bluuebunny/crossref_metadata_2025 huggingface dataset:
‘The small girl with the huge dildo is what we needed’: The politics of sex toy in Cocks Not Glocks protest
Authors: Kähkönen Lotta
Published: July 2024

The University of Texas at Austin became the epicenter of the gun debate in fall 2016 as a student-led protest against the campus carry law grew into a movement known as Cocks Not Glocks. Utilizing humor and slogans, the activists adapted the dildo as a symbol of defiance. This article focuses on how the dildo as a protest tool gained agency. The subversive potential of the dildo was in its affective ambivalence, which was augmented in Texas, where the state law censors the public brandishing of sex toys. Through an analysis of media coverage and interviews with activists, students and faculty belonging to the University of Texas at Austin (UT Austin) community, the article demonstrates the cultural meaning-making and complex relationality of the dildo, an object shaped by competing meanings in relation to obscenity laws, sex-positivism and debates on gendered gun culture. Drawing from Sara Ahmed’s theorization of how objects may become sticky as an effect of substitutions, the article argues that the affective ambivalence of the dildo worked in the protest as a gesture that moved between sexual desires and political demands about gendered gun culture and public security in the United States.
Unemployment protection for the self-employed and platform workers in Hungary
Authors: Jakab Nóra
Published: July 2024

This article analyses unemployment protection for platform workers and the self-employed in Hungary. Hungarian legislation lacks a separate, well-designed system for the self-employed and platform workers, who consequently fall under the general unemployment scheme. The Hungarian unemployment scheme is fundamentally a compulsory, Bismarckian-type of unemployment insurance financed by contributions, which acts as compensation for loss of work. The Hungarian legislation also contains a system of services and subsidies that could be considered as a separate protection scheme also addressing unemployment. In this case, the protection provided compensates absence of work, rather than a lack of work. This contribution presents the protection available under Hungarian law for the self-employed and platform workers, via an in-depth analysis of the entitlement conditions, contribution base, and the amount, duration and financing of the unemployment benefit. In this paper we also highlight the difficulties and consequences of the taxation regime for the self-employed and platform workers. As they can pay contributions based on favourable tax rules, these lower social security contributions result in lower unemployment benefits.
Using mobile air cleaners in school classrooms for aerosol removal: Which, where and how
Authors: Ding Er , Giri Arghyanir , Gaillard Antoine , Bonn Daniel , Bluyssen Philomena M
Published: July 2024

Mobile air cleaners (MACs) have been proposed as a supplementary solution to combat the spread of respiratory aerosols in school classrooms. To determine which, where and how to use MACs, seven small- and medium-sized MACs were selected and assessed for different settings and configurations by 1) a decay test for determining the clean air delivery rate (CADR), and 2) a perception test with a panel of subjects, together with physical measurements, of noise and air movement. The findings show that to achieve the desired CADR (appr. 1000 m3/h for 30 students per classroom), the key factors are the induced airflow pattern and the location of the MACs. MACs with an upward air supply toward the occupied zone showed much higher CADR (max. 775–1332 m3/h) than those with a horizontal air supply (max. 219–333 m3/h). Moreover, using multiple devices simultaneously was crucial when the room size was increased, and combining mechanical ventilation could improve aerosol removal. Achieving a sufficient CADR would always lead to a noise level above the limit of 35 dB(A), yet sometimes the rating of the panel was more than 50% acceptable. The air velocities mostly fulfilled the requirement (<0.2 m/s), which aligned with the positive panel assessment. Hence, the evaluation by a panel of subjects can help to optimize the use of MACs in a classroom.
‘Standing with Soldier F’: Bloody Sunday, disrupting the degradation ceremony and the court of public opinion
Authors: Hearty Kevin
Published: July 2024

This article merges the literatures on crime and punishment, law and performance and transitional justice to critically examine how high-profile prosecutions for historic state violence become contested in societies attempting to address the legacy of prolonged conflict. Drawing empirically from the case study of the prosecution of Soldier F for the 1972 Bloody Sunday killings in the North of Ireland, it demonstrates how legacy case prosecutions become a proxy for wider societal and political disagreement over the causes and consequences of past violence. It argues that when the legal basis for prosecution becomes obscured by extra-legal factors the expressivist function of punishment and criminal law is fundamentally undermined. By concentrating on these extra-legal factors rather than focusing on the legal semantics of the case, certain constituencies can challenge the legitimacy of the prosecution, question whether it is helpful to a post-conflict society that needs to ‘move on’ and prevent the accused being ‘othered’ as an ‘outsider’. In disrupting the expressivist logic of criminal prosecution like this, it is concluded the accused can be reframed by sympathetic audiences as a victim who needs solidarity and support rather than a victimiser who needs to be denounced and punished.
Successful completion of orthodontic therapy in a patient with osteopetrosis: Case Report
Authors: Brooks John K , Height Lillian M , Jimenez Beverly A , Anyadike Anthony C , Kvint Joseph G , Price Jeffery B
Published: July 2024

Introduction:

Orthodontic therapy in patients with osteopetrosis (OP) of the jaws has typically been contraindicated owing to the presence of poorly perfused and extremely compact bone, and the potential risk for infection and osteomyelitis. As such, completed orthodontic cases in association with OP have rarely been published.

