Real Experiments Research Articles

Practice patterns and survival outcomes for muscle-invasive bladder cancer: real-life experience in a general population setting.

Bladder cancer (BC) is a common malignancy in Europe and North America. Among BCs, muscle-invasive BCs (MIBCs) are distinguished, as they require aggressive treatment due to their spreading potential and poor prognosis. Despite its clinical relevance, little information on MIBC in a general population setting is available. This study aims to report practice patterns and survival outcomes for MIBC patients in a general population setting. MIBCs among BC incidence in 2011 and 2012 recorded in a French population-based cancer registry (810 000 inhabitants) were included in the study. Data were extracted from the medical files. Individual, tumour-related characteristics and initial management including diagnostic tools, multidisciplinary team meeting (MDT) assessment, and treatment delivered were described. Cystectomy, chemoradiation, radiotherapy, and chemotherapy were considered as specific treatments. Matching between MDT decision and the treatment provided was detailed. Management practices were discussed according to the guideline's recommendations. Overall survival (using the Kaplan-Meier method) and net survival (using the Pohar-Perme estimator) were calculated. Among 538 incident BC cases, 147 (27.3%) were MIBCs. Diagnostic practices displayed a relevant locoregional assessment of BC. Almost all cases (n = 136, 92.5%) were assessed during an uro-oncological MDT with a median time from diagnosis of 18 days (first quartile:12-third quartile:32). Discrepancies appeared between MDT decisions and treatments delivered: 71 out of 86 subjects received the recommended cystectomy or chemoradiation (with or without neoadjuvant chemotherapy); 6 out of 11 had the recommended radio- or chemotherapy; and 9 patients did not undergo any specific treatment despite the MDT decision. Cystectomy was the most common treatment performed; the time to surgery appeared consistent with the guideline's recommendations. Forty people only received supportive care. Still, the 5-year overall and net survival was poor, with 19% (13-26) and 22% (14-31), respectively. The 5-year net survival was 35% (23-48) for people who underwent curative-intent treatments. MIBC management remains challenging even for cases assessed during an MDT. Many people did not undergo any specific treatment. Prognosis was poor even when curative-intent therapies were delivered. Efforts to reduce exposure to risk factors such as tobacco smoking and occupational exposures must be maintained.

Stochastic basins of attraction for uncertain initial conditions

Outcomes of nonlinear dynamical systems strongly depend on the initial conditions. However, it is common knowledge that it is not possible to fix the initial conditions in real experiments, with uncertainties being inherent to it. In this work, the problem of uncertain initial conditions in dynamical systems is investigated. These uncertainties are represented through four postulates, and a new definition of basin of attraction is deduced to quantify their effects on the global dynamics. It is shown that the new definition is a convolution between the uncertainty distribution and the deterministic basin of attraction, and a reduced-cost strategy to compute them is defined. A Helmholtz nonlinear oscillator is investigated by considering initial conditions with uniform and normal distributions, and a three-dimensional Hénon map is analyzed, too. Finally, the influence of the uncertainty level and the effectiveness of new basin definition are verified.

Gonadal and sexual function in men living with HIV: a real-life single centre experience

Gonadal and sexual function in men living with HIV: a real-life single centre experience

Bayesian uncertainty evaluation applied to the tilted-wave interferometer

The tilted-wave interferometer is a promising technique for the development of a reference measurement system for the highly accurate form measurement of aspheres and freeform surfaces. The technique combines interferometric measurements, acquired with a special setup, and sophisticated mathematical evaluation procedures. To determine the form of the surface under test, a computational model is required that closely mimics the measurement process of the physical measurement instruments. The parameters of the computational model, comprising the surface under test sought, are then tuned by solving an inverse problem. Due to this embedded structure of the real experiment and computational model and the overall complexity, a thorough uncertainty evaluation is challenging. In this work, a Bayesian approach is proposed to tackle the inverse problem, based on a statistical model derived from the computational model of the tilted-wave interferometer. Such a procedure naturally allows for uncertainty quantification to be made. We present an approximate inference scheme to efficiently sample quantities of the posterior using Monte Carlo sampling involving the statistical model. In particular, the methodology derived is applied to the tilted-wave interferometer to obtain an estimate and corresponding uncertainty of the pixel-by-pixel form of the surface under test for two typical surfaces taking into account a number of key influencing factors. A statistical analysis using experimental design is employed to identify main influencing factors and a subsequent analysis confirms the efficacy of the method derived.

