Special Case Research Articles

Does Fertility Culture Matter in Reproductive Decision Making? A Case Study of a Chinese Multi-child Family

This study delves into individual cases within multi-child families, building upon a literature review of sociocultural factors influencing reproductive decision-making. Although previous research has highlighted the significant influence of societal factors on reproductive choices, there is a knowledge gap regarding specific cases within multi-child families, necessitating a more in-depth investigation. The research question is framed as follows: "How do sociocultural factors interact in the reproductive decision-making of multi-child families, influencing family interactions and dynamics?" Using a case study approach, this paper employs a qualitative methodology by conducting semi-structured interviews to gather data. Grounded theory is applied for in-depth qualitative analysis. Findings reveal the pivotal role of sociocultural factors in internal family dynamics, supporting and extending previous research. Theoretical implications highlight a nuanced understanding of multi-child family dynamics, while practical implications underscore the need for tailored family support programs. This research contributes theoretical insights and practical guidance for addressing the unique needs of multi-child families in reproductive decision-making processes.

Study on the Determination of the Nature of Trademark Dilution--Taking the "Lao Gan Ma Case" as an Example

China's current legislation does not make clear provisions for the dilution of well-known trademarks, resulting in difficulties in identifying dilution cases in practice. One such notable instance is the "Lao Gan Ma" dispute, which serves as a telling example of this legal gray area. Consequently, it becomes imperative to delve into typical cases like the "Lao Gan Ma Case" to gain clarity on the disputed actions' legal nature, and then demonstrate how the dilution theory can be applied in the determination of specific cases. Utilizing the case study method, this paper aims to examine a particular case of trademark dilution in detail. The initial focus is on clarifying the concept of trademark dilution and its constituent elements of determination. This theoretical foundation is essential for accurately analyzing the nature of the complained-of infringing act. The significance of this paper is to theoretically solve the problem of determining the nature of dilution under the system of China's Trademark Law, to provide a reference for the determination of the same type of cases in the future, and hopefully to provide ideas for the future legislation on trademark dilution in China.

Fuzzy-Based Robot Behavior with the Application of Emotional Pattern Generator

The article deals with the development of human–robot interaction, where the robot measures the user’s emotional state as well as environmental factors by different devices, and it responds to the user with colorful patterns and controls the smart home through the guidance of the Internet of Things system. Before the design process, robots and their functions currently on the market, the role of emotions in communication, and technologies for measuring emotion (such as face recognition, measurement of heart rate, breath, and physical changes) are presented in detail. The designed robot (Em-Patty) uses a previously developed emotion-based automatic pattern generation system based on a fuzzy system, which is a suitable tool for handling emotions. A main and three fuzzy subsystems are fusioned in order to create the most efficient control system for Em-Patty. The nature-inspired design of the robot is also presented, as well as a description of its behavior with the help of a specific case study.

A digital twin model of the axial temperature field of a DC cable for millisecond calculations

The most important features of digital twin technology for transmission and transformation equipment are symbiotic evolution and state prediction. In constructing the digital twin model for DC cables, the transient and steady-state temperature fields need to be considered comprehensively in the modeling process to consider the multi-physical field coupling, mathematical model, operational data, and environmental conditions in specific cases, which makes it difficult to satisfy the real-time requirements of the digital twin. To solve this problem, in this paper, a digital twin technology architecture for DC cables is proposed, and a neural network learning model is constructed to minimize the latency due to the complexity of the model simulation. Moreover, an efficient digital twin model that is based on the model-driven approach is developed for the fast computation of the axial temperature field of DC cables. The use of the Sequence-to-Sequence residual neural network algorithm for the reduced-order multi-physics field coupling modeling reduces the computational time and ensures as much accuracy as possible up to the finite element model simulation. The simulation results show that the use of the digital twin model reduces the simulation time from 275 s to 0.56 s, and the relative error between the accuracy of the digital twin model and the finite element model for the axial temperature field of the DC cable is 0.46 %. Finally, verification of the reasonableness and feasibility of the proposed method is carried out via an experimental platform according to the standard of CIGRE TB496. In particular, the maximum value of the DC cable temperature is verified to have a small error compared with the result of the Sequence-to-Sequence calculation under specific working conditions.

