Expert Experience Research Articles

Real-world experience with a treatment with a skincare regimen of products containing the Macrocystis pyrifera ferment for optimizing facial skin rejuvenation.

Specialized aesthetic skincare treatments are an important part of helping reduce facial signs of aging. This article highlights real-world experience with a Macrocystis pyrifera ferment-containing skincare regimen comprising a cleansing foam, a serum, and a moisturizer with anti-aging, anti-inflammatory, anti-erythema, and healing properties for facial skin condition improvement. The real-world case (RWC) series presented highlights and the expert panel's clinical experience with the M. pyrifera ferment-containing skincare regimen used for 12 weeks to improve facial skin conditions. The panelists convened a meeting to discuss and select RWCs from their practice using the M. pyrifera ferment-containing skincare regimen. The RWC series showed that erythema and inflamed, easily irritated skin bother patients, even when it is mild. Reducing inflammation, erythema, and visible signs of facial aging and improving skin health contributed to patient satisfaction. The M. pyrifera ferment-containing skincare regimen comprising a cleansing foam, a serum, and a moisturizer is effective in decreasing the visible effects of inflammation and signs of aging while promoting healing by enhancing barrier resilience and recovery.

AutoAMS: Automated attention-based multi-modal graph learning architecture search

Multi-modal attention mechanisms have been successfully used in multi-modal graph learning for various tasks. However, existing attention-based multi-modal graph learning (AMGL) architectures heavily rely on manual design, requiring huge effort and expert experience. Meanwhile, graph neural architecture search (GNAS) has made great progress toward automatically designing graph-based learning architectures. However, it is challenging to directly adopt existing GNAS methods to search for better AMGL architectures because of the search spaces that only focus on designing graph neural network architectures and the search objective that ignores multi-modal interactive information between modalities and long-term content dependencies within different modalities. To address these issues, we propose an automated attention-based multi-modal graph learning architecture search (AutoAMS) framework, which can automatically design the optimal AMGL architectures for different multi-modal tasks. Specifically, we design an effective attention-based multi-modal (AM) search space consisting of four sub-spaces, which can jointly support the automatic search of multi-modal attention representation and other components of multi-modal graph learning architecture. In addition, a novel search objective based on an unsupervised multi-modal reconstruction loss and task-specific loss is introduced to search and train AMGL architectures. The search objective can extract the global features and capture multi-modal interactions from multiple modalities. The experimental results on multi-modal tasks show strong evidence that AutoAMS is capable of designing high-performance AMGL architectures.

Difficulties in Extracting Dentures Stuck in The Esophagus

Foreign bodies in the esophagus are one of the common problems that ENT doctors often face. One of them is a foreign body with dentures in the esophagus. The diagnosis of a foreign body of dentures in the esophagus is established based on anamnesis, physical examination, radiological examination, and esophagoscopy. The act of esophagoscopy is performed as diagnostic and therapeutic. If not handled immediately and adequately, foreign bodies in the esophagus can cause complications such as esophageal perforation, blockage, and pressure of the foreign body into the airway and other complications. Patient readiness, availability of tools, and the presence of experienced experts are significant factors in overcoming this case. Prompt treatment using esophagoscopy is a safe and effective way to perform foreign body extraction in most cases. Thoracotomy to esophagectomy is performed in cases of foreign bodies in the esophagus that experience difficulties or failure in extracting by esophagoscopy. It was reported that two cases of foreign bodies with dentures in the esophagus were removed by thoracotomy accompanied by esophagectomy.

Rapid Algorithm for Generating and Selecting Optimal Metro Train Speed Curves Based on Alpha Zero and Expert Experience

Rapid Algorithm for Generating and Selecting Optimal Metro Train Speed Curves Based on Alpha Zero and Expert Experience

Intracerebral Hemorrhage Prognosis Classification via Joint-Attention Cross-Modal Network.

Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care.