Patient concerns:

A patient aged 14 years 6 months, with no known diagnosis of OP, sought orthodontic assessment for anterior crowding.

Clinical findings:

The patient exhibited a straight facial profile and increased mandibular facial height, competent lips, shallow mentolabial sulcus with mild mentalis strain, flat/reverse smile arc and wide buccal corridors on smiling.

Primary diagnosis:

The patient had a Class I incisor relationship on Class I skeletal bases with bilateral Class I molars and Class II canine relationships. This was complicated by a crossbite involving the lateral incisors and a Bolton discrepancy due to small maxillary lateral incisors. A radiologic assessment revealed polyostotic OP of the oromaxillofacial complex.

Interventions:

Treatment consisted of maxillary and mandibular fixed orthodontic therapy, bite turbos and elastics to level and align the dentition. Extractions of permanent teeth were not needed.

Outcomes:

At the conclusion of treatment, there was a slight left Class II malocclusion, with incomplete intercuspation on the left side due to tooth size discrepancy, possibly attributed to inadequate elastics compliance and the presence of osteopetrotic bone. The treatment was completed in 3 years, 1 year longer than anticipated.

Conclusion:

This report represents the second published account of a patient with OP successfully managed with comprehensive orthodontic care and without osseous complications. Obtaining cephalometric measurements on OP-affected patients may be imprecise owing to the presence of extremely dense bone and difficulty to identify bony landmarks. To reduce osteopetrotic sequelae, attending clinicians should consider reduced exertional orthodontic forces and closely monitor patients for adverse alveolar events.
Phenomenological model for unburned hydrocarbon emissions from spark-ignition, pre-chamber, and dual-fuel internal combustion engines
Authors: Malfi Enrica , Esposito Stefania , De Felice Massimiliano , Pitsch Heinz , Pischinger Stefan , De Bellis Vincenzo
Published: July 2024

Considering the strict regulations on the transport sector emissions, predictive models for engine emissions are essential tools to optimize high-efficient low-emission internal combustion engines (ICE) for vehicles. This aspect is of major importance, especially for developing new combustion concepts (e.g. lean, pre-chamber) or using alternative fuels. Among the gaseous emissions from spark-ignition (SI) engines, unburned hydrocarbons (uHC) are the most challenging species to model due to the complexity of the formation mechanisms. Phenomenological models are successfully used in these cases to predict emissions with a reduced computational effort. In this work, uHC phenomenological model approaches by the authors are further developed to improve the model predictivity for multiple variations including engine design, engine operating parameters, as well as different fuels and ignition methods. The model accounts for uHC contributions from piston top-land crevice, wall flame quenching, oil film fuel adsorption/desorption and features a tabulated-chemistry approach to describe uHC post-oxidation. With the support of 3D-CFD simulations, multiple novel modelling assumptions are developed and verified. The model is validated against an extensive measurement database obtained with two small-bore single-cylinder engines (SCE) fuelled with gasoline-like fuel, one with SI and one with pre-chamber, as well as against data from two different ultra-lean large-bore engines fuelled with natural gas (one equipped with a pre-chamber and one dual-fuel with a diesel pilot). The model correctly predicts the trends and absolute values of uHC emissions for all the operating conditions and the engines with an accuracy on average of 11.4%. The results demonstrate the general applicability of the model to different engine designs, the correct description of the main mechanisms contributing to fuel partial oxidation, and the potential to be extended to predict unburned fuel emissions with other fuels.
Spray characteristics of elliptical orifice spray in diesel engine under air movement conditions
Authors: Jia Hekun , Cao Xiangyu , Yin Bifeng , Wei Zhuangbang
Published: July 2024

The development of high-quality mixture is a critical requirement for achieving high-efficiency and low-emission diesel engines, and fuel injection system performance improvement and in-cylinder airflow organization are crucial ways for achieving high-quality mixture. The elliptical nozzle is used as the research object in this research, and numerical simulation is utilized to investigate the effect of different airflow speeds and directions on the atomization characteristics of the elliptical nozzle jet, in order to provide a theoretical basis for the engineering application of the elliptical nozzle in diesel engines. The results show that under the same airflow conditions, the vertical penetration distance of the spray decreases while the horizontal penetration distance increases with the use of elliptical orifices, the surface wave perturbation on the windward side is more violent, and reduce spray field the Sauter Mean Diameter (SMD). The spray projected area grew by 13.5%, the spray SMD dropped by 14.8%, and the vertical penetration distance of the spray with elliptical orifices fell by 18% with an increase in airflow velocity from 0 to 20 m/s. When the airflow direction and the spray direction were at a 90° angle and the SMD was lower than that of the circular orifice by 12.9%. The angle between the airflow direction and the short axis of the elliptical orifice was 30°when the spray projection area was larger, the perturbation of the spray body was more intense, and the surface wave amplitude was larger.
Optimization of NOX emissions of a CRDI DIESEL engine using CMA-ES method
Authors: Berk Seyfullah , Alptekin Ertan
Published: July 2024

