Comprehensive Process Research Articles

Theoretical study on wheel wear mechanism of high-speed train under different braking modes

The high-speed train wheel wear problem persists throughout the whole service cycle, including the traction-braking stage, constant-speed stage, acceleration and deceleration stage, and so on. This article focuses on the wheel wear of high-speed trains under various braking modes. Firstly, high-speed motor cars and trailers are created, and the two are linked by the coupler buffer device, and the former considers detailed gear transmission system. A comprehensive wheel wear prediction process is then established based on the vehicle model, wheel-rail contact model, wear model, and wheel tread update strategy. Secondly, vehicle model and wheel wear prediction procedure are validated using the measured dynamic data and worn wheel profile. Thirdly, common braking modes and emergency braking modes are used for the braking process of high-speed trains. Finally, wheel wear and wear mechanism of high-speed trains during braking process is revealed. The results demonstrate that with the increase of braking mode level, the amplitude of longitudinal creepage also increases synchronously, while lateral creepage and spin creepage do not change significantly. According to wheel wear depth under different braking processes, the order from large to small is emergency braking mode >8-level common braking mode >6-level common braking mode >4-level common braking mode >3-level common braking mode >B1-level common braking mode. Furthermore, with the increase of rail slope, wheel wear is increasing during braking process. With the increase of friction coefficient, wheel wear depth decreases gradually during braking process.

ENHANCING ENGLISH LANGUAGE PROFICIENCY IN GRADE 1 JUNIOR HIGH SCHOOL: THE IMPACT OF YOUTUBE VIDEOS AS A LEARNING TOOL

The purpose of this large-scale study is to comprehensively investigate the effectiveness of adding YouTube videos as a pedagogical tool to improve the English language skills of first-year high school students. The purpose of the research is to provide a nuanced understanding of the benefits of using YouTube as a learning tool and to determine whether the videos that students watch significantly improve their English vocabulary comprehension, retention and cognitive processing. This study uses strong descriptive qualitative research and carefully focuses on first-year high school students. The primary method of data collection involves conducting in-depth interviews that provide a deep and comprehensible study of students experiences and insights into the transformative impact of YouTube videos on their journey to English language proficiency. The research findings undoubtedly emphasize the effective power of YouTube videos as an important educational tool. Students who regularly use YouTube to improve their English skills show not only remarkable but also transformational differences from those who occasionally watch such videos. Basically, students who actively participate in the consumption of YouTube videos show a significant expansion of their vocabulary, which proves the positive and tangible impact of this multimedia platform on language learning. The study carefully examines the benefits of seamlessly integrating YouTube into the language learning curriculum. Beyond the limitations of traditional pedagogical methods, YouTube and its inherently engaging audiovisual nature appeals to students and attention and at the same time different learning styles are taken into account, which promotes a dynamic and engaging language learning experience. Through carefully crafted open-ended questions, participants share their nuanced observations, preferences, and challenges they encounter using YouTube videos for language learning. Characterized by its richness and complexity, this qualitative data collection strategy provides a comprehensive understanding of the complex dynamics between students and engagement with YouTube. The research findings highlight the central role of YouTube in improving high school students' English language skills. Active integration of YouTube with students and study routines not only improve comprehension but also contribute to better retention of vocabulary. The sensory stimuli characteristic of YouTube videos stimulate cognitive processes, making it easier to enter and remember English words. In addition, the research findings emphasize the critical importance of developing a technologically enriched learning environment. Integrating YouTube into educational software not only acts as a catalyst for language acquisition, but also promotes digital literacy and adaptability, skills that are critical in today's educational environment. In conclusion, this comprehensive study provides invaluable insights into the effectiveness of YouTube videos as a catalyst for improving English language skills among first-year elementary school students. The findings highlight the paradigm-shifting impact of seamlessly integrating multimedia content into the language curriculum, prompting further research and providing important pedagogical considerations. As technology advances, harnessing the pedagogical potential of platforms like YouTube becomes a key strategy to create dynamic, engaging and highly effective language learning experiences for students.

Addressing employee misfit—A crucial HR concern for organizational success

In the rapidly evolving landscape of contemporary business, the strategic alignment of employees with their designated roles is a pivotal determinant of organizational success. Employee misfit, characterized by a misalignment between employees’ skills, interests, and assigned roles, poses formidable challenges to individual and collective performance. This comprehensive research report delves into the intricate implications of employee misfit, explores evolving trends in career consciousness among job seekers, outlines the multifaceted challenges HR managers face, and fervently advocates for implementing a comprehensive selection process to address this prevalent issue effectively. The report underscores the proactive role of management in cultivating a supportive work environment, fostering diverse career pathways, and embedding an inclusive selection framework to confront and mitigate the persistent issue of employee misfit.