Preface: 6th International Conference on Art, Design and Cultural Studies (ADCS 2024)

6th International Conference on Art, Design and Cultural Studies (ADCS 2024) was held during April 13-14, 2024 in Wuhan City, China, and this conference was a great success. The conference provides a useful and wide platform both for display the latest research and for exchange of research results and thoughts in Art, Design and Cultural Studies. The success and prosperity of the conference is reflected high level of the papers received. The aim of ADCS 2024 is to provide a platform for educators, sociologists, designers, writers, historians and graduate students from different cultural backgrounds to present and discuss research, developments and innovations in all fields of Art, Design and Cultural Studies. It provides opportunities for the delegates to exchange new ideas and application experiences, to establish business or research relations and to find global partners for future collaboration. We would like to acknowledge all of those who supported 6th ADCS. Each individual in addition, institutional help was very important for the success of this conference. Especially we would like to thank the organizing committee for their valuable advices in the organization and helpful peer review of the papers. It was a fully synchronized online conference that had live Q&A, participants jointly listened to presentations, and attended other live events associated with the conference. Each speech lasted for 30 minutes, including 5 minutes for Q&A. and for other sessions, there was 20 minutes for each presentation, including 5 minutes for Q&A. The interaction session was made the real experience to the participants and they had good exposure with the foreign university students and delegates. We would like to thank all the author submitted papers to the conferences and thank you all the reviewers for their time and effort in reviewing articles. Finally, it is a great honor to thank you all of behind staff who made their constant effort to make sure the conference process can go smoothly and the proceeding team who have dedicated their constant support and countless time to bring these scratches into this volume. Shanhong Zhu, Jiayu Liang Conference Committees

Development and assessment of a virtual escape-room game for teaching industrial bioprocesses.

This article presents a study on the implementation of a virtual escape-room game as a novel teaching methodology in biochemistry education. The game aimed to engage students in producing monoclonal antibodies against SARS-CoV-2 while reinforcing theoretical concepts and fostering teamwork. Three versions of the game were tested, incorporating modifications to address student feedback on and improve the overall experience. The study employed a satisfaction survey to gather insights from students regarding their perception of the game. Results showed that the implementation of answer flexibility using RegEx had a significant positive impact on student satisfaction and motivation. The introduction of RegEx allowed for a more realistic and immersive gaming experience, as students could provide varied answers while still being evaluated correctly. Overall, the findings highlight the effectiveness of the game's design, the suitability of the Google Forms platform for distance learning, and the importance of incorporating answer flexibility through RegEx. These results provide valuable guidance for educators seeking to enhance student engagement and satisfaction through the use of escape-room games in biochemistry education.

Mechanistic analysis of acid gas storage and oil recovery in naturally fractured reservoirs using single matrix block approach

