to address these problems. In particular, recent efforts have addressed the question of how to produce thermal Gibbs states on a quantum computer. In this study, we apply a variational quantum algorithm to a low-dimensional model which has a local abelian gauge symmetry. We demonstrate how this approach can be applied to obtain information regarding the phase diagram as well as unequal-time correlation functions at non-zero temperature.
Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer’s Disease
Authors: Shaffi Noushath , Subramanian Karthikeyan , Vimbi Viswan , Hajamohideen Faizal , Abdesselam Abdelhamid , Mahmud Mufti
Published: March 2024

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer’s disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3–5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer’s Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.
Entropy-Weighted Numerical Gradient Optimization Spiking Neural System for Biped Robot Control
Authors: Liu Xingyang , Rong Haina , Neri Ferrante , Yu Zhangguo , Zhang Gexiang
Published: March 2024

The optimization of robot controller parameters is a crucial task for enhancing robot performance, yet it often presents challenges due to the complexity of multi-objective, multi-dimensional multi-parameter optimization. This paper introduces a novel approach aimed at efficiently optimizing robot controller parameters to enhance its motion performance. While spiking neural P systems have shown great potential in addressing optimization problems, there has been limited research and validation concerning their application in continuous numerical, multi-objective, and multi-dimensional multi-parameter contexts. To address this research gap, our paper proposes the Entropy-Weighted Numerical Gradient Optimization Spiking Neural P System, which combines the strengths of entropy weighting and spiking neural P systems. First, the introduction of entropy weighting eliminates the subjectivity of weight selection, enhancing the objectivity and reproducibility of the optimization process. Second, our approach employs parallel gradient descent to achieve efficient multi-dimensional multi-parameter optimization searches. In conclusion, validation results on a biped robot simulation model show that our method markedly enhances walking performance compared to traditional approaches and other optimization algorithms. We achieved a velocity mean absolute error at least 35% lower than other methods, with a displacement error two orders of magnitude smaller. This research provides an effective new avenue for performance optimization in the field of robotics.
Simulation Learning and Optimization: Methodology and Applications
Authors: Chang Kuo-Hao , Chen Tzu-Li
Published: March 2024

Due to the rapid advancement of computational power and the Internet of Things (IoT) together with recent developments in digital-twin and cyber-physical systems, the integration of big data analytics techniques (BDAT) (e.g., data mining, machine learning (ML), deep learning, data farming, etc.) into traditional simulation methodologies, along with the enhancement of past simulation optimization techniques, has given rise to an emerging field known as simulation learning and optimization (SLO). Unlike simulation optimization, SLO is a data-driven fusion approach that integrates conventional simulation modeling methodologies, simulation optimization techniques, and learning-based big data analytics (BDA) to enhance the ability to address stochastic and dynamic decision-making problems in the real-world complex system and quickly find the best outcomes for the decision support in both real-time and nonreal-time analyses to achieve the data-driven digital twin capability. Although some literature in the past has mentioned similar applications and concepts, no paper provides a structural explanation and comprehensive review of relevant literature on SLO from both methodological and applied perspectives. Consequently, in the first part of this paper, we conduct a literature review on a novel SLO methodology emerging from the fusion of traditional simulation methodology, simulation optimization algorithms, and BDAT. In the second part of this paper, we review the applications of SLO in various contexts, with detailed discussions on manufacturing, maintenance, redundancy allocation, hybrid energy systems, humanitarian logistics, and healthcare systems. Lastly, we investigate potential research directions for SLO in this new era of big data and artificial intelligence (AI).
The impact of molybdenum and tungsten co-doping on the physical properties of CeO2: First-principles calculations
Authors: Bouhlala Aicha , Doghmane Malika , Halais Wissem Tayeb , Chettibi Sabah
Published: March 2024

In this study, the WIEN2k code, utilizing the full potential linearized augmented plane wave (FPLAPW) approach within the framework of density functional theory (DFT), explicitly employing the PBEsol approximation, is employed to investigate the fundamental properties of W-doped and (Mo, W) co-doped cubic CeO2. Herein, lattice constants, bulk modulus and cell volumes were evaluated for structural properties. The total density of states (TDOS) for various doped and co-doped CeO2 systems was used to explain the origin of magnetic behavior. The band structure (BS) calculations show that the doped and co-doped materials exhibit semiconductor behavior with a direct bandgap in both spin channels. The calculated total spin magnetic moments of W/CeO2 and (Mo, W)/CeO2 alloys are 2.002[Formula: see text][Formula: see text] and 4.007[Formula: see text][Formula: see text] respectively.