Exploring plant-derived small molecules as inhibitors of Marburg virus RNA binding protein activity

The search for antiviral medications is greatly influenced by the hunt for potent inhibitors of viral proteins. To find possible inhibitors of the RNA binding activity of the Marburg virus VP35 protein, we used a thorough in silico drug discovery approach in this investigation. A comprehensive virtual screening process, followed by a detailed MMGBSA analysis, led to the discovery of four potential inhibitory compounds viz. Kudzuisoflavone A, Miquelianin, Rutin, and Protopseudohypericin. They were identified from an extensive library of phytomolecules derived from three medicinal plants: Adiantum capillus-veneris, Hypericum perforatum, and Pueraria montana. In molecular dynamics (MD) simulations, all these compounds showed steady binding to the target protein and favourable interactions. Notably, the free binding energies of all the selected compounds were better than the myricetin, a well-known blocker of the VP35 protein of the Ebola virus. Overall, this investigation offers insightful information about the molecular interactions and binding dynamics of the identified inhibitors’ binding to the VP35 protein of the Marburg virus. The findings highlight the potential of three particular medicinal plants as sources of key chemicals for the creation of brand-new Marburg virus antiviral drugs. More experimental validation and optimization of the identified inhibitors are necessary in order to transform these findings into effective medicines for treating Marburg virus infections. Communicated by Ramaswamy H. Sarma

Techno-economic analysis of production of octane booster components derived from lignin

AbstractIn this study, a comprehensive process for production of an environmentally friendly octane booster (acetophenone) from lignin is presented, along with a detailed techno-economic analysis. Recognizing that much of the prior research on octane boosters has been confined to experimental lab-level investigations, this study develops comprehensive process design to unravel the intricacies of large-scale acetophenone production. The acetophenone production process involves catalytic hydrogenolysis, which also yields phenol as a valuable side product. Based on the process flow diagram, mass and energy balances were developed, revealing significantly improved yields and purity of acetophenone compared to industry standards, reaching 0.74 kg acetophenone per kg of lignin and 99 wt%. In the techno-economic analysis, calculations involving fixed capital investment (FCI), operating costs, and working capital were conducted based on a feed of 100 kg/h of dry lignin. The results indicate FCI at 2.72 million USD, operating costs at 1.09 million USD per year, and working capital at 0.57 million USD. Assuming a 20-year operational lifespan, the payback period is estimated at 6.09 years, as depicted by the cumulative cash flow diagram. Moreover, techno-economic analysis demonstrates a net present value (NPV) of 3.24 million USD at a 10% discount rate, an internal rate of return (IRR) of 22.73%, and a return on investment (ROI) of 34.39%. These positive outcomes underscore the robust profitability of the proposed acetophenone production plant derived from lignin. Additionally, a sensitivity analysis on the IRR indicates that increasing the production capacity could further enhance profitability, reaffirming the feasibility of the plant’s operation. Crucially, this study highlights the potential for sustainable and economically viable production of acetophenone, offering an environmentally friendly alternative to toxic octane boosters and advancing the development of sustainable fuel additives. Graphical Abstract

Multiple-carrier-lifetime model for carrier dynamics in InGaN/GaN LEDs with a non-uniform carrier distribution

We introduce a multiple-carrier-lifetime model (MCLM) for light-emitting diodes (LEDs) with non-uniform carrier distribution, such as in multiple-quantum-well (MQW) structures. By employing the MCLM, we successfully explain the modulation response of V-pit engineered MQW LEDs, which exhibit an S21 roll-off slower than −20 dB/decade. Using the proposed model and employing a gradient descent method, we extract effective recombination and escape lifetimes by averaging the carrier behavior across the quantum wells. Our results reveal slower effective carrier recombination and escape in MQW LEDs compared with LEDs emitting from a single QW, indicating the advantages of lower carrier density achieved through V-pit engineering. Notably, the effective carrier recombination time is more than one order of magnitude lower than the effective escape lifetime, suggesting that most carriers in the quantum wells recombine, while the escape process remains weak. To ensure the reliability and robustness of the MCLM, we subject it to a comprehensive threefold validation process. This work confirms the positive impact of spreading carriers into several quantum wells through V-pit engineering. In addition, the MCLM is applicable to other LEDs with a non-uniform carrier distribution, such as micro-LEDs with significant surface recombination and non-uniform lateral carrier profiles.