AbstractThe objective of this study is to assess the storage of acid gas, containing CO2 and H2S, in a depleted naturally fractured reservoir (NFR) using single matrix block (SMB) approach. The acid gas dissolution in oil is considered by Peng‐Robinson equation of state and compositional simulation. The PHREEQC package is used to determine acid gas solubility in formation brine. Three types of acid gases with different compositions are used for this study and their swelling behavior and miscibility in relation to the reservoir oil are analyzed. An SMB model, with a matrix block surrounded by fractures, is constructed, and validated for simulation of a real experiment. The simulation is conducted for synthetic and real reservoir fluids when the oil is in its residual saturation. A sensitivity analysis is performed to study the effects of key parameters, such as acid gas composition, reservoir pressure, permeability, porosity and matrix height on the storage capacity and oil recovery factor. The matrix has a volume of 27 m3 and about half of acid gas storage is achieved in the first 5 years while the simulations are run for 30 years. The results show that up to 90% of remained oil is recoverable, and more than 0.67 kmol of acid gas per cubic meter of matrix is stored whether matrix contains a real oil or a synthetic one. Higher storage is achieved for higher matrix porosities and heights and large H2S proportion in acid gas. In all cases about 10% of acid gas is trapped in water and the remaining 90% is dissolved in oil. The mineral trapping was more active in CO2‐rich acid gases. While about 10 kg of the matrix rock was dissolved in the acidic brine when the acid gas contained H2S, the amount of the dissolved minerals in acidic brine resulted from the injection of CO2‐rich acid gas was more than 16 kg. Finally, this study gives a comparative analysis of the storage performance of acid gas mixture and pure CO2. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Long-Term Efficacy and Safety of Guselkumab in Psoriasis Patients Who Failed Anti-IL17: A Two-Year Real-Life Study.

Guselkumab is the first approved human IgG1λ monoclonal antibody selectively targeting the p19 subunit of interleukin (IL)-23. Despite its effectiveness and safety, which have been widely reported by clinical trials and real-life experiences, data regarding its use on patients who previously failed anti-IL17 are limited or characterized by a reduced follow-up period. These data are essential to guide clinicians in biologic switching, considering that anti-IL23 and anti-IL17 partially share their therapeutic targets, as well as some patients who may have to interrupt treatment with anti-IL17 for loss of efficacy over time or the development of adverse events (AEs). In this context, we performed a retrospective study with the aim of evaluating the long-term use (2 years) of guselkumab in psoriasis patients who previously failed at least one anti-IL17 in a real-life setting, also focusing attention on psoriasis located in difficult-to-treat areas (the scalp, palms or soles, fingernails, genitals). A total of 61 patients (35 male, 57.4%; mean age 57.6 ± 8.8 years) were enrolled. Of these, 30 (49.2%) patients failed secukinumab, 21 (34.4%) failed ixekizumab, 7 (11.5%) failed brodalumab, and 3 (4.9%) failed both secukinumab and ixekizumab. At the baseline, the mean PASI and BSA were 12.8 ± 8.4 and 24.5 ± 26.6, respectively. During week 16, PASI90 and PASI100 responses were achieved by 60.7% and 37.7% of patients, respectively, which continued to improve up to week 104 (PASI90: 73.8%, PASI100: 59.0%). Clinical improvement in difficult-to-treat areas was detected as well. In particular, a slower improvement for fingernails and the palmoplantar region was reported compared to scalp and genital psoriasis at week 16. However, no differences were found following 28 weeks of therapy. Primary and secondary inefficacy were reported by 1 (1.6%) and 5 (8.2%) patients. As regards safety, no severe AEs were collected.

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-conditional generative adversarial network (CGAN), which relies on a CGAN that combines one-dimensional convolutional neural network (CNN) with a bidirectional gate recurrent unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers early fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

Locally Self-Adjustive Smoothing for Measurement Noise Reduction with Application to Automated Peak Detection.

Smoothing is a widely used approach for measurement noise reduction in spectral analysis. However, it suffers from signal distortion caused by peak suppression. A locally self-adjustive smoothing method is developed that retains sharp peaks and less distorted signals. The proposed method uses only one parameter that determines the global smoothness of data, while balancing the local smoothness using the data itself. Simulation and real experiments in comparison with existing convolution-based smoothing methods indicate both qualitatively and quantitatively improved noise reduction performance in practical scenarios. We also discuss parameter selection and demonstrate an application for the automated smoothing and detection of a given number of peaks from noisy measurement data.