Furthermore, the optical absorption shows absorption peaks in the visible range and a redshift. As a result, adding molybdenum and tungsten additives improves the electro-optical properties of previous materials.
A Study on Robotic Arm Target Recognition and Grasping Method Based on Deep Learning
Authors: Shu Yufeng , Xiong Changwei , Chen Chaodong
Published: March 2024

In this research, a 3D visual recognition system has been developed based on Wei deep learning algorithm using GPU. The proposed system consisted of a GPU with depth image function library, which performed image data acquisition, depth information operation, coordinate conversion, image contour search, convolutional class neural network model training, etc., and achieved object pinning by TCP/IP communication with motion control system. The obtained experimental results revealed that the recognition rate of the developed algorithm for target objects at different positions was as high as 92%. Experimental target recognition rates for different angles were relatively low, but reached 87%, and experimental accuracy rates of different luminance values also reached 89%. The errors of robot hand clamping targets also fell within 1–4[Formula: see text]mm, which were higher than experimental expectation.
UAV Target Tracking Algorithm Based on Illumination Adaptation and Future Awareness in Low Illumination Scenes
Authors: Huo Yuan-Lian , Chen Bo , Zhang Jin-Shi , Zhang Qiao-Sen
Published: March 2024

Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized, and an efficient image enhancement module is used to enhance the brightness of low illumination images. Then a future-aware module is constructed to train the tracking model using the contextual information of the target in the next frame for better robustness. Finally, the model updating stage involves adaptive filter updating and adaptive learning rate updating to enhance target tracking precision. The results of the comparison experiments with the state-of-the-art algorithms on the UAVDark135, UAVDT, and DTB70 datasets show that the algorithm in this paper outperforms the state-of-the-art tracking methods and has better tracking performance under light changes and fast motion (FM) scenarios. The tracking speed on a single Central processing unit (CPU) reaches 49 FPS, which satisfies the real-time requirement of UAV tracking.
Prediction of Cleaning Loss of Rice Wheat Combine Harvester Based on Dynamic Bayesian
Authors: Zhang Jing , Zhu Ting , Liu Yang , Zhang Yifan , Zhou Xiancun , Xu Xueying , Jiang Qing
Published: March 2024

The cleaning loss rate is a crucial performance metric for rice-wheat combine harvesters. Most studies on the relationship between the cleaning operation parameters of the combined harvester and the cleaning loss rate lack research on the dynamic correlation between harvest losses and operational parameters, specifically focusing on the dynamic correlation between harvest losses and multiple operational parameters such as the cleaning loss rate. In this paper, we formulate a dynamic deductive model for the scenario of harvesting loss regulation involving multiple operational parameters, delving into the dynamic associations between cleaning loss and the regulation of multiple operational parameters and constructing dynamic Bayesian network prediction model. Through experimental analysis of the dynamic Bayesian network prediction model for cleaning loss rates, the comparison of results among the other three algorithms demonstrated that it was efficient for our DBN to predict the cleaning loss rate.
Secure Virtual Machine Migration and Host Overload Detection Using Modified Pelican Optimization with Variable Load Mean Function
Authors: Parthasarathy S.
Published: March 2024

Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system.
The descriptive complexity of the set of Poisson generic numbers
Authors: Becher Verónica , Jackson Stephen , Kwietniak Dominik , Mance Bill
Published: March 2024