Clinical Needs Assessment of a Machine Learning-Based Asthma Management Tool: User-Centered Design Approach.

Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. We aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians' experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use. At the "discovery" phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the "define" phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical "how might we" research questions to guide model development and implementation. We identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.

Management of Velopharyngeal Dysfunction (VPD) Following Cleft Palate Repair: A Comprehensive Decision-Making Process Based on Severity and Structural Deficiencies.

To describe a comprehensive decision-making process for surgical correction of structural Velopharyngeal Dysfunction (VPD) following cleft palate repair and evaluate its efficacy. Retrospective study. Tertiary care hospital. 300 consecutive patients with unilateral or bilateral cleft lip and palate (CLP) or isolated cleft palate (CP) diagnosed with clinical VPD following cleft palate repair between 2009 and 2014. Of these 206 patients had structural VPD and underwent surgical correction. Surgical corrections were carried out according to the comprehensive two stage decision making process developed by the investigators. Step 1 of decision-making involved visualisation of the VP sphincter function by nasoendoscopy. This was followed by step 2 which involved per-operative identification of scarring, tissue loss, hypoplasia and other structural deficiencies in the soft palate and septal mucoperiosteum. The choice of operation was then made from a repertoire of interrelated and escalating surgical procedures consisting of palate revision and pharyngoplasties ranging from most anatomical to the least. Evidence of postoperative restoration of VP function on nasoendoscopy, evaluation of speech for hypernasality, understandability, acceptability and symptoms of obstructive sleep apnea. Complete VP closure was demonstrated in 94% of patients treated using this algorithm. There was significant improvement in all speech parameters (p < 0.00001). Our comprehensive decision-making process is designed to effectively correct structural VPD according to the severity of structural and functional deficiencies in the soft palate and avoid over treatment.

Critical factors influencing green consumer behavior: A case study in Vietnam

Green shopping is becoming increasingly popular as a way of saving the environment and combating climate change in Vietnam and many other nations across the world. In addition, the agriculture sector, along with other industries such as manufacturing, services, commerce, and environmentally conscious consumption, is being examined in the global shift towards sustainable green consumption. This is in line with attempts to adopt eco-friendly solutions and mitigate climate change. As a result, the study investigated the critical elements influencing individual consumers' green consumer behavior in five Vietnamese provinces and gave management recommendations for improving green consumer behavior. The article technique utilized structural equation modeling (SEM), the statistical package for the social sciences (SPSS), and Amos. In addition, data was collected from 700 green product buyers. The findings of the article identified five essential factors influencing green consumer behavior: green products, the price of green products, green places, green promotion, and green policy. The article provides valuable insights for managers to develop an effective green marketing plan for businesses that sell organic food products. The goal is to encourage customers to embrace environmental consciousness and promote sustainable development. Additionally, the article emphasizes the importance of conveying positive information about green businesses. The value of this study raises awareness of the fact that green consumption is a comprehensive and responsible management process that satisfies, identifies, meets, and predicts the needs of producers, sellers, and consumers while preserving the natural health of the environment and not endangering human health.

Optimizing workflow of urgent stroke endovascular intervention: A focused lean six sigma project