Unveiling Socioeconomic Disparities: Understanding Pregnancy Awareness and Family Well-being among Middle-Late Adolescent Mothers across Diverse Socioeconomic Strata in Urban Manila, Philippines

This research study examines the real-life experiences of adolescent mothers within the age group of 13-17 years old (middle adolescence) to 18-21 years old (late adolescence), coming from middle and low socioeconomic backgrounds. Using different qualitative research techniques, this study aims to uncover unique obstacles, coping strategies, and support networks encountered by teenage mothers from different economic strata. A total of 6 teenage mothers from middle-class (n=3) and low-income (n=3) settings is conducted to gather their stories and perspectives. Thematic analysis is employed to discern common threads and distinctions between the two groups, creating themes to categorize the similarities between their responses. The outcomes of this inquiry offer valuable insights into how socioeconomic status intersects with teenage parenthood, underscoring four significant factors that emerged through the data analysis: Support Systems, Educational and Career Trajectory, Family Dynamics and Expectations, and Awareness and Perception. Having identified these themes can design a path towards creating customized interventions and support mechanisms for both vulnerable demographics while advocating for more inclusive and educational programs in raising awareness and removing the stereotypes about teenage pregnancy. For future researchers interested in studying the real-life experiences of adolescent mothers from different economic backgrounds, it is essential to consider the myriad of factors that shape their lives. These factors include race, culture, and geographical location, all of which intersect with their socioeconomic status to influence their experiences.

Open educational resource case studies: responding to nursing student educational needs in the moment

BackgroundThe purpose of this paper is to share the pedagogical innovation of using open educational resource (OER) inclusive case studies in the classroom to respond to immediate student learning needs and augment the theory-to-practice gap that exists in undergraduate education. InnovationStudents were introduced to case studies as an OER as a weekly activity. The initial case studies were rooted in educators’ real-life experiences and nursing practice. As courses evolved, if there was an emerging trend in healthcare or a component of a concept, model, or theory that students were challenged with, subsequent case studies were edited to suit learning needs. ImplicationsWhen used intentionally, case studies can be responsive to student needs in the moment as OERs can facilitate a smoother transition of theory into practice. ConclusionThis collaborative initiative can inspire other educators and institutions to bridge the theory-to-practice gap, improving the quality of care for patients, using a similar approach.

Effectiveness and Safety of Tildrakizumab in Psoriasis Patients Who Failed Anti-IL17 Treatment: A 28-Week Real-Life Study.

Tildrakizumab is a humanised IgG1/k-type monoclonal antibody that targets the p19 protein subunit of IL23. Despite its effectiveness and safety have been widely reported by clinical trials and real-life experiences, data regarding its use on patients who previously failed anti-IL17 (brodalumab, ixekizumab, bimekizumab and/or secukinumab) are scant. Therefore, further studies on this topic would be beneficial for clinicians in guiding the selection of biologic shifting, considering that anti-IL23, -12/23, and -IL17 partially share their therapeutic targets. In this context, we performed a 28-week, single-center, real-life, retrospective study, with the aim of assessing the efficacy and safety of tildrakizumab in patients who previously failed anti-IL17, also focusing the attention on psoriasis located in difficult-to-treat areas (scalp, palms or soles, fingernails, genitals). A total of 23 patients (12 male, 52.2%; mean age 52.8 ± 12.4 years) were enrolled. Of these, 11 (47.8%) failed secukinumab, 7 (30.4%) ixekizumab, 3 (13.0%) brodalumab, 1 (4.3%) both secukinumab and ixekizumab and 1 (4.3%) bimekizumab. At baseline, mean PASI and BSA were 12.8 ± 5.9 and 18.7 ± 9.6, respectively. At W16 PASI75 and PASI90 response were achieved by 15 (65.2%), and 9 (39.1%) patients, respectively, whereas 19 (82.6%) and 13 (56.6%) subjects reached these scores at W28. One (4.3%) case of primary inefficacy and 1 (4.3%) case of secondary inefficacy were assessed. Finally, no severe adverse events were collected. Tildrakizumab seems to be a valuable option in selected patients with psoriasis unresponsive to anti-IL17, suggesting that prior exposure to biological therapies seem not directly affect its effectiveness.