Let [Formula: see text] be an integer. We show that the set of real numbers that are Poisson generic in base [Formula: see text] is [Formula: see text]-complete in the Borel hierarchy of subsets of the real line. Furthermore, the set of real numbers that are Borel normal in base [Formula: see text] and not Poisson generic in base [Formula: see text] is complete for the class given by the differences between [Formula: see text] sets. We also show that the effective versions of these results hold in the effective Borel hierarchy.
Money Market Funds and N-CR Regulations
Authors: Allen Kyle D. , Baig Ahmed , Winters Drew B.
Published: March 2024

In an attempt to make money markets safer and more resilient, regulators have created new requirements for market participants. Money market funds are now required to file a new N-CR form if a significant event occurs: for example, fund financial support or liquidity fees. We investigate whether fund stakeholders respond to the N-CR filings. We find that investors do not respond to the N-CR filings. However, fund managers, who do not need the filings to learn of the financial support, reduce the weighted average life (WAL) of the funds and increase the daily liquidity available. These actions have real costs and our results suggest the costs outweigh the benefit of the newly required Form N-CR (N-CR) filing.
Study on the Stress Histories and Fatigue Stress Spectrums of Longitudinal Connected Slab Track on Bridge During Service Life
Authors: Xu Qingyuan , Wang Xi , Li Bin , Zhang Ze , Zhang Zilong , Wang Lexuan
Published: March 2024

The paper aims to study the stress histories and the fatigue stress spectrums (FSS) of longitudinal connected slab track (LCST) on the bridge during service life, and to provide an important basis for the fatigue test design of slab track (ST) in the laboratory and in situ, fatigue life prediction and fatigue designing of ST for the actual engineering. First, the temperature field model for LCST on bridge, high speed train (HST)-LCST-bridge coupling dynamic model, and LCST-bridge-piers and abutments longitudinal interaction model are established and verified. Then, based on these models, a calculation method of the FSS for the LCST on bridge under combined loads is proposed. Finally, taking the climate of the Guangzhou district in China as an example, the stress histories and FSS of LCST on a multi-span simply supported box girder bridge under combined loads are numerically simulated. The simulation results show that: (1) the stress of each component of LCST is small when considering only the vertical train load, and there will be no fatigue failure for LCST. (2) The temperature gradient load and longitudinal loads of ST significantly affect the stress distribution of LCST, so the combined effects of these loads should be considered when calculating the FSS. (3) The mechanical state of LCST is more severe around the beam end. The concrete stresses of track slab (TS) on the bottom surface and base plate (BP) on the top surface are significantly larger than the concrete stresses of TS on the top surface and BP on the bottom surface, respectively. (4) There are many peaks in the FSS of LCST on bridge under combined loads. Therefore, several probability curves should be used together to fit the FSS of LCST.
Transient Aerodynamic Heating Effects on Aeroelastic Dynamic Response of Curved Fiber-Reinforced Composite Panel in Supersonic Airflow
Authors: Liu Yating , Duan Jingbo , Xu Buqing , Wu Jiaqi
Published: March 2024

The nonlinear aero-thermo-elastic behaviors of the curved-fiber composite panel are studied accounting for transient aerodynamic heating effects with two-way coupling method. The Von Karman assumption is utilized to depict the large deflection of the composite panel and the supersonic aerodynamic load is incorporated through first-order piston theory, respectively. The aerothermal and aeroelastic models are coupled with each other by performing the iterative procedure in the time domain. The aerodynamic heating heat flux is obtained by the Eckert reference temperature method, the transient temperature field is solved by the finite difference method, and the aeroelastic dynamic response is solved by the combination of the Newmark method and Newton–Raphson method. The accuracy and validity of the established model are justified by comparing the present solutions with the present numerical results in published literature. Then, the influences of the key parameters such as the transient temperature field, coupling step, heat transfer coefficient, shock wave, initial disturbance force and fiber configurations on aerothermoelastic responses of the composite panel respectively are discussed in detail.
Dynamic Modeling and Theoretical Analysis of Planar Nonlinear Vibrations of an Articulated Hoop Truss Antenna
Authors: Fang Bo , Wang Yilong , Wu Yi , Han Jae-Hung , Cao Dengqing
Published: March 2024