IntroductionUrgent endovascular intervention is currently accepted as the primary and critical therapeutic approach to patients whose acute ischemic stroke results from a large arterial occlusion (LAO). In this context, one of the quality metrics most widely applied to the assessment of emergency systems performance is the “door-to-puncture” (D-P) time. We undertook a project to identify the subinterval of the D-P metric causing the most impact on workflow delays and created a narrowly focused project on improving such subinterval. MethodsUsing the DMAIC (i.e., define, measure, analyze, improve and control) approach, we retrospectively reviewed our quality stroke data for calendar year (CY) 2021 (i.e., baseline population), completed a statistical process control assessment, defined the various subintervals of the D-P interval, and completed a Pareto analysis of their duration and their proportional contribution to the D-P interval. We retooled our workflow based on these analyses and analyzed the data resulting from its implementation between May and December 2022 (i.e., outcome population). ResultsThe baseline population included 87 patients (44 men; mean age = 67.2 years). Their D-P process was uncontrolled, and times varied between 35-235 minutes (Mean = 97; SD = 38.40). Their door to angiography arrival (D-AA) subinterval was significantly slower than their arrival to puncture (AA-P) (73.4 v. 23.5 minutes; p < 0.01), accounted for 73% of the average length of the D-P interval. The group page activation to angiography arrival (GP-AA) subinterval accounted for 41.5% of the entire D-AA duration, making it the target of our project. The outcome population originally consisted of 38 patients (15 men; mean age = 70.3 years). Their D-P process was controlled, its times varying between 43-177 minutes (Mean = 85.8; SD = 34.46), but not significantly difference than the baseline population (p = 0.127). Their target subinterval GP-AA varied between 0-37 minutes and was significantly improved from the baseline population (Mean = 13.21 v. 29.68; p < 0.001). ConclusionsIt seems feasible and reasonable to analyze the subinterval components of complex quality metrics such as the D-P time and carry out more focused quality improvement projects. Care must be exercised when interpreting the impact on overall system performance, due to unexpected variations within interdependent subprocesses. The application of a robust and comprehensive LSS continuous quality improvement process in any CSC will have to include individualized focused projects that simultaneously control the different components of overall system performance.

Efficient and comprehensive recycling of valuable components from scrapped Si-based photovoltaic panels

The increasing scrapped Si-based photovoltaic (PV) panels has become an urgent problem, and their disposal is essential for resources utilization and environment issues. This paper proposes a comprehensive process for recycling of discarded silicon-based PV panels economically, environmentally, and efficiently. Based on the thermal properties of ethylene vinyl acetate (EVA), they are removed from the discarded PV panels at 600 °C with heating rate of 5 °C/min and maintain for one hour. The glass, solar cells, and copper strips were separated after heat treatment. Simultaneously, the solar cells were crushed into powder. Nitric acid was used to recover silver from the solar cell powder, while most of the metal impurities such as Mg, Ti and Al, were removed as well. The leaching efficiency of silver was over 96 % under the optimized conditions: HNO3 of 4.0 mol/L, liquid-to-solid ratio of 10:1, temperature of 50 °C for 80 min. Regarding the copper strips, they were sequentially treated with 0.5 mol/L CH3COOH and NaOH solution to remove the oxides of Pb and Sn on their surface. Subsequently, they were placed into the solution of 1.0 mol/L CuSO4 with pH of 2 ∼ 3 to eliminate Pb and Sn. This article provides significant reference for the recycling of Si-based PV panels.

Investigation of the enrichment-purification process and electrochemical performance of kish graphite in dust from blast furnace tapping yard

Kish graphite is a typical byproduct of steel production, and its enrichment and purification are essential prerequisites for its high value and comprehensive utilization. To solve the problem of recovery and application of difficult-to-treat kish graphite with a small particle size obtained from metallurgical dust, kish graphite in blast furnace tapping yard dust was effectively enriched and purified by a comprehensive flotation-acid leaching treatment process in this study. The influence of the flotation agents on the flotation process was explored. The results showed that the optimized flotation agent dosage was 500.0 g·t−1 (collector) and 120.0 g·t−1 (frother), respectively. Based on the optimized flotation scheme, a graphite concentrate (FG) with 79.12 % carbon content and 93.5 % carbon recovery was obtained. After the leaching treatment with a HCl-HF mixed acid solution, the carbon content of the graphite concentrate increased to 95.55 %. The ID/IG value of the graphite concentrate was 0.145, and the average lattice spacing was approximately 0.3354 nm. The SEM results showed that the leaching-treated graphite concentrate (AFG) had a loose, fragment-like structure. When used as an anode material for lithium-ion batteries, The AFG still provided a high reversible capacity of ∼370 mAh·g−1 and excellent coulombic efficiency of 99.6 % after 350 cycles. In addition, an industrial-grade recycling and utilization path for kish graphite based on a circular supply chain strategy was proposed. The results of this study may serve as a conceptual basis for the recovery and application of kish graphite from metallurgical dust.