Determination of impact parameter for CEE with digi-input neural networks

The impact parameter characterizes the centrality in nucleus-nucleus collision geometry. The determination of impact parameters in real experiments is usually based on the reconstructed particle attributes or the derived event-level observables. For the scheduled Cooler-storage-ring External-target Experiment (CEE), the low beam energy reduces correlation between the impact parameter and charged particle multiplicity, which decreases the validity of the explicit determination methods. This work investigates a few neural network-based models that directly take the digitized signals from the external Time-of-flight detectors as input. The model with the best performance shows a mean absolute error of 0.479 fm with simulated U-U collisions at 0.5 AGeV. The performances of the models implemented with digi inputs are compared with reference models with phase space inputs, showing the capability of neural networks to handle the original but potentially interrelated digitized signal information.

Gas source localization using Dueling Deep Q-Network with an olfactory quadruped robot

Gas source localization is one of the most common applications of a gas-sensitive mobile robot. However, most of the existing work focuses on rule-based algorithms for wheeled robots, which are difficult to apply in complex terrain with obstacles. In this article, we propose an olfactory quadruped robot to perform the gas source localization task using the Dueling Deep Q-Network (Dueling DQN) algorithm. For training, we designed a set of environments and imported gas dispersion data from computational fluid dynamics (CFD) software to construct a simulator. The olfactory quadruped robot was trained in this simulator using the Dueling DQN algorithm to learn how to find the gas source. The trained neural network was then deployed on the olfactory quadruped robot. Our method has been tested in both simulation and real environments. The olfactory quadruped robot can traverse rugged terrain in real experiments and efficiently find gas sources, demonstrating that our method is highly robust and has important practical value.

Real emotional experience of family members of patients transported within hospital in neurosurgical intensive care unit: A descriptive qualitative study.

To understand the real experience of family members of patients in neurosurgical intensive care unit (NICU) during intra-hospital transport (IHT), explore their inner needs and provide effective intervention measures for the construction of standardized IHT plan. A descriptive qualitative study. For the purposes of this study, 10 family members of IHT patients were included using a purposive sampling method. Semi-structured in-depth interviews were used to collect the data, Nvivo 11 software was used to organize the data, and Colaizzi's 7-step descriptive phenomenology method was used to analyse the data. A total of three themes and nine subthemes were extracted, namely: Experience of emotional changes at different stages (uncertainty before transfer, complex internal activity during transit, ambivalence after transfer); Perception of problems in IHT (poor doctor-patient communication, weak awareness of risk assessment, deficiencies in the transfer procedure); Consciousness of the real needs (emotional respect and closeness, stay informed of the progression of the disease, greater social support). Family members of patients in the NICU have complex internal experiences and multiple support needs during IHT, reflecting the need for further standardization of the transport process. In the future, we should improve the mode of safe IHT involving doctors, nurses and family members of patients, ensure the safety of patient transport, meet the social support needs of family members and improve the experience of IHT and the medical satisfaction of family members.

Distributed Optimal Solutions for Multiagent Pursuit-Evasion Games for Capture and Formation Control

In this paper, distributed optimal solutions are designed for networked multiagent pursuit-evasion (MPE) games for capture and formation control. In the games, the pursuers aim to minimize the distance from their target evaders while the evaders attempt to maximize it, and at the same time, all players desire to maintain cohesion with their teammates. The goals of agents are obviously reflected in the obtained optimal control strategies which consist of an attracting term and/or a repelling term. Nash equilibrium is obtained by means of optimal strategies using the solutions of the HJI equations. Furthermore, three scenarios are considered in the MPE game: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader, where sufficient conditions are given for pursuers in achieving exponential capture or formation control with ultimate zero or bounded errors. It is shown that the conditions depend on the structure of the communication graph, the parameters in the controllers, and the expected formation configurations. Finally, both simulations and real flight experiments successfully demonstrate the effectiveness of the proposed strategies.

Improving Needle Tip Tracking and Detection in Ultrasound-Based Navigation System Using Deep Learning-Enabled Approach.