The dynamic behaviors of large-scale hoop truss antennas (HTAs) are significantly influenced by the nonlinear torque transmission properties of flexible hinges. Due to the nonlinearity of the hinge, the HTA can be easily excited to exhibit internal resonances that result in the energy exchange between the adjacent two or three modes. This paper focuses on the 3:1 internal resonant responses of an articulated HTA induced by the nonlinearity of the hinge. The analytical modes are obtained by using the global mode method (GMM) and are validated by finite element method (FEM). Then the partial differential equations (PDEs) of planar motions are discretized into ordinary differential equations (ODEs) by Galerkin’s technique. The multiple time scale method is employed to obtain the four-dimensional modulation equations of the HTA with primary and 3:1 internal resonance. By using the Newton–Raphson iteration and the pseudo arclength continuation, frequency–response and force–response curves are obtained to investigate the theoretical steady-state vibrations of the HTA. Moreover, the influence of the cubic spring stiffness, damping, and external excitation amplitude on the system’s nonlinear dynamic behaviors are investigated. The numerical simulation reveals that the articulated multi-beam hoop structure exhibits typical nonlinear phenomena such as hardening-spring type characteristic, jumps, bi-stability, and double peaks. The research findings contribute to the reliable structural design and vibration control of the articulated hoop truss antenna.
Effect of Bearing Friction on Seismic Performance of Bridges with Massive Piers Isolated by Friction Pendulum Bearings
Authors: Jiang Xinyan , Li Jianzhong , Xiang Nailiang
Published: March 2024

Although seismic isolation is a well-accepted design strategy for bridges to provide structural earthquake resistance, for bridges with massive piers, it may unexpectedly pose an abnormal influence on the seismic response of bridges due to the effect of substructure mass. This study investigates the effects of bearing friction and pier mass on the seismic performance of bridges isolated by friction pendulum bearings (FPBs). Nonlinear time history analysis is conducted to demonstrate the variation of the bridge peak responses with different bearing frictional coefficients and pier mass. The inherent influential mechanism of the frictional coefficient of FPBs is also discussed in the current study. The results of the analysis show that for FPB-isolated bridges with massive piers, the peak base shear forces of piers first decrease and then increase with the increase of the bearing frictional coefficient. Such a phenomenon may be attributed to the fact that the inertia force of the massive piers will be altered by the variation of bearing friction. The outcome of this study provides a useful reference for practical engineering that the seismic base force of massive bridge piers calculated under a specified bearing frictional coefficient may be smaller than the actual response and cause reduced safety to bridges.
Sharp bounds for the second Zagreb index and co-index under trees of given conditions
Authors: Zhao Ziyang , Chen Zunzheng
Published: March 2024

The second Zagreb index and co-index are two important graph invariants. They are defined as [Formula: see text] and [Formula: see text], respectively, where [Formula: see text] is the complement of G and [Formula: see text] is the degree of a vertex u in G. In this paper, we study the upper and lower bounds for second Zagreb index and co-index of trees. Moreover, we determine the conditions that the upper and lower bounds for second Zagreb index and co-index of graphs are equal.
Who Determines What Is Relevant? Humans or AI? Why Not Both?
Authors: Faggioli Guglielmo , Dietz Laura , Clarke Charles L. A. , Demartini Gianluca , Hagen Matthias , Hauff Claudia , Kando Noriko , Kanoulas Evangelos , Potthast Martin , Stein Benno , Wachsmuth Henning
Published: March 2024

A spectrum of human-artificial intelligence collaboration in assessing relevance.
Local Interpretations for Explainable Natural Language Processing: A Survey
Authors: Luo Siwen , Ivison Hamish , Han Soyeon Caren , Poon Josiah
Published: March 2024