Using Engineering Methods (Kaizen and Micromovements Science) to Improve and Provide Evidence Regarding Microsurgical Hand Skills

ObjectiveMicrosurgical interventions involve the interaction of numerous variables, making objective analysis of skill proficiency challenging. This difficulty is even more pronounced in low-resource contexts. Continuous improvement methodologies such as Kaizen-PDCA and Micro-Movements Science can address this issue. This study aimed to demonstrate the advantages of designing and implementing microsurgical training programs using these methodologies. MethodsFollowing an extensive literature review of Kaizen-PDCA and Micro-Movements Science and under the guidance of experienced neurosurgeons and engineers, a microvascular bypass training program was developed using the human placenta. Subsequently, the training program was used to analyze and describe the process of a trainee neurosurgeon in Argentina with no prior experience in microvascular anastomosis, as the operator gained proficiency. ResultsThe trainee required 12 attempts to achieve the program goals. The longest procedural time was during the first attempt (1 h 49 min 05 s with two mistakes), while the shortest time was during the fourth attempt (53 min 29 s with three mistakes). After 12 attempts, the trainee made no mistakes, and the procedural time was reduced to 57 min 37 s. The final learning curve demonstrated a regular pattern and reached a plateau after seven attempts. ConclusionsThe training program and methodology effectively assessed, facilitated, and demonstrated the acquisition of microsurgical skills. Kaizen-PDCA and Micro-Movements Science enabled the effective use of expert experience, detailed evaluation of microsurgical procedures, and integration into a continuous improvement cycle. The program structure could also be valuable for teaching, evaluating, and enhancing similar surgical procedures.

Factors affecting architectural decision‐making process and challenges in software projects: An industrial survey

AbstractSoftware architecture plays a fundamental role in overcoming the challenges of the development process of large‐scale and complex software systems. The software architecture of a system is the result of an extensive process in which several stakeholders negotiate issues and solutions, and as a result of this negotiation, a series of architectural decisions are made. This survey study aims to determine the experiences of the software industry experts with respect to architectural decision‐making, the factors that are effective in decision‐making, and the technical and social problems they encounter. An online questionnaire‐based survey was conducted with 101 practitioners. The responses were analyzed qualitatively and quantitatively. Analysis of responses revealed that the majority of the participants prefer to document some or all of the architectural decisions taken and to store these documents in web‐based collaboration software. Decisions are usually made by teams of two or three, and discussion‐based approaches (brainstorming and consensus) are adopted. In the software architecture decision‐making process, “major business impact” is the most challenging situation. Information sharing and keeping track of decisions and decision rationale are areas in need of improvement as identified by most participants. From the participants' feedback and their answers to open‐ended questions, we concluded that the software architecture decision‐making process has an important role in the industry. Our key findings are that decisions made in the architectural decision‐making process are taken by teams and generally all decisions are documented. In projects where decisions are made by a single person, peer pressure is found to be significantly different from pressure in projects where decisions are made by the group. This is an indication that as the number of people in the decision‐making process increases, the disagreements also increase.

Corporate Governance Factors and Financial Reporting Timeliness: A Systematic Review and Future Research

One of the crucial elements influencing the level of financial reporting is timeliness, which is also a characteristic of good governance; thus, this study reviews and synthesises existing literature on corporate governance factors that influence financial reporting timeliness. This systematic review is based on twenty articles published in the Scopus, Emerald, and Web of Science databases. By examining publications that employ measurements of financial reporting timeliness, five corporate governance factors can influence financial reporting timeliness. The systematic review findings show that specific characteristics of the audit committee, board of directors, size of audit firm, chief executive officer and chief financial officer influence the timeliness of financial reporting. The study also discovered the need to appoint an audit committee chairperson with accounting expertise and public accounting experience, which can improve financial reporting timeliness. Due to search constraints, this study may have excluded relevant publications. This study contributes to the academic discourse on agency theory in corporate governance, enhancing understanding and reducing management's opportunistic behaviour to achieve the company's goals and reduce agency costs. Furthermore, it can significantly assist legislators in resolving the highly debated issue of financial reporting timeliness by developing and improving guidelines and regulations for public companies.

A dynamic correction method for the optimal value settings of the solution purification process at multiple time scales

The solution purification process includes multiple continuous reactors. Setting the key technical indicators of each reactor through global optimization is the prerequisite for realizing the optimal operation of the entire process. Affected by fluctuations in inlet conditions, adjustments of operating parameters, and random disturbances, the operating status of the solution purification process will change accordingly, causing the optimal value settings based on global optimization to become no longer applicable. To ensure the applicability of the optimal value settings as the process changes and considering that the production data collected at different time scales contain different process information, this study proposes a dynamic correction method for the optimal value settings of the solution purification process at multiple time scales. First, considering the low-frequency testing data that can reflect the operation effect, the low-frequency correction is realized by combining mechanism knowledge and expert experience. Second, based on the characteristic that the high-frequency detection data can reflect the changing operating status in time, a supervised self-organizing map method is proposed to classify the changing trends in the operating status. Finally, an integrated, spatiotemporal, just-in-time learning method (with multiple changing trends in the operating status) is proposed to realize high-frequency correction. The experimental results show that the proposed method can dynamically correct the optimal value settings and reduce resource consumption while ensuring product quality.