Engine calibration is the tuning of embedded parameters in the engine control unit (ECU) software to improve vehicle characteristics and meet legal requirements. Due to the stricter emission limits and rising customer expectations, current ECU software may include variables up to 30,000, which require very much time for engine calibration development. For this reason, automotive manufacturers continuously develop mathematical-based optimization methods to find optimum operating conditions for the engines. This study aimed to develop an online optimization algorithm to conduct automated dynamometer tests in the calibration development process. A modified covariance matrix adaptation (CMA) algorithm, which is an evolutionary strategy (ES) method belonging to meta-heuristic optimization, was integrated with an automation system for online calibration optimization. Some CMA method parameters such as step size and damping factor were initially revised to achieve the method to function efficiently in online engine calibration. Optimization was conducted at three different operating points of a 2-liter common rail direct injection (CRDI) diesel engine, where NOx emission mainly impacts the New European Driving Cycle (NEDC) results. The main injection timing, rail pressure, pilot injection quantity and timing, manifold pressure, and mass air flow were controlled in the optimization process. Optimization targets were determined according to the NOx-PM Pareto curve for each operating point. Covariance matrix adaptation was used to generate Pareto curves. Sixty-five measurements were taken for each operating point in the optimization process. Once optimization targets were determined, optimization occurred at each operating point. A total NOx emission reduction of 3.8% was obtained in the NEDC test, while fuel consumption and PM remained almost the same at steady-state operating points. The modified CMA-ES algorithm is expected to be an efficient method for online calibration optimization.
A social researcher researching social researchers – Lessons from feminist epistemologies
Authors: Schwoerer Lili
Published: July 2024

Qualitative research literature discusses how power shapes the interview process and the resulting data and explores the epistemic basis for interview research theoretically. However, processes of negotiating epistemic authority in the interview situation, and in data analysis, are investigated less frequently. This paper draws on 34 interviews with social science academics interested in gender, feminist and queer studies in four English universities to reflect on the epistemological challenges of researching social researchers about their work. Through this, it contributes to explorations of how, in qualitative interviewing and data analysis, we can combine a critical reading of interview data with a commitment to respondents’ accounts of their realities. I argue that Black, anti-colonial, queer, feminist epistemological approaches can be well suited to navigate this challenge. I advocate for an epistemic reflexivity that acknowledges the fluidity of speaker positions while taking structural power relations, and their effects on epistemology, seriously.
Review essay: The quest for meaning
Authors: Delanty Gerard
Published: July 2024

This essay is a review of Raymond Geuss’s book, Seeing Double. The review discusses the seven essays that comprise the book, which is concerned with the implications of the fact that the world never makes complete sense to us and that there are only fragments of meaning without any higher order or authority to make sense of the world. The book explores the consequences of abandoning the idea of a unity view of the world. The review essay discusses some of the issues around an alternative perspective that prioritizes plurality.
Digitization processes at the neighborhood scale: Infrastructure, governance, community, and practices
Authors: Hatuka Tali , Elhanan Gal
Published: July 2024

This paper links the discourse on neighborhoods with the discourse on digitization. From an analytical perspective, digitization is no longer viewed as an independent infrastructure but one that is physically located and affects materiality, sociability, and daily conduct. From the normative perspective, it is becoming clear how differences in digital infrastructure/services/use among and within neighborhoods influence social resiliency and access to resources. Exploring these ideas, this paper offers a thematic review of four key areas, infrastructure, governance, community, and practices, while addressing critical ideas in studies of neighborhoods pre-digitization and neighborhoods supported by digitization. This review is followed by an elaboration of the main shifts, open questions, and gaps in the literature. The paper concludes with a discussion on the neighborhood in the digital age, a much-needed lens for understanding both past and contemporary development, and a pathway leading to better neighborhood futures.
An Indigenous scholar’s journey towards decolonizing social work
Authors: BlackDeer Autumn Asher
Published: July 2024

Tonóéva Mo'⊙htáeváótséva ná-heševehe. Ná-tsėhéstahe. I seek to begin this work in a good way by introducing myself and my nation; I am Dr Autumn Asher BlackDeer, sovereign member of the mighty Southern Cheyenne Nation. It is an honor to be of the Little Calf bloodline, to be part of the legacy of my grandfather Sam B. Deer, a sacred arrow keeper, medicine man, and camp crier for our people. As my ancestors have served our people and created a path for future generations, I strive to do the same, honoring their legacy and furthering their work, standing on the shoulders of giants. The present work is a weaving of a storytelling approach with an academic lens to sharing pieces of my journey as an Indigenous scholar towards decolonizing social work.
Overcoming the Myths: Interactive Learning Environments as Myth Busters in Teaching and Learning
Authors: Deibl Ines , Zeitlhofer Ines , Geroldinger Anna , Zumbach Jörg
Published: July 2024