As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models. This work investigates various methods to improve the interpretability of deep neural networks for Natural Language Processing (NLP) tasks, including machine translation and sentiment analysis. We provide a comprehensive discussion on the definition of the term
interpretability
and its various aspects at the beginning of this work. The methods collected and summarised in this survey are only associated with local interpretation and are specifically divided into three categories: (1) interpreting the model’s predictions through related input features; (2) interpreting through natural language explanation; (3) probing the hidden states of models and word representations.
Lay User Involvement in Developing Human-centric Responsible AI Systems: When and How?
Authors: Vincenzi Beatrice , Stumpf Simone , Taylor Alex S. , Nakao Yuri
Published: March 2024

Artificial Intelligence (AI) is increasingly used in mainstream applications to make decisions that affect a large number of people. While research has focused on involving machine learning and domain experts during the development of responsible AI systems, the input of lay users has too often been ignored. By exploring the involvement of lay users, our work seeks to advance human-centric responsible AI development processes. To reflect on lay users’ views, we conducted an online survey of 1,121 people in the United Kingdom. We found that respondents had concerns about fairness and transparency of AI systems, which requires more education around AI to underpin lay user involvement. They saw a need for having their views reflected at all stages of the AI development lifecycle. Lay users mainly charged internal stakeholders to oversee the development process but supported by an ethics committee and input from an external regulatory body. We also probed for possible techniques for involving lay users more directly. Our work has implications for creating processes that ensure the development of responsible AI systems that take lay user perspectives into account.
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
Authors: Zhu Yunchang , Pang Liang , Wu Kangxi , Lan Yanyan , Shen Huawei , Cheng Xueqi
Published: March 2024

Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. While this generally improves performance on average, the extra neurons do not yield a consistent improvement for all instances. This is because some hidden neurons are redundant, and the noise mixed in input neurons tends to distract the model. Previous work mainly focuses on extrinsically reducing low-utility neurons by additional post- or pre-processing, such as network pruning and context selection, to avoid this problem. Beyond that, can we make the model reduce redundant parameters and suppress input noise by intrinsically enhancing the utility of each neuron? If a model can efficiently utilize neurons, no matter which neurons are ablated (disabled), the ablated submodel should perform no better than the original full model. Based on such a comparison principle between models, we propose a cross-model comparative loss for a broad range of tasks. Comparative loss is essentially a ranking loss on top of the task-specific losses of the full and ablated models, with the expectation that the task-specific loss of the full model is minimal. We demonstrate the universal effectiveness of comparative loss through extensive experiments on 14 datasets from three distinct NLU tasks based on five widely used pre-trained language models and find it particularly superior for models with few parameters or long input.
A Proof of the Brill-Noether Method from Scratch
Authors: Berardini Elena , Couvreur Alain , Lecerf Grégoire
Published: March 2024

In 1874 Brill and Noether designed a seminal geometric method for computing bases of Riemann-Roch spaces. From then, their method has led to several algorithms, some of them being implemented in computer algebra systems. The usual proofs often rely on abstract concepts of algebraic geometry and commutative algebra. In this paper we present a short self-contained and elementary proof that mostly needs Newton polygons, Hensel lifting, bivariate resultants, and Chinese remaindering.
Influence of the Complex Interface between Transport and Catalyst Layer on Water Electrolysis Performance
Authors: Ma Tien-Ching , Hutzler Andreas , Bensmann Boris , Hanke-Rauschenbach Richard , Thiele Simon
Published: March 2024

The interface design between anode catalyst layer (ACL) and porous transport layer (PTL) significantly influences the performance of proton exchange membrane water electrolyzers. Lately, the influence of the ACL/PTL interface on performance is more intensively investigated, including modeling approaches. Contrary to other models that apply through-plane resolved modeling, in-plane models better characterize the ACL/PTL interface. These models separate the interface into three domains: in an open pore area (P), under a contacted solid of the PTL (S), and the interfacial point between the pore and solid (S│P). In our work, we focused on the behavior of the model in the kinetic region, in which no two-phase behavior is to be expected. Consequently, we apply a one-phase model as the main model and a simple two-phase model for comparison. We find that for most reference samples, the one-phase model well describes polarization behavior. However, for samples with larger interfacial contact area, a two-phase model might explain the found effect better even for very low current densities. Finally, we show that the one-phase model and the simple two-phase model can be used to study the general behavior of different solid to pore ratios to guide electrode development in the future.
Fault Detection of In‐Service Bridge Expansion Joint Based on Voiceprint Recognition
Authors: Dong Yiqing , Wang Dalei , Pan Yue , Di Jin , Chen Airong
Published: March 2024