A Review of Artificial Intelligence in Tumor Pathology Image Analysis

Accurate diagnosis of tumors is crucial to the treatment and prognosis of patients. Pathological diagnosis is regarded as the "gold standard" of tumor diagnosis, which helps to detect the disease at an early stage and formulate precise treatment plans for patients. However, traditional pathology diagnosis relies heavily on the expertise and diagnostic experience of physicians, making the quality and accuracy of pathology diagnosis largely dependent on their individual capabilities. With the popularization of Whole Slide Image (WSI) technology, the application of AI in pathology has gained significant momentum. With its powerful analyzing ability, AI has been widely used in computational pathology, especially in pathology-assisted diagnosis, showing great potential. This paper first explores two core tasks of AI in the field of pathology image analysis - image segmentation and image classification. Finally, it looks at the challenges and opportunities facing the field.

Multi-robot multi-station welding flow shop closed-loop rescheduling with deep reinforcement learning and improved artificial bee colony algorithm

Multi-robot multi-station manufacturing system has gained significant popularity in welding workshop for processing large jobs. The implementation of cooperative welding robots effectively enhances the efficiency and flexibility of the system. However, robot failures also bring challenges for rescheduling to determine the rescheduling timing and re-assign appropriate robots for the affected processes so as to minimize the losses in performance and the maximum completion time. Distinct from open-loop rescheduling approach, this paper proposes a closed-loop rescheduling method via deep reinforcement learning and improved artificial bee colony algorithm. This method addresses two crucial questions: when to trigger a rescheduling, and how to provide a rescheduling scheme. For the first issue, current methods make decisions according to indicators derived from expert experience, failing to adapt to the dynamic environment. Therefore, we propose a deep reinforcement learning method to learn the adaptive trigger policy for the trigger agent under real-time running state. For the second issue, we present an improved artificial bee colony algorithm to re-assign robots without changing processing sequence and feed the performance back to trigger agent. Specially, a variable local search method is incorporated to avoid falling into local optimum. Finally, the proposed method is applied to solve the instances with different scales and a practical case. The experimental results of both demonstrate that the proposed closed-loop rescheduling method can greatly reduce the deviation of the total completion time and the maximum completion time and have superior performance over other algorithms.

Two-stage residual networks for damage identification and location of stiffened composite panel based on guided waves

The damage detection of the stiffened composite panel, as a typical aircraft structure, is a research hotspot in Structural Health Monitoring (SHM). where guided waves propagate with multi-modal and dispersion characteristics. The traditional damage detection method manually extracts the potential discriminative features of the signal to achieve damage identification, depending on expert experience. In this paper, we propose a two-stage residual networks (ResNets) framework based on guided waves to locate damage in the stiffened composite panel, which automatically mines the high-dimensional features with sensitive discriminant information. The guided wave signal acquisition system collects four types of data: health data, stringer damage data, damage data on the skin of the stringer-side, and damage data on the skin-side. The first-stage utilizes a ResNet to classify the structure condition, while in the second-stage, three separate ResNets are employed to locate the damage according to the classification results of the first-stage. The experimental results show that the accuracy of the first-stage damage classification and the damage localization of the stringer and the skin of the stringer-side in the second-stage has reached 100%, and that of the skin-side is 99.13%, which significantly outperforms single-stage methods. This strategy of inter-class discrimination and intra-class precise localization of damage can not only identify the damaged regions but also determine the specific location of the damage, which greatly increases the performance of SHM. The present two-stage method is a potential solution for future SHM strategies and further investigation is warranted.

Making transdisciplinarity work for complex systems: A dynamic model for blending diverse knowledges

The world is increasingly characterized by a paradigm of interconnectivity within a complex system, so that impact to any single element or location is likely to cause unanticipated, unequal, and disruptive impacts elsewhere. As society has become more complex, the intractable problems of our global community have also become complex, interconnected, dynamic and nonlinear. Potential solutions to complex global issues will not be identifiable through efforts associated with any single discipline. Boundary-spanning collaborations and collective action are required to create the necessary paradigm shifts. In response to this need, this paper presents a transdisciplinary framework designed to blend different knowledges and resources via a non-hierarchical, self-organizing collaboration. This boundary-spanning process fosters the integration of diverse scholarly expertise, artistic expression and lived experiences to engage broad audiences in knowledge exchange. As illustration, a virtual art + science exhibition produced during the COVID-19 pandemic, Long COVID: We Are Here! is examined. The exhibition, co-created by six artists and six scientists, curated multiple voices, skills, talents and artefacts to explore perspectives of an emerging global health and social problem. This paper scrutinizes the design process, production, and outcomes of the exhibition as a model for engaging with complexity, at multiple scales; as a method of knowledge sharing and new knowledge creation, of disseminating empirical knowledge to a broad audience, and for enabling and catalyzing transformative and sustainable pathways for social change.