Penerapan Etika Komunikasi Reception Pada Divisi Kantor Depan Dalam Pelayanan Tamu Check In Di Javana Royal Vila Seminyak Bali

The Increasing competition among accommodation supplies require it to keep optimizing its services in order to give the impression of loyalty to guests to stay. In more specific case, receptionists hold important rules to improve their services, especially in communication ethics. This study is conducted in Javana Royal Villa Seminyak Bali, under the title of Penerapan Etika Komunikasi Reception Pada Front Office Department Dalam Pelayanan Tamu Check in di Javana Royal Villa Seminyak. Bali. This research aims to determine the application of reception communication ethics and the influence of the application of reception communication ethics toward room occupancy rate at Javana Royal Villa Seminyak Bali. This study uses qualitative descriptive in which the translation of words or sentences are explained in the presentation. Moreover, the data collection techniques used are observation, interviews and documentation. The types of data used are primary data and secondary data. The study showed that by not concerning attention to communication ethics, it will greatly impact the comfort of guests in staying which results in bad reviews on the online travel agent so that in this case it certainly has a huge impact on the level of room reservations or on room occupancy in Javana Royal Villa Seminyak Bali.

Exploring feminist translation in Liu Cixin’s The Three-Body Trilogy

ABSTRACT According to the interventionist feminist translation theory, sexist ideas found in source texts should be questioned and corrected. In some materials, signs of sexism are embodied in some unnecessary and inappropriate emphases regarding women’s gender attributes, which are almost related to certain stereotypical images. The present paper aims to explore how these problematic emphases could be treated in a feminist translation praxis applying a contrastive study of the original texts and some solutions provided by translators. To this end, the current work is designed to focus on the study of six specific cases that have been collected from Liu Cixin’s The Three-Body Trilogy, a well-known Chinese science fiction novel that has sparked widespread controversy in terms of sexism. In each case, after studying the questionable original Chinese fragment, I will analyze its translations into English and Spanish to assess whether the sexism found in the source text can be attenuated or avoided after the translation process. This study will shed some light on how translators could deal with sexist emphases from a feminist approach.

Analysis of pipeline leakage in unsaturated stratum: A new seepage-diffusion model

Accurate prediction of the spread of fluids leaking from underground pipelines is crucial for risk assessment. In this study, we developed a method for predicting the diffusion range of pipeline seepage fluids within an unsaturated stratum. First, we derived a seepage–diffusion equation based on the generalized Darcy’s law and subsequently analyzed the mechanism of fluid diffusion in the unsaturated stratum. We then constructed a model for the seepage–diffusion of a pipeline leakage fluid, incorporating variables such as the saturation, permeability coefficient, diffusion pressure, and diffusion distance of soil microelements across various time intervals, using a stepwise algorithm in tandem with the Green–Ampt model and VG–Mualen permeability coefficient function. We investigated the influence of fluid self-gravity, saturated permeability coefficient, initial saturation, and intra-pipe pressure on the diffusion distance of the pipeline leakage fluid in an unsaturated stratum, focusing on specific cases. The results indicate that an increase in the saturated permeability coefficient, initial saturation, and intra-pipe pressure leads to an increase in the fluid diffusion distance. A simulation test was conducted to validate the proposed seepage–diffusion model. The findings of this study can be employed to predict the diffusion range of pipeline leakage fluids in various formation types, providing a vital foundation for pipeline leakage accident management.

Faulty cell prediction accuracy comparison of machine learning algorithms using temperature sensor placement optimization approach in immersion cooled Li-ion battery modules

Immersion cooling is a promising thermal management system for LiBs where the cells are submerged in a thermally conductive coolant, thus improving heat dissipation and prolonging battery life. The present study investigates an aligned arranged 4S4P immersion-cooled LiB module to develop a best-fit machine learning (ML) model that could predict the fault position in the module depending on the inputs from the temperature sensors that are optimized under different operating and fault conditions. The Pearson Correlation Coefficient (PCC) feature selection approach is used to optimize the sensors, while the data is generated from numerical simulations on a 4S4P immersion-cooled LiB module. The model validation through internal experimental trials is performed on a 2S2P battery module. The four ML classification algorithms, which include the K Nearest Neighbors, Random Forest (RF), Extreme Gradient, and Long Short Term Memory (LSTM), are trained on the optimized sensors data and are further tested internally and externally to assess their performance on unseen data within and outside the training range. A 5-fold cross-validation process is also implemented, and following a comprehensive comparison of the predictions based on the accuracies and model elapsed time, the best-fit model is identified. The results conclude that while the LSTM model slightly outweighs the other models with an accuracy of 98.18% for the specific external test cases, the RF model is chosen as the best-fit model with a prediction accuracy of 99.7% based on the error metrics and low training time for the internal testing. The outcomes of this present work contribute to the early identification of battery module failures, enhancing safety and reducing costs.