Recent research has shown a growing interest in myths about teaching and learning and their prevalence among different populations. However, little is known about the extent to which these myths are prevalent among high school students. Future research must focus on methods that best support dispelling these myths to promote high school students in terms of knowledge security, recognition of fake news, and critical thinking, to foster their acquisition of competencies and to implement conceptual change. In the field of natural sciences, studies have shown that the use of learning videos can be an effective method to provoke conceptual change. For this purpose, two versions of a digital learning video (interactive [ N = 36] vs. not interactive [ N = 35]) have been developed to help high school students overcome five common and widely distributed myths (e.g., the learning style myth). The aim of our study was to analyze whether educational videos in an educational psychology context, in this case, neuromyths, can also lead to conceptual change. Furthermore, we wanted to know what role the degree of interactivity of the learning video plays in this. Results reveal that the interventions lead to a reduction of beliefs in myths, but only for those myths that were presented within the interventions. Results also reveal that the interactivity of the video did not make a difference in whether students believed in myths or not.
A new damage detector for bridges based on natural frequencies with missing data
Authors: Dederichs Anno Christian , del Pozo Gabriel A , Svendsen Bjørn T , Øiseth Ole
Published: July 2024

Automatic structural health monitoring can simplify the surveillance process of many structures and bridges if its underlying methods return correct interpretations of the structural state. A common method to differentiate between a damaged and undamaged state of a structure is to use its modal properties from an assumed undamaged state to build a baseline to which all new information is compared. The comparison can be performed by calculating the Mahalanobis squared distance (MSD) of natural frequencies. Considering the inherent uncertainties associated with automatic system identification, a new novelty detection algorithm is proposed in this work, intended to work with missing and randomly available natural frequency information, like the outcome of automatic operational modal analysis and mode tracking algorithms. The moments of a multivariate normal distribution used to characterize the bridge’s undamaged behavior are determined elementwise. The damage indicator measures the MSD of new data points to this distribution considering the available natural frequencies and normalizes it using the chi-squared nature of the MSD. The proposed method works as intended for two numerical cases with 25% of the natural frequency values missing at random, where all but the smallest of damages become clearly detectable. It is also tested on two real-world bridges, one of which has a small, controlled change to its structural state. The automatic operational modal analysis of the bridges’ data recordings leads to randomly missing natural frequency values. Despite this, the damage can be detected by the proposed novelty detection algorithm.
Deep hybrid neural network-aided electromechanical impedance method for automated damage detection of lining concrete under freeze-thaw cycling
Authors: Zhang Chuan , Yan Qixiang , Liao Xiaolong , Qiu Yunhui , Zhang Yifeng , Wang Ping
Published: July 2024

Cold regional tunnels extensively suffer from severe damage in concrete linings under cyclic freeze-thaw environment. Therefore, accurate detection and evaluation of cyclic freeze-thaw damage within lining concrete is of great significance to help grasp structural health state and guarantee timely maintenance. This study pioneered the application of electromechanical impedance (EMI) method to monitor the freeze-thaw damage in bended concrete beams. The mass loss and flexural strength degradation of concrete beams under two different bending loads were thoroughly assessed to quantify the evolution of cyclic freeze-thaw damage. Moreover, the conductance signatures driven by d31 and d33 modes were analyzed, respectively. It was found that the variation in the d31 mode-dominated signal well agreed with the progressive damage characterized by the flexural strength degradation. The key innovation of this study is that a deep hybrid neural network DenseNet–GRU was constructed and well trained to predict the cyclic freeze-thaw damage from augmented EMI data. The results indicated that the proposed model achieved excellent performance with determination coefficients exceeding 0.997 for both bending scenarios. Additionally, DenseNet–GRU outperformed conventional baseline machine or deep learning models in prediction accuracy and noise-resistance capacity. Notably, it demonstrated good adaptability when trained with limited data samples. In summary, the proposed methodology enabled automated detection and accurate forecasting of the cyclic freeze-thaw damage in lining concrete without hand-crafted features.
An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures
Authors: Lee Guan-Wei , Livadiotis Stylianos , Salamone Salvatore
Published: July 2024