Ultrasound-guided percutaneous interventions have numerous advantages over traditional techniques. Accurate needle placement in the target anatomy is crucial for successful intervention, and reliable visual information is essential to achieve this. However, previous studies have revealed several challenges, such as the variability in needle echogenicity and the common misalignment of the ultrasound beam and the needle. Advanced techniques have been developed to optimize needle visualization, including hardware-based and image-processing-based methods. This paper proposes a novel strategy of integrating ultrasound-based deep learning approaches into an optical navigation system to enhance needle visualization and improve tip positioning accuracy. Both the tracking and detection algorithms are optimized utilizing optical tracking information. The information is introduced into the tracking network to define the search patch update strategy and form a trajectory reference to correct tracking results. In the detection network, the original image is processed according to the needle insertion position and current position given by the optical localization system to locate a coarse region, and the depth-score criterion is adopted to optimize detection results. Extensive experiments demonstrate that our approach achieves promising tip tracking and detection performance with tip localization errors of 1.11 ± 0.59 mm and 1.17 ± 0.70 mm, respectively. Moreover, we establish a paired dataset consisting of ultrasound images and their corresponding spatial tip coordinates acquired from the optical tracking system and conduct real puncture experiments to verify the effectiveness of the proposed methods. Our approach significantly improves needle visualization and provides physicians with visual guidance for posture adjustment.

A Fault Diagnosis Method for Rectifier-Filter Circuit Integrating EEMD Algorithm and Transformer Network

Rectifier-filter circuit, as a critical component of the drive circuit in instrumentation and control systems of nuclear power plants, can convert the 50 Hz AC into the smooth DC. Thus, it plays a vital role in the power control of reactors. However, the weak waveform anomalies of soft faults in the rectifier-filter circuit make fault feature extraction difficult. Therefore, in this article, an ensemble empirical modal decomposition (EEMD) algorithm is employed to decompose the signal mode components in the monitor data of the rectifier-filter circuit. The weak waveform anomalies are indirectly enhanced by the IMF and residual components. Subsequently, the Transformer network is utilized to construct the feature extractor. With the advantage of multi-head attention (MHA) mechanism in the Transformer network, the multi-directional, multi-scale, and highly sensitive long-range time-dependent features in the EEMD feature data are extracted. Then, a deep Softmax classifier is adopted to reduce the dimensionality and diagnose the soft faults of the rectifier-filter circuit. Finally, a fault simulation model of the rectifier-filter circuit is constructed and the condition monitor data are collected. The effectiveness and diagnosis accuracy of the proposed method are verified by a real case experiment and some comparative methods.

University innovation and start-ups: Barriers and facilitators experienced by nursing faculties - A content analysis study.

University start-ups have gained significant popularity as a means of translating knowledge and research into practical applications. However, there is a lack of understanding regarding the experiences and perspectives of nursing faculties engaged in start-up ventures. The objective of this study was to explore valuable insights into the barriers and facilitators within this context for promoting success, generating entrepreneurial attitudes, as well as enhancing the sustainability of nursing faculty-led start-ups. We employed a content analysis methodology to capture the real-life experiences of nursing faculties involved in university start-ups. This approach is an effective way to gain a comprehensive understanding of the subject. Data were collected using semi-structured interviews. Graneheim and Lundman's step was used for analysis and to identify recurring patterns and themes within the collected data. Sampling was done purposefully, with convenience and snowball techniques until data saturation. The MAXQDA version 10 software was utilised to easily analyse, code and present data. The analysis revealed four major themes encompassed: motivational factors, personal attributes, limited entry support and barriers to scaling. These themes shed light on factors, support systems, challenges faced during growth as well as personal characteristics highlighted by the participants. Our study highlighted the factors influencing entrepreneurship in nursing faculties. Addressing the challenges of this study requires enhancing support systems and fostering personal development for entrepreneurial success in healthcare innovation. Policymakers can promote university education to expand the entrepreneurship process using more facilities and make more services accessible.