Impact of Solar Eclipses on Vision: Insights from Optical Coherence Tomography and Optical Coherence Tomography Angiography Analysis*

Purpose Solar retinopathy, resulting from solar eclipse exposure, poses risks to visual health. This study explores acute and chronic phase findings using clinical examinations and optical coherence tomography (OCT) and optical coherence tomography angiography (OCT-A) with a focus on longitudinal assessment. Methods Seven eyes with a history of unprotected solar eclipse exposure were included. Clinical examination, fundus photography, OCT, and OCT-A imaging were performed at initial assessment, as well as at one-month and six-month follow-up intervals. Data analysis included descriptive statistics. Results The cases, exposed without protection, underwent assessments, revealing variable visual acuity, outer retinal layer, and Henle fiber layer changes during follow-up. Regression of hyperreflectivity within the outer retinal and Henle fiber layers was observed over time in all eyes, although persistent microdefects within the outer retinal layer were noted in specific cases. OCT-A imaging revealed a larger foveal avascular zone, which persisted over a six-month period in select cases. Additionally, affected eyes exhibited a decrease in superficial vascular density, with subsequent improvement noted during the six-month period. Conclusion Solar retinopathy can result in visual impairment, accompanied by alterations observed in the Henle fiber layer using OCT. Additionally, OCT-A findings indicate possible vascular involvement. This study underscores the significance of adopting protective measures during solar eclipses and emphasizes the value of employing longitudinal multimodal imaging techniques to comprehend the pathophysiology of the condition.

Artificial intelligence in practice: measuring its medical accuracy in oculoplastics consultations

Purpose: The aim of this study was to investigate the medical accuracy of responses produced by Chat Generative Pretrained Transformer 4 (Chat GPT-4) and DALLE-2 in relation to common questions encountered during oculoplastic consultations. Methods: The 5 most frequently discussed oculoplastic procedures on social media were selected for evaluation using Chat GPT-4 and DALLE-2. Questions were formulated from common patient concerns and inputted into Chat GPT-4, and responses were assessed on a 3-point scale. For procedure imagery, descriptions were submitted to DALLE-2, and the resulted images were graded for anatomical and surgical accuracy. Grading was completed by 5 oculoplastic surgeons through a 110-question survey. Results: Overall, 87.3% of Chat GPT-4’s responses achieved a score of 2 or 3 points, denoting a good to high level of accuracy. Across all procedures, questions about pain, bruising, procedure risk, and adverse events garnered high scores. Conversely, responses regarding specific case scenarios, procedure longevity, and proceduredefinitions were less accurate. Images produced by DALLE-2-were notably subpar, often failing to accurately depict surgical outcomes and realistic details. Conclusions: Chat GPT-4 demonstrated a creditable level of accuracy in addressing common oculoplastic procedure concerns. However, its limitations in handling case-based scenarios suggests that it is best suited as a supplementary source of information rather than a primary diagnostic or consultative tool. The current state of medical imagery generated by means of artificial intelligence lacks anatomical accuracy. Significant technological advancements are necessary before such imagery can complement oculoplastic consultations effectively.

Social Media Tourism Marketing Optimization: Improving Promotion Effectiveness and Broadening the Consumer Base

This research investigates the price-based strategy of social media in tourism marketing, specifically in China. The study involves both primary and secondary methodology research, represented in qualitative format. The primary research evidence is collected from Chinese travelers and relevant people in the travel industry by using online questionnaires and interviews since it is a more efficient way to receive more responses within less amount of time. On the other hand, secondary evidence is collected from various online sources, including relevant and credible articles, news reports, and academic essays. According to the secondary evidence, the key areas considered in the pricing-based Marketing Landscape for the Travel industry on social media are the challenges, stakeholders perceptions, pricing-based strategies, and the impact of social media marketing. By evaluating the evidence and data, the research concludes that from both social and economic perspectives, social media marketing has a more significant positive impact than a negative one on the views of companies and customers, which benefit both firms and consumers in terms of the ability to provide better product quality. This research further shows that the tourism industry needs to adopt a pricing strategy tailored to firms specific cases in order to achieve more effective marketing and operation efficiency with long-term development.