We introduce an explainable variational autoencoder for three-dimensional (3D) localization of acoustic emission sources in hollow cylindrical structures, with an unsupervised approach. This research capitalizes on multi-arrival waveforms generated by helical path propagation in cylindrical geometries to enable efficient two-receiver localization. By integrating the modal characteristics of Lamb modes under multi-path conditions, we demonstrate that two sets of time-of-arrival differences and peak amplitudes extracted from one receiver can serve as effective localization features. This initial approach identifies four potential source locations, highlighting the feasibility of two-receiver source localization using traditional feature extraction methods. However, direct extraction can be challenging when mode overlaps occur, complicating the localization process. To address this, our work proposes a novel waveform-based method. This method leverages the consistent dispersion characteristics within isotropic materials, where each unique combination of mode arrival times and peak amplitudes constructs a distinct waveform. This distinctiveness overcomes the ambiguities associated with mode overlaps, significantly enhancing the method’s precision and robustness. Our approach adopts a data-driven strategy for waveform-based localization using variational autoencoder (VAE). VAE discerns waveform patterns for localization, while also addressing data uncertainties. The VAE’s encoder and decoder networks capture the localization process and the source’s influence on waveform generation, respectively, guiding latent variables to segregate waveforms by source in the latent space. The design of the learning process focuses on specific localization characteristics to enhance result explainability. Localization predictions are generated by projecting test waveforms, not included in the training set, onto a trained latent space. The prediction is determined using a nearest-neighbor approach based on the closest latent representation of a source. Validation with pencil-lead-break tests on a metallic pipe confirmed our method’s effectiveness, achieving an averaged 3D localization accuracy of 0.84.
Detection and time-of-arrival estimation of impact-induced waves in composite laminates
Authors: Grasboeck Lukas , Humer Alexander , Benjeddou Ayech
Published: July 2024

This work assesses experimentally three selected methods for estimating the time of arrival (TOA) of guided waves generated by impacts on a carbon fiber reinforced polymer plate-like structure. This is reached by the individual evaluation of the continuous wavelet transform (CWT), the modified energy ratio (MER), and the Akaike information criterion (AIC). Considering the anisotropic nature of the laminated structure, the propagation of guided waves within such thin-walled structure presents intricate challenges. The utilization of surface-bonded piezoceramic patch sensors aids to detect and measure wave propagation across the plate. The experiments involved impacting the plate at various positions using an impulse hammer and an impact gun, generating a comprehensive dataset encompassing signals captured by sensors placed at different locations. Our investigations have revealed that, with specific modifications, these methods offer effective means of estimating the TOA. In the CWT framework, the introduced frequency domain threshold crossing technique enhances precision in the TOA estimation. To address the slight delays in TOA estimations using the MER method, we refined the method by identifying the first local maximum that exceeds a predefined threshold. This enhancement significantly improves the method’s ability to estimate the TOA of sensor signals. Among the explored methods, the AIC stands out for its precise detection of initial impact-induced signal alterations. This method consists of two sequential steps, with the second step notably influencing its outcome. By incorporating local minima from the method-specific function, instead of relying solely on the global minimum, a significant improvement in estimation accuracy was observed in several cases. Nevertheless, our modification led to notably inaccurate estimates in certain cases, indicating instances where the estimates were significantly too early. These findings offer valuable insights into successful TOA estimation, facilitating potential applications such as impact localization and guiding further research in this domain.
Multiscale ECA network: a rotation mechanical domain adaptation method with minimal class confusion
Authors: Li Xueyi , Yu Tianyu , Su Kaiyu , Yuan Peng , Xie Zhijie
Published: July 2024

Unsupervised rotation mechanical fault diagnosis methods have become popular, but existing unsupervised methods still have some issues. For example, it is challenging to capture vibration signal features at different scales and to address partial class confusion. To improve the diagnostic performance, this study introduces a multiscale, efficient channel attention (ECA) attention mechanism, joint adaptation network (JAN), and minimum class confusion (MCC) for addressing the aforementioned issues. First, the authors design a multiscale fault feature extraction module to capture discriminative information at different scales in vibration signals. Second, the authors introduce the ECA mechanism to weight the extracted features at the channel level, enhancing useful features and suppressing redundant features. Then, the authors employ the JAN method to establish local maximum mean discrepancy, enabling adaptation between corresponding subdomains of the source and target domains, avoiding the problem of being too close. Finally, the authors use MCC as the loss function to reduce prediction confusion between correct and ambiguous categories in target samples, thus improving transfer performance. Experimental results demonstrate that the proposed method exhibits excellent performance in unsupervised rotation mechanical fault diagnosis tasks.
A temperature compensation approach using dynamic time warping for electro-mechanical admittance-based concrete structural damage identification
Authors: Li Hedong , Luo Yaozhi , Ai Demi
Published: July 2024

Ambient temperature effect on electro-mechanical admittance (EMA) signals always imposes a threat to the accurate identification of concrete damages when using surface-mounted piezoelectric lead zirconate titanate transducers. To reduce adverse temperature effect on the EMA signals, this paper proposed a dynamic-time-warping-based temperature compensation approach for concrete structural damage identification, making use of the similarity between two series via accumulating Euclidean distance under admissible temporal alignments. Validating experiments were conducted on a lab-scaled concrete cube with artificial cracks, and practical application on a full-scaled assembled tunnel structure undergone bolt-loosened defects. The approach was sufficiently verified through comparing with the traditional effective frequency shift method for restoration of the conductance signatures altered by temperature. Experimental results demonstrated that the approach possessed superior performance both for pure temperature compensation and temperature-compensated damage identification to the traditional one, which is promising for practical extension to the in situ concrete infrastructural health monitoring.
Damage identification of a jacket platform based on a hybrid deep learning framework
Authors: Xin Su , Qi Zhang , Yang Li , Yi Huang , Ziguang Jia
Published: July 2024