Advanced practice nurses’ experiences of patient safety: a focus group study

Background: Patient harm from unsafe care is an increasingly global phenomenon leading to death or disability. Drawing on their expertise, Advanced Practice Nurses provide the opportunity to improve care quality and safety. Aim: To explore Nurse Practitioners and Clinical Nurse Consultants’ experiences in patient safety. Design: A qualitative design was used involving six audio-visually recorded focus group interviews. Participants working in an acute or community adult nursing speciality were involved. Methods: Twenty-eight Advanced Practice Nurses (female 82.1%, mean age 47.5 ± 10 years) were recruited by convenience and snowball sampling. After transcription of interview data, qualitative content analysis was conducted. Results: Six categories were identified: patient safety as the highest priority (1), special contribution to patient safety (2), patients/relatives role in safety (3), multidisciplinary team approach (4), government regulation in safety (5), and further needs to improve safety (6). Advanced Practice Nurses saw themselves as role models and leaders for other healthcare staff through their expertise and professional experience and thus able to see the bigger picture in health. They identified as change agents at the system-level due to their decision-making ability and multi-professional team connectivity. Conclusions: This study emphasises the key position of extended nursing roles and the need for future development of patient safety strategies in hospitals and community care. As influential leaders, Advanced Practice Nurses are best placed to identify improvements. They play a central role in guiding the multi-professional team, the patient and their family, educating nursing staff, and identifying and addressing system-wide safety gaps to improve patient safety.

Automatic labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation

Ancient murals have suffered from continuous damage over time, and especially paint loss disease. Therefore, disease labeling, as the basis for ancient mural restoration, plays an important role in the protection of cultural relics. The predominant method of disease labeling is currently manual labeling, which is highly dependent on expert experience, time consuming, inefficient and results in inconsistent accuracy of the marking effect. In this paper, we propose a labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation. The proposed framework involves first the extraction of features from the hyperspectral image, and then image segmentation is performed based on the spatial features to obtain more accurate region boundaries. Then, the hyperspectral image’s regions are classified based on their spatial-spectral characteristics, and the candidate areas of paint loss disease are obtained. Finally, by leveraging the true color image segmentation results, the proposed disease labeling strategy combines the results of classification and segmentation to propose the final paint loss disease labeling areas. The experimental results show that the proposed method can not only combine the hyperspectral space and spectral information effectively to obtain accurate labeling of paint loss disease, but can also mark the paint loss disease not easily observed using ordinary digital cameras. Compared with the state-of-the-art methods, the proposed framework could be promising for accurate and effective paint loss disease labeling for ancient murals.

Strategies for Integrating Technology into Physical Education Pedagogy in the 21st Century

Background and Aim: In the educational environment of the twenty-first century, the incorporation of technology has become a key factor in driving pedagogical innovation and improving the quality of education. This study aims to assess the implementation of technology integration in physical education identify the problems it faces, to propose strategies to enhance the integration of technology in physical education. Materials and Methods: Firstly, the study sorted out the current situation and development trend of technology application in physical education through a literature review. Next, the study collected the views and practical experiences of experts from 7 universities in Yunnan Province, China, on the integration of technology into teaching strategies through expert interviews. The results of the expert interviews with the 7 experts were summarized and analyzed using Nvivo for textual data, and all the interview texts were coded hierarchically through the rooted theory approach. Based on the coding results, the index system of the components of technology integration into physical education pedagogy strategies was drafted, and 5 experts were selected for IOC evaluation. Then, 20 experts conducted the Delphi method, and after two rounds of Delphi analysis the experts reached a consensus, and the system of indicators of the constituent elements of technology integration into physical education pedagogy strategies was finally established. Finally, the strategies were further discussed and evaluated through focus groups with 9 experts. Results: Through this study, a strategy for integrating technology into physical education pedagogy in the 21st century was finally established in five dimensions: teaching and learning environment, teaching and learning resources, services and management, monitoring and evaluation, and challenges and strategies. Conclusion: The results of the study showed that experts agreed on the feasibility of the strategy of integrating technology into physical education pedagogy and the scientific and reliable nature of the proposed strategy.

