Decision Strategies Research Articles

Exploring the Intersection of Mental and Reproductive Health Among Women Living with HIV in Spain: A Qualitative Secondary Data Analysis

Background/Objectives: Globally, girls and women make up over half of those living with Human Immunodeficiency Virus (HIV), facing unique reproductive and mental health challenges. An HIV diagnosis impacts motherhood desires and increases trauma, stigma, and depression risks. Addressing these overlapping vulnerabilities with tailored, comprehensive healthcare is essential. This study aims to qualitatively explore the complex interplay between mental health and reproductive decision making among women living with HIV (WLWH). Methods: A secondary data analysis approach was employed, utilizing qualitative data from a wider study. Theoretical sampling was used to recruit a sample of WLWH attending the HIV clinic at a public hospital in Badalona (Barcelona, Spain). Interviews took place in a private and quiet space between May 2019 and January 2020. To perform secondary data analysis, Braun and Clarke’s thematic analysis was used. Results: Twenty-six women living with HIV were interviewed, with an average age of 39.3 years. The participants had diverse nationalities and faced economic challenges. Following data analysis, four main themes were identified: Emotional Fragmentation and Reproductive Self-Perception after the HIV Diagnosis; Coping Strategies in Reproductive Decision Making; Impacting Emotional Resilience and Motherhood Decisions; and Emotions, Mental Health, and Desire for Motherhood. The themes revealed the profound emotional impact of HIV on their identity, mental health, and reproductive decision making, affecting motherhood aspirations. Conclusions: Women living with HIV face psychological challenges in reproductive decision making, including anxiety and stigma. The study highlights their resilience and emphasizes incorporating hope-based strategies into HIV care, advocating for integrated and mental health-focused approaches to improve support and outcomes.

COMCARE: A Collaborative Ensemble Framework for Context-Aware Medical Named Entity Recognition and Relation Extraction

The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the extraction process. Existing approaches, which typically rely on single models such as BiLSTM or BERT, often struggle with these complexities. Although large language models (LLMs) have shown promise in various NLP tasks, they still face limitations in handling token-level tasks critical for medical NER and RE. To address these challenges, we propose COMCARE, a collaborative ensemble framework for context-aware medical NER and RE that integrates multiple pre-trained language models through a collaborative decision strategy. For NER, we combined PubMedBERT and PubMed-T5, leveraging PubMedBERT’s contextual understanding and PubMed-T5’s generative capabilities to handle diverse forms of medical terminology, from standard domain-specific jargon to nonstandard representations, such as uncommon abbreviations and out-of-vocabulary (OOV) terms. For RE, we integrated general-domain BERT with biomedical-specific BERT and PubMed-T5, utilizing token-level information from the NER module to enhance the context-aware entity-based relation extraction. To effectively handle long-range dependencies and maintain consistent performance across diverse texts, we implemented a semantic chunking approach and combined the model outputs through a majority voting mechanism. We evaluated COMCARE on several biomedical datasets, including BioRED, ADE, RDD, and DIANN Corpus. For BioRED, COMCARE achieved F1 scores of 93.76% for NER and 68.73% for RE, outperforming BioBERT by 1.25% and 1.74%, respectively. On the RDD Corpus, COMCARE showed F1 scores of 77.86% for NER and 86.79% for RE while achieving 82.48% for NER on ADE and 99.36% for NER on DIANN. These results demonstrate the effectiveness of our approach in handling complex medical terminology and overlapping entities, highlighting its potential to improve clinical decision support systems.

Source Apportionment and Analysis of Potentially Toxic Element Sources in Agricultural Soils Based on the Positive Matrix Factorization and Geo-Detector Models

The potentially toxic element pollution of agricultural soils has become a significant environmental threat to food safety and human health. Accurately identifying sources of potentially toxic element pollution is key to developing effective pollution prevention and control measures. In this study, regional potentially toxic element pollution of the soils in the Nanliujiang River Basin was analyzed using the positive matrix factorization (PMF) model and the geo-detector model. First, topsoil samples from the study area were collected to analyze eight potentially toxic elements in the soil, including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. The PMF model was used to conduct source apportionment of the potentially toxic element data and identify the primary pollution sources and their contribution rates. Then, the geo-detector model was used to analyze the key factors affecting the spatial distribution of the potentially toxic elements and the influence of natural and human factors on the distribution of the potentially toxic elements. There are four potentially toxic element pollution sources of the agricultural soil in the study area: geological background, agricultural activities, industrial discharge, and river irrigation. The geological background contributed the most. The main factors affecting the spatial distribution of potentially toxic elements included agricultural activities, industrial discharge, and river irrigation. This integrated method can analyze the formation of potentially toxic element pollution in depth from the perspectives of source apportionment and spatial differentiation and provide a scientific basis and decision support for preventing and controlling potentially toxic element pollution in agricultural soils. This study provides a new method and scientific basis for identifying and preventing potentially toxic element pollution sources in agricultural soil and can guide the formulation of targeted soil pollution control measures.

Enhancing One Health outcomes using decision science and negotiation

One Health initiatives have advanced zoonotic disease management by recognizing the interconnectedness of three sectors of governance (human, ecosystem, and animal) and by identifying options that can improve full‐system health. Although One Health has had many successes, its full realization may be inhibited by a lack of strategies to overcome simultaneous impediments in decision making and governance. Decision impediments that hinder management may include uncertainty, risk, resource limitations, and trade‐offs among objectives. Governance impediments arise from disparities in costs and benefits of disease management among sectors. Tools and strategies developed from decision science, collaboration, and negotiation theory can help articulate and overcome coinciding decision and governance impediments and enhance multisectoral One Health initiatives. In cases where collaboration and negotiation are insufficient to address disparities in cross‐sector costs and benefits, altering incentive structures might improve disease‐specific outcomes and improve the realization of One Health.

An Improved Method for Human Activity Detection with High-Resolution Images by Fusing Pooling Enhancement and Multi-Task Learning

Deep learning has garnered increasing attention in human activity detection due to its advantages, such as not relying on expert knowledge and automatic feature extraction. However, the existing deep learning-based approaches are primarily confined to recognizing specific types of human activities, which hinders scientific decision-making and comprehensive environmental protection. Therefore, there is an urgent need to develop