Given the complex operational environment of offshore platforms, accurate identification of structural damage has become a crucial aspect of structural health monitoring. However, accurately pinpointing the damage locations based on vibration data under load, particularly for intricate platform structures, is a challenging task. Existing damage-identification methods, particularly those rooted in deep learning frameworks, often encounter difficulties when applied to marine platforms. Therefore, this study proposes an innovative approach. The accuracy of damage identification for marine platforms operating under unique service conditions was enhanced by introducing a deconvolutional parallel processing module and an auxiliary loss function processing module into the core ResNet50 network. This enhancement improved the accuracy of the model in detecting damage within complex marine structures. Information processing is enriched by fusing the vibration data acquired from the measurement points across different domains: time, frequency, and recurrence plots. The results of this approach were remarkable. When the algorithm model, validated through model experiments, is extended to a digital twin established based on real marine platforms, simulations and loading under real loads were performed on a refined high-fidelity finite-element model, yielding dynamic response information that closely mirrored real-world conditions. A corresponding damage-recognition database was established to support the digital twin system. For the eight different directions, the model accuracy ranged from a minimum of 87.38% to a maximum of 92.27%. This represents a significant advancement compared to the performance of the original network. Empirical experiments substantiated the efficacy of the improved algorithm, demonstrating an impressive recognition accuracy of 93.75%. This achievement underscores the potential of this method to revolutionize damage identification for marine platforms, particularly under the distinctive conditions that these structures encounter. The integration of specialized modules and enhanced processing methodologies further bolster the accuracy of deep-learning-based damage identification and makes the building of digital twin models of offshore platforms feasible.
A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on Domain Generalized Graph Convolution Autoencoder
Authors: Zhao Xiaoli , Hu Yuanhao , Liu Jiahui , Yao Jianyong , Deng Wenxiang , Hu Jian , Zhao Zhuanzhe , Yan Xiaoan
Published: July 2024

The data measured by the servo motor-bearing system under complex working conditions will present problems such as amplitude fluctuations, unequal impact intervals, and significant differences in data distribution, and so forth. However, the most intelligent fault diagnosis focus on deep learning or transfer learning, which cannot complement knowledge transfer and generalized diagnosis with the structural neighbor relationship under unknown conditions or cross-machine samples. By utilizing Domain Generalized Graph Convolution Autoencoder (DGGCAE), a novel intelligent multicross domain fault diagnosis method for servo-motor bearing systems can be developed. Specifically, the Dirichlet Mixup and Distilled augmentations are first employed to augment the domain data of the feature and label layer for model training. Accordingly, graph representation learning on multisource domain data is mainly performed for the developed algorithm. Afterward, the graph convolutional autoencoder is employed to extract enough generalized high-dimensional features. Furthermore, DGGCAE’s classification loss and domain discrimination loss can be calculated to narrow the distribution gap among multisource domains. Finally, the fault simulation test bench (called servo motor-Cylindrical roller bearing system from Nanjing University of Science and Technology) has validated the development of the diagnostic method.
A semisupervised fault frequency analysis method for rotating machinery based on restricted self-attention network
Authors: Zhang Huaqin , Hong Jichao , Yang Haixu , Zhang Xinyang , Liang Fengwei , Zhang Chi , Huang Zhongguo
Published: July 2024

With the development of informatization and digitalization, condition monitoring has been applied to industrial equipment such as rotating machinery. Collecting and storing large amounts of equipment operating data enable the detection of mechanical equipment faults using historical operational data. This article proposes a semisupervised data-driven approach to analyze the fault frequencies of rotating machinery. Frequency band information and the degree of association with faults are obtained through the variance of attention values. To address the inherent issue of decoupling information between data segments in deep learning, restrictive layers are proposed. These layers prevent the flow of information between data segments from rendering interpretable information ineffective. Bearing and gearbox datasets are used to validate the proposed method. The fault frequencies extracted by this method correspond to actual faults. The preferred deep learning framework achieves an accuracy exceeding 99% on both datasets. The method is compared with various signal processing methods and identifies fault frequencies that are difficult to identify using traditional methods. Furthermore, the unreliability of traditional deep learning in fault diagnosis is also exposed. In this study, semisupervised deep learning fault frequency extraction is achieved for the first time.
Plasma Levels of Thiamine and Histamine in Childhood-Onset Stuttering
Authors: Alqhazo Mazin T. , Newbury Dianne F. , Rashaid Ayat B.
Published: July 2024