Living the Language of Psychotherapy: How Therapists Use Their Experiences from Being Patients

Personal therapy is a requirement in several psychotherapy-training programs, and more than 80% of therapists have been psychotherapy patients. No quantitative evidence of the effects on their patients’ outcomes has been presented. However, qualitative studies and expert experiences highlight the value of therapists having been patients themselves. To investigate this further, we used in-depth qualitative methods to explore the experiences of eight early career psychologists. For our data collection, the participants wrote reflexive notes on two occasions, and met for a full day of focus groups. The notes and transcriptions of the focus groups were analyzed from the perspective of reflexive hermeneutic phenomenology. In all, the therapists had been patients in 19 courses of various psychotherapies. The core experiential themes were: (1) their own suffering had been for real; (2) ambivalence of sharing vulnerability; (3) the significance of experiencing theoretical concepts – the language of psychotherapy – in real life; (4) dealing with shame; (5) the need to know that therapists may become so important that the patient can easily be hurt; and (6) the therapist became an inner model that the patient could use. The therapists seemed to use their psychotherapy experiences in their personal development and professional lives. Therapists may have a unique opportunity to benefit from psychotherapy due to the language they have acquired for sharing experiences with their therapists.

Knowledge-based decisions for asset management of urban water infrastructure in Iran and Brazil using a nominal-based multi-criteria group decision method

ABSTRACT Asset management of the Urban Water Infrastructure (UWI) is typically faced with limitations regarding uncertain operational data. This shortcoming can be improved using the knowledge-based decisions of experienced experts. However, since knowledge-based decisions depend on decision-makers’ knowledge and experience, verifying their results in various regions is essential. This subject was addressed in this research. Hence, four groups of experts in Iran and Brazil collaborated to assess the criteria effective in the Maintenance-Rehabilitation Planning (MRP) of urban water pipes. For this purpose, a nominal-based multi-criteria group decision-making method was developed. To verify the results of this method, the viewpoints of non-weighted and weighted decision-makers were investigated and compared pairwise. The findings of this research improved the asset management of the UWI in Iran and Brazil by identifying the criteria applicable to the MRP of the pipes based on knowledge-based decisions. Likewise, it was found that weighting the experts has no decisive effect on the results and is only effective in groups where the decision-makers’ weights are identically distributed. Moreover, it was shown that there is a relationship between the similarity of results and the similarity of experts’ profiles in knowledge-based decision groups of various countries such as Iran and Brazil.

Consensus Report and Recommendations on the Management of Late-stage Internal Derangement of the Temporomandibular Joint.

Introduction: This report investigates late-stage internal derangement (ID) of the temporomandibular joint (TMJ) with the aim of establishing a more effective and personalized treatment protocol to improve patients' quality of life (QoL). Material and methods: A consensus was reached among maxillofacial surgeons specializing in LSID, based on a literature research and collective expert experience following the Delphi method. Consensus was considered to be achieved when a response received at least 80% of votes. Results: Four expert groups were established, respectively, focusing on diagnosis, minimally invasive surgery (MIS), open surgery and joint replacement. A comprehensive approach to late-stage ID of the TMJ requires a consensus report. This underscores the need for a personalized treatment plan, considering the variability in clinical presentations and progression of this pathology. Our recommendations aim to optimize clinical outcomes and enhance patient QoL.

Research on the construction of multi objective coupling model and optimization method of ship form

AbstractMulti‐objective optimization of ship form can effectively reduce ship energy consumption, and is one of the important research topics of green ships. However, the computational cost of numerical simulation based on computational fluid dynamics (CFD) theory is relatively high, which affects the efficiency of optimization. Traditional subjective weighting methods mostly rely on expert's experience, which affects the scientificity of optimization. This paper effectively integrates the CFD method, the improved multi‐objective optimization algorithm and the objective weighting method to build a ship form multi‐objective optimization framework. Conduct multi‐objective optimization research on resistance and seakeeping performance of a very large crude oil carrier (KVLCC) ship. The improved bare‐bones multi‐objective particle swarm optimization (IBBMOPSO) algorithm is used to obtain the pareto front, and the kernel principal component analysis (KPCA) method is used to objectively assign the weight of each target. Finally, the optimal ship form scheme with high satisfaction was obtained. The multi‐objective optimization framework constructed in this paper can provide a certain theoretical basis and technical support for the development of ship greening and digital transformation.