Bridge expansion joints (BEJs) in service are susceptible to damage from various factors such as fatigue, impact, and environmental conditions. While visual inspection is the most common approach for inspecting BEJs, it is subjective and labor‐intensive. In this paper, we propose a novel methodology for detecting the fault status of BEJs, inspired by voiceprint recognition (VPR) based on audio signals. We establish an Artificial Neural Network to filter nonevent segments from low signal‐to‐noise ratio signals, achieving an AuC value of 0.981. We design and improve ConFormer VPR models with a multifeature aggregation strategy and cascade them to realize fault detection of BEJs. For three successive tasks in classifying environment sound types, vehicle impact types, and faults, the ConFormer VPR models achieve AuC values of 0.975, 0.925, and 0.886, respectively, demonstrating the feasibility of our methods for unmanned inspection of BEJs. In future research, the introduction of multiple types of damage and the implementation of benchmarking tests are planned to further enhance the capabilities of the system.
Changes of Serum Adiponectin Level in Patients with Obstructive Sleep Apnea Hypopnea Syndrome and Its Relationship with Sleep Monitoring Indexes
Authors: Li Ji , Zhou Kejing , Chen Xing
Published: March 2024

Objective. To observe the changes of serum adiponectin (AP) levels in patients with obstructive sleep apnea hypopnea syndrome (OSAHS) and explore the correlation between serum AP and polysomnography (PSG) parameters in patients with OSAHS. Methods. The data of subjects who underwent PSG at the hospital between January 2021 and December 2022 were collected retrospectively and divided into simple snoring group (AHI < 5 times/h, n = 45), mild OSAHS group (5 ≤ AHI < 15 times/h, n = 63), moderate OSAHS group (15 ≤ AHI ≤ 30 times/h, n = 52), and severe OSAHS group (AHI > 30 times/h, n = 60). The general data, PSG indices, and serological indices of the subjects were collected and compared between groups. Pearson correlation analysis and partial correlation analysis were employed to examine the correlation between serum AP level and PSG parameters. Ordered logistic regression was employed to analyze the risk factors influencing the severity of OSAHS. The predictive capability of the serum AP level in determining the occurrence of OSAHS was assessed using ROC. The serum AP levels of subjects with different subtypes of PSG indicators were compared. Results. In the simple snoring group, mild OSAHS group, moderate OSAHS group, and severe OSAHS group, there were statistically significant differences in microarousal count, MAI, AHI, times of blood oxygen decreased by ≥ 3%, L-SaO2, and TS90% among the 4 groups (P<0.05). The level of serum AP was positively correlated with L-SaO2 and negatively correlated with the proportion of REM, microarousal count, MAI, AHI, times of blood oxygen decreased by ≥ 3%, TS90%, and LP (P<0.05). High AHI was a risk factor affecting the severity of OSAHS (95% CI: 1.446–4.170). The AUC of serum AP level in diagnosing OSAHS was 0.906 (95% CI: 0.8601–0.9521), and when the Youden Index was 0.678, the sensitivity was 88.9%, and the specificity was 78.9% (P<0.0001). In the population with a high microarousal count, high AHI, and high times of blood oxygen decreased by ≥ 3% and high TS90%, the serum AP level was lower than that in the low-level population (P<0.05). In the population with high L-SaO2, the serum AP level was higher than that in low-level population (P<0.05). Conclusion. The level of serum AP decreased with the increase of the disease severity of patients with OSAHS and demonstrates a significant predictive capability for the occurrence of OSAHS. Monitoring the level of serum AP can effectively forecast the risk of OSAHS. Furthermore, alterations in serum AP levels are associated with both hypoxemia and a heightened frequency of arousal in patients with OSAHS.