This study aims to compare the concentrations of thiamine and histamine in the samples of 41 normal fluent speakers and 43 children who stutter. Blood samples were collected for the determination of thiamine and histamine using high-performance liquid chromatography coupled to ultraviolet detector (HPLC-UV). The results revealed a significantly lower level of thiamine in the stuttering group ( M = 29.9, SD = 15.6) compared with the control group ( M = 38, SD = 19.3, p = .02), and a significantly higher level of histamine in the stuttering group ( M = 20.5, SD = 6.5) compared with the control group ( M = 9.4, SD = 7.5, p ≤ .00). The results also indicated a significant negative correlation between severity scores and the level of histamine. The findings of the current study may provide further supporting information about the relationship between childhood-onset stuttering and some biological markers such as thiamine and histamine.
Phase I/II Clinical Study of PRaG Regimen Combined With Intraperitoneal Infusion of PD-1 Inhibitor for Advanced Refractory Solid Tumors With Cancerous Ascites (PRaG4.0P Study)
Authors: Xu Yingying , Kong Yuehong , Ma Yifu , Xu Meiling , Yang Jiabao , Zhang Junjun , Chen Rongzheng , Chen Guangqiang , Hong Zhihui , Zhao Xiangrong , Zhang Chenyang , Xing Pengfei , Zhang Liyuan , Zhao Peifeng
Published: July 2024

Objective: The prognosis of malignant tumors with peritoneal metastases and cancerous ascites has generally been poor, with limited treatment options. The PRaG regimen, which comprised of hypofractionated radiotherapy, programmed cell death-1 (PD-1) inhibitor, and granulocyte-macrophage colony-stimulating factor (GM-CSF), showed a survival advantage in patients with advanced solid tumors who failed at least the first line of standard systemic treatment. Intraperitoneal infusion of PD-1 inhibitors may be a novel therapeutic strategy for managing malignant ascites. Integrating the PRaG regimen with intraperitoneal perfusion of a PD-1 inhibitor might control malignant ascites and provide further survival benefits in these patients. This proposed study aims to investigate the safety and efficacy of intraperitoneal infusion of serplulimab in combination with the PRaG regimen in patients with simultaneous advanced solid tumors and cancerous ascites who fail at least the first-line treatment. Methods: This proposed study is a prospective, single-arm, open-label, multicenter clinical trial. All eligible patients will receive 2 cycles of intensive treatment, a combination of PRaG regimen with an intraperitoneal infusion of PD-1 inhibitor. The patients who are beneficially treated with intensive treatment will receive consolidation treatment every 2 weeks until ascites disappear, disease progression occurs, intolerable toxicity occurs, or for up to 1 year. Phase I of this study will be conducted using a modified 3 + 3 design. The dose of intraperitoneal infusion of PD-1 inhibitor for phase II will be determined according to dose-limiting toxicity evaluation in the phase I study. Conclusion: This prospective, open-label, multicenter study will potentially lead to intraperitoneal perfusion of a PD-1 inhibitor being a new strategy for malignant ascites patients and provide a meaningful efficacy and safety of the combination of PRaG regimen with an intraperitoneal infusion of PD-1 inhibitor for these patients.
VT204: A Potential Small Molecule Inhibitor Targeting KRASG12C Mutation for Therapeutic Intervention in Non-Small Cell Lung Cancer
Authors: Yang Xuechao , Zhang Shu , Yang Yang , Duan Xiaoqun , Li Xiaochuan
Published: July 2024

Objectives: The development of effective treatments for non-small cell lung cancer (NSCLC), particularly targeting the KRASG12C mutation, remains a challenge. In this study, we investigated the therapeutic potential of VT204, a small molecule inhibitor of KRASG12C, in NSCLC. Methods: To achieve the objectives, we conducted a comprehensive set of experimental methods. In vitro experiments involved the investigation of VT204 on proliferation, apoptosis, cell cycle dynamics, migration, invasion, and on the RAF/MEK/ERK signaling pathway in NCI-H358 cells. In addition, in vivo experiments were performed to evaluate the inﬂuence of VT204 on tumor growth. Results: We demonstrated that VT204 effectively suppressed cell proliferation in NCI-H358 cells, with significant inhibition observed at a concentration of 8 μM. Colony formation assays further supported the inhibitory effect of VT204 on NCI-H358 cell growth. Moreover, VT204 exhibited notable effects on suppressing migration and invasion capacities of NCI-H358 cells, indicating its potential as a metastasis-inhibiting agent. Mechanistic investigations revealed that VT204 induced apoptosis and G2M-phase cell cycle arrest in NCI-H358 cells. Additionally, VT204 modulated the RAF/MEK/ERK signaling pathway, leading to reduced phosphorylation of ERK. In vivo studies using xenograft models confirmed the inhibitory effect of VT204 on NCI-H358 tumor growth. Conclusion: These findings highlight VT204 as a promising therapeutic candidate for NSCLC targeting the KRASG12C mutation.
Large Foundation Model for Cancer Segmentation
Authors: Ren Zeyu , Zhang Yudong , Wang Shuihua
Published: July 2024