The dark side of drug repurposing. From clinical trial challenges to antimicrobial resistance: analysis based on three major fields

Drug repurposing is a strategic endeavor that entails the identification of novel therapeutic applications for pharmaceuticals that are already available in the market. Despite the advantageous nature of implementing this particular strategy owing to its cost-effectiveness and efficiency in reducing the time required for the drug discovery process, it is essential to bear in mind that there are various factors that must be meticulously considered and taken into account. Up to this point, there has been a noticeable absence of comprehensive analyses that shed light on the limitations of repurposing drugs. The primary aim of this review is to conduct a thorough illustration of the various challenges that arise when contemplating drug repurposing from a clinical perspective in three major fields—cardiovascular, cancer, and diabetes—and to further underscore the potential risks associated with the emergence of antimicrobial resistance (AMR) when employing repurposed antibiotics for the treatment of noninfectious and infectious diseases. The process of developing repurposed medications necessitates the application of creativity and innovation in designing the development program, as the body of evidence may differ for each specific case. In order to effectively repurpose drugs, it is crucial to consider the clinical implications and potential drawbacks that may arise during this process. By comprehensively analyzing these challenges, we can attain a deeper comprehension of the intricacies involved in drug repurposing, which will ultimately lead to the development of more efficacious and safe therapeutic approaches.

Amplification in parametrically-driven resonators near instability based on Floquet theory and Green’s functions

Abstract Here we use Floquet theory to calculate the response of
parametrically-driven time-periodic systems near the onset of parametric
instability to an added external ac signal or white noise.
We provide new estimates, based on the Green's function method,
for the response of the system in the frequency domain.
Furthermore, we present novel expressions for the power and
noise spectral densities.
We validate our theoretical results by comparing our predictions for the
specific cases of a single degree of freedom parametric amplifier and of the
parametric amplifier coupled to a harmonic resonator with the
numerical integration results and with analytical approximate results obtained via
the averaging method up to second order.

Community-based participatory-research through co-design: supporting collaboration from all sides of disability

BackgroundAs co-design and community-based participatory research gain traction in health and disability, the challenges and benefits of collaboratively conducting research need to be considered. Current literature supports using co-design to improve service quality and create more satisfactory services. However, while the ‘why’ of using co-design is well understood, there is limited literature on ‘how’ to co-design. We aimed to describe the application of co-design from start to finish within a specific case study and to reflect on the challenges and benefits created by specific process design choices.MethodsA telepractice re-design project has been a case study example of co-design. The co-design was co-facilitated by an embedded researcher and a peer researcher with lived experience of disability. Embedded in a Western Australian disability organisation, the co-design process included five workshops and a reflection session with a team of 10 lived experience and staff participants (referred to as co-designers) to produce a prototype telepractice model for testing.ResultsThe findings are divided into two components. The first describes the process design choices made throughout the co-design implementation case study. This is followed by a reflection on the benefits and challenges resulting from specific process design choices. The reflective process describes the co-designers’ perspective and the researcher’s and organisational experiences. Reflections of the co-designers include balancing idealism and realism, the value of small groups, ensuring accessibility and choice, and learning new skills and gaining new insights. The organisational and research-focused reflections included challenges between time for building relationships and the schedules of academic and organisational decision-making, the messiness of co-design juxtaposed with the processes of ethics applications, and the need for inclusive dissemination of findings.ConclusionsThe authors advocate that co-design is a useful and outcome-generating methodology that proactively enables the inclusion of people with disability and service providers through community-based participatory research and action. Through our experiences, we recommend community-based participatory research, specifically co-design, to generate creative thinking and service design.

Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE

Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities’ preparedness for future pandemics.

Addressing Counterfeiting and Fraud Concerns in Healthcare Packaging and Labeling with Blockchain: Opportunities and Challenges