Machine learning-aided generative design methodology for a Martian regolith habitation shell

This study presents a novel design methodology to automate the development process of a regolith habitation shell for a crewed mission on Mars. The integration of generative design, simulation tools, and data science methodologies allows for a systematic and comprehensive iterative design process, which facilitates the development of dual-habitation shells that can resist harsh conditions on Mars.The study establishes a methodology consisting of four fundamental sections. Initially, the environmental conditions on the surface of Mars are assessed and quantified as structural loads, so that the generative design tools effectively transform these stresses into design parameters that might be used in finite element modeling (FEM). Next, this study employs human factors derived from existing literature as input for Social Network Analysis (SNA) and the Label Propagation Algorithm (LPA) to address important design inquiries concerning functional zoning, layout configuration, geometry generation, and habitability variables. These inquiries aim to inform the decision-making process regarding autonomous typology design. Furthermore, the use of space syntax, physics simulations, and Voronoi algorithms improves the accuracy of the habitation planning process by generating floor designs and habitable volumes that align with the distinctive requirements and preferences of individuals. Lastly, the regolith habitation shell is constructed using structural simulation techniques that incorporate Mars' environmental pressures as design considerations. By employing Karamba3D and numerical finite element modeling (FEM) techniques, the structural performance of the regolith habitation shell under the environmental conditions on Mars is simulated.Consequently, this study introduces three distinct habitation shell designs that were developed and exposed to structural simulations to evaluate the effects of environmental stresses on Mars. The study explores the conjunction between generative design, data science, and structural simulations by employing a systematic and comprehensive design process. Its objective is to provide insights into the impact of environmental pressures on habitation shells and improve our understanding of design outcomes for future exploration on Mars.

Conv3D-Based Video Violence Detection Network Using Optical Flow and RGB Data.

Detecting violent behavior in videos to ensure public safety and security poses a significant challenge. Precisely identifying and categorizing instances of violence in real-life closed-circuit television, which vary across specifications and locations, requires comprehensive understanding and processing of the sequential information embedded in these videos. This study aims to introduce a model that adeptly grasps the spatiotemporal context of videos within diverse settings and specifications of violent scenarios. We propose a method to accurately capture spatiotemporal features linked to violent behaviors using optical flow and RGB data. The approach leverages a Conv3D-based ResNet-3D model as the foundational network, capable of handling high-dimensional video data. The efficiency and accuracy of violence detection are enhanced by integrating an attention mechanism, which assigns greater weight to the most crucial frames within the RGB and optical-flow sequences during instances of violence. Our model was evaluated on the UBI-Fight, Hockey, Crowd, and Movie-Fights datasets; the proposed method outperformed existing state-of-the-art techniques, achieving area under the curve scores of 95.4, 98.1, 94.5, and 100.0 on the respective datasets. Moreover, this research not only has the potential to be applied in real-time surveillance systems but also promises to contribute to a broader spectrum of research in video analysis and understanding.

Advancements in Precision Trajectory Generation: A Synergistic Approach to Design and Implementation for the 5-DOF Alpha-II Robot's Digital and Physical Twins

This research paper explores the comprehensive design and verification process of a 5-DOF articulated robotic arm, named the Alpha-II. Leveraging SolidWorks and CoppeliaSim, the digital twin is meticulously developed, with kinematics and working envelope computations verified on CoppeliaSim and MATLAB. The transition to CoppeliaSim for testing involvesthe implementation of diverse control strategies, such as position control, velocity control, and torque control, to rigorously evaluate the manipulator's movements. Highlighting the synergy between SolidWorks and CoppeliaSim, this literature emphasizes the versatility of SolidWorks in digital twin design and the effectiveness of CoppeliaSim's virtual environment. The research culminatesin the creation of a physical twin, aligning with the meticulously crafted digital counterpart. This study not only advances the understanding of trajectory generation for 5-DOF robotic arms but also underscores the pivotal role of cohesive digital and physical twin methodologies in cutting-edge robotics research. The rigorous verification, testing, and synthesis process contribute to the development of a refined and thoroughly validated end product, showcasing the significance of integrating simulation toolsin the design and analysis of robotic systems.

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving.

Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models' tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

A Critical Review and Systematic Design Approach for Linkage-Based Gait Rehabilitation Devices

This study aims to provide a comprehensive critical review of the existing body of evidence pertaining to gait rehabilitation. It also seeks to introduce a systematic approach for the development of innovative design solutions in this domain. The field of gait rehabilitation has witnessed a surge in the development of novel robotic devices. This trend has emerged in response to limitations observed in most commercial solutions, particularly regarding their high costs. Consequently, there is a growing need to explore more cost-effective alternatives and create opportunities for greater accessibility. Within the realm of cost-effective options, linkage-based gait trainers have emerged as viable alternatives, prompting a thorough examination of this category, which is carried out in this work. Notably, there is a wide heterogeneity in research approaches and presentation methods. This divergence has prompted discourse regarding the standardization of key elements relevant to the proposals of new linkage-based devices. As a result, this study proposes a comprehensive and standardized design process and offers a brief illustration of the application of this design process through the presentation of a potential new design.

A novel creep contact model for rock and its implement in discrete element simulation

The creep behavior of rocks significantly impacts projects' structural safety, including mining, tunneling, and hydraulic engineering. Numerous creep models used in numerical calculations incorporate basic elements, such as Burger's model. However, relying solely on series or parallel combinations of these basic components fails to account for the accelerated creep stage, prompting efforts toward developing enhanced creep models. Despite noted progress, a limited number of studies on the full-process simulation of graded creep loading and the consequent generation of cracks. To overcome this problem, an innovative creep model is proposed based on both the Parallel Bond and the Kelvin-Voigt contact models. This proposed creep model simulates the graded loading creep test of rocks under different load conditions and also conducts a parameter sensitivity analysis for the impact of the proportionate discrepancy between the two contact models. The results indicate that this new creep model could successfully simulate the comprehensive creep process, effectively capture crack progression, and represent ultimate failure modes during the creep process. Notably, to ensure the precision of simulations, it is recommended that the proportion of the Kelvin-Voigt contact model within the total contact is at most 25%.

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Millions of women globally are impacted by the major health problem of breast cancer (BC). Early detection of BC is critical for successful treatment and improved survival rates. In this study, we provide a progressive approach for BC detection using multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral (HS) imaging. The proposed findings are based on the measurement of the interference pattern between the blue (446.6 nm) and red (632 nm) wavelengths. We consider implementing a comprehensive image processing and categorization method based on the use of Fast Fourier (FF) transform analysis pertaining to a change in the refractive index between tumor and normal tissue. We observed that cancer growth affects tissue organization dramatically, as seen by persistently increased refractive index variance in tumors compared normal areas. Both malignant and normal tissue had different depth data collected from it that was analyzed. To enhance the categorization of ex-vivo BC tissue, we developed and validated a training classifier algorithm specifically designed for categorizing HS cube data. Following the application of signal normalization with the FF transform algorithm, our methodology achieved a high level of performance with a specificity (Spec) of 94% and a sensitivity (Sen) of 90.9% for the 632 nm acquired image categorization, based on preliminary findings from breast specimens under investigation. Notably, we successfully leveraged unstained tissue samples to create 3D phase-resolved images that effectively highlight the distinctions in diffuse reflectance features between cancerous and healthy tissue. Preliminary data revealed that our imaging method might be able to assist specialists in safely excising malignant areas and assessing the tumor bed following resection automatically at different depths. This preliminary investigation might result in an effective "in-vivo" disease description utilizing optical technology using a typical RGB camera with wavelength-specific operation with our quantitative phase MWI imaging methodology.

The symptom perception processes of monitoring, awareness, and evaluation in patients with heart failure: a qualitative descriptive study.

Patients with heart failure (HF) experience various signs and symptoms and have difficulties in perceiving them. Integrating insights from patients who have engaged in the process of symptom perception is crucial for enhancing our understanding of the theoretical concept of symptom perception. This study aimed to describe how patients with HF perceive symptoms through the processes of monitoring, awareness, and evaluation and what influences the process. Using a qualitative descriptive design, we conducted in-person semi-structured interviews with a purposeful sample of 40 adults experiencing an unplanned hospitalization for a HF symptom exacerbation. We elicited how patients monitor, become aware of, and evaluate symptoms prior to hospitalization. Data were analysed using directed qualitative content analysis. One overarching theme and three major themes emerged. Patients demonstrated Body listening, which involved active and individualized symptom monitoring tactics to observe bodily changes outside one's usual range. Trajectory of bodily change involved the patterns or characteristics of bodily changes that became apparent to patients. Three subthemes-sudden and alarming change, gradual change, and fluctuating change emerged. Patients evaluated symptoms through an Exclusionary process, sequentially attributing symptoms to a cause through a cognitive process of excluding possible causes until the most plausible cause remained. Facilitators and barriers to symptom monitoring, awareness, and evaluation were identified. This study elaborates the comprehensive symptom perception process used by adults with HF. Tailored nursing interventions should be developed based on the factors identified in each phase of the process to improve symptom perception in HF.