Recently, large language models such as ChatGPT have made huge strides in understanding and generating human-like text and have demonstrated considerable success in natural language processing. These foundation models also perform well in computer vision. However, there is a growing need to use these technologies for specific medical tasks, especially for identifying cancer in images. This paper looks at how these foundation models, such as the segment anything model, could be used for cancer segmentation, discussing the potential benefits and challenges of applying large foundation models to help with cancer diagnoses.
Causal Performance of Scholarship Students: A Case Study of a Peruvian University
Authors: Cristobal Velásquez Doris Hilda , Dávila Rivera Jhanely Marlit , Maggi Poisetti Guido , Morey Lezama Orangel José , Susanibar Arevalo Jhian Piers , Virgilio Gianluca P. M.
Published: July 2024

Higher education policies are paramount for putting limited resources at work for the best result. In Peru, PRONABEC© is the institution in charge of assigning scholarships to promising students with the aim of promoting social inclusivity and development of the country. This research uses Difference-in-Differences, Regression Discontinuity, and other statistical tools to analyze the causal relationship between the Peruvian scholarship system and the results it achieves. Based on its findings, the study makes recommendations.
Impact of demographic and clinical factors on in-hospital delays in acute ischemic stroke treatment
Authors: Naftali Jonathan , Tsur Gal , Auriel Eitan , Raphaeli Guy , Findler Michael , Brauner Ran , Perlow Alain , Keret Ophir , Barnea Rani
Published: July 2024

Background

Delays in reperfusion treatment, both intravenous thrombolysis (IVT) and endovascular treatment (EVT), adversely affect outcomes in patients with acute ischemic stroke (AIS). To alleviate these delays, it is essential to comprehend how patients’ baseline and stroke characteristics impact in-hospital reperfusion delays. While demographic and socioeconomic factors affect stroke outcomes, their impact on in-hospital delays remains unclear.

Method

This is retrospective analysis at a tertiary stroke center, encompassing AIS patients receiving IVT and / or EVT between 2019 and 2022 (re-canalization cohort). Outcomes of interest were time intervals of admission to CT and admission to recanalization. Univariable analyses explored age, gender, baseline functional status, socioeconomic status (SES), ethnicity, vascular risk factors, and stroke characteristics. Subsequently, multivariable logistic regression analyses were performed.

Results

Altogether, 313 patients treated with IVT and 293 with EVT were included in the re-canalization cohort. No demographic variables were found to be associated with stroke treatment time intervals. Following multivariable analysis, stroke severity (low NIHSS, p < 0.01), arrival to the hospital by other means than ambulance ( p < 0.01), and atypical stroke symptoms ( p < 0.01), were associated with in-hospital delays, both in the EVT and the IVT groups.

Conclusion

Our findings indicate that patients with a more severe ischemic stroke, typical stroke symptoms, and arrival by ambulance have shorter stroke treatment time intervals. These results emphasize that, in atypical cases, even a lower suspicion of stroke should promote urgent workup for stroke diagnosis. Our findings do not indicate any influence of demographic or SES on in-hospital reperfusion delays.
Validation of a model for outcome prediction after endovascular treatment for ischemic stroke
Authors: Martins Pedro , Sambhu Krishna , Tarek Mohamed , Dolia Jaydevsinh , Pabaney Aqueel , Grossberg Jonathan , Nogueira Raul , Haussen Diogo
Published: July 2024

Introduction

The recently developed MR-PREDICTS@24 h model showed excellent performance in the MR-CLEAN Registry cohort in patients presenting within 12 h from onset. However, its applicability to an U.S. population and to patients presenting beyond 12 h from last known normal are still undetermined. We aim to externally validate the MR-PREDICTS@24 h model in a new geographic setting and in the late window.

Methods

In this retrospective analysis of a prospectively collected database from a comprehensive stroke center in the United States, we included patients with intracranial carotid artery or middle cerebral artery M1 or M2 segment occlusions who underwent endovascular therapy and applied the MR-PREDICTS@24 h formula to estimate the probabilities of functional outcome at day 90. The primary endpoint was the modified Rankin Scale (mRS) at 90 days.

Results

We included 1246 patients, 879 in the early (<12 h) and 367 in the late (≥12 h) cohort. For both cohorts, calibration and discrimination of the model were accurate throughout mRS levels, with absolute differences between estimated and predicted proportions ranging from 1% to 5%. Calibration metrics and curve inspections showed good performance for estimating the probabilities of mRS ≤ 1 to mRS ≤ 5 for the early cohort. For the late cohort, predictions were reliable for the probabilities of mRS ≤ 1 to mRS ≤ 4.

Conclusion

The MR-PREDICTS@24 h was transferrable to a real-world U.S.-based cohort in the early window and showed consistently accurate predictions for patients presenting in the late window without need for updating.