Blockchain technology (BT), originally developed to facilitate secure digital monetary transactions, has recently gained significant traction in various healthcare sectors. Characterized by the exponential growth of sensitive data, the healthcare sector is poised to witness the emergence of BT. This emergence is primarily driven by the pressing need to globally expose, protect against threats, ensure confidentiality, and establish traceability for the plethora of sensitive data continuously generated by the healthcare industry. The healthcare supply chain focuses on traceability due to the prevalence of counterfeit and recalled drugs. Managing operational constraints such as temperature, humidity, and air quality within specified parameters is paramount. The various processes involved in international trade transactions contribute to the creation of numerous records, each of which is meticulously entered into the systems of the companies involved. Therefore, the problem set for this study was: What are the challenges and prospects for BT in the healthcare sector? To answer this question, the following objective was set: describe and examine the challenges and prospects of BT in the healthcare sector. In addition, a key research objective was to identify specific applications and use cases that can benefit the most from this technological advancement. In line with the research objective, a systematic review of all studies BT for traceability, anti-counterfeiting, and fraud detection was conducted from January 2023 to September 2023. Using robust tools such as VosViewer, we used bibliometric metrics from the renowned medical repository PubMed to construct and visually represent data analysis networks. BT shows remarkable potential to improve traceability and optimize supply chain management within healthcare organizations. The study includes a deep analysis of blockchain capabilities, including smart contracts, identity management, access control, and zero-knowledge proofing.

Image Quality Assessment Tool for Conventional and Dynamic Magnetic Resonance Imaging Acquisitions

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification.

Evaluation of inversion approaches for plates based on guided waves and modal analysis

The identification of defects in plate-like structures has been successfully treated using both local guided ultrasonic waves and global modal quantities. Although there are many papers on these techniques, a lack of comparability between the two methods persists. This makes it difficult for users to identify the most appropriate method for the defect in question. This paper examines the effect of different parameterizations of a circular defect in a square aluminum plate on the system response in a case study. The measured local ultrasound signal from a propagating guided wave makes up the initial data set. The first method uses the entire waveform for the objective function of the optimization problem, while the second considers the velocity of the A0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_0$$\\end{document} mode. In the third and fourth methods, the first and second global natural frequencies of the plate modeled with free-free boundary conditions are investigated. The numerical models are validated experimentally through measurements with a laser Doppler vibrometer. This results in the qualitative and quantitative evaluation of the objective functions for all parameter combinations of the defect. The recommendation to sequentially employ modal analysis and then the ultrasound procedure is made for the defect type used in this study. The data gathered on the objective functions suggests that potential joint employment of the natural frequency and ultrasound methods may increase computational efficiency. For a specific case, the methods could also complement each other in terms of challenges such as local minima.

Fast Aerodynamic Prediction of Airfoil with Trailing Edge Flap Based on Multi-Task Deep Learning

Conventional methods for solving Navier–Stokes (NS) equations to analyze flow fields and aerodynamic forces of airfoils with trailing edge flaps (TEFs) are known for their significant time cost. This study presents a Multi-Task Swin Transformer (MT-Swin-T) deep learning framework tailored for swift prediction of velocity fields and aerodynamic coefficients of TEF-equipped airfoils. The proposed model combines a Swin Transformer (Swin-T) for flow field prediction with a multi-layer perceptron (MLP) dedicated to lift coefficient prediction. Both networks undergo gradient updates through the shared encoder component of the Swin Transformer. Such a trained network model for computational fluid dynamics simulations is both effective and robust, significantly improving the efficiency of complex aerodynamic shape design optimization and flow control. The study further investigates the impact of integrating multi-task learning loss functions, skip connections, and the network’s structural design on prediction accuracy. Additionally, the effectiveness of deep learning in improving the aerodynamic simulation efficiency of airfoils with TEF is examined. Results demonstrate that the multi-task deep learning approach provides accurate predictions for TEF airfoil flow fields and lift coefficients. The strategic combination of these tasks during network training, along with the optimal selection of loss functions, significantly enhances prediction accuracy compared with the single-task network. In a specific case study, the MT-Swin-T model demonstrated a prediction time that was 1/7214 of the time necessitated by CFD simulation.

Identification of applicable regulation and public policy gaps regarding marine renewable energy in Mexico

Integrating renewables into the energy matrix enables efficient energy systems and meets the UN Sustainable Development Goals 7) affordable and clean energy, 9) industry, innovation, and infrastructure, 12) responsible production and use, and 13) climate action. A specific case is the use of marine renewable energy (MRE) for power supply in coastal cities affected by environmental and anthropogenic threats. MRE can be harnessed from waves, currents, offshore wind, and thermal and salinity gradients. In emerging economies, the marine renewable energy industry is at different stages from quantifying the potential, to device development, or testing. Currently, in Mexico, there is a wide range of marine energy projects despite the technological advances there is no framework for the device’s deployment.As a guide for developers, this article presents the current Mexican legislation on MRE. The review of the legislation has identified some niche opportunities that were highlighted during the research; legal pathway for planning and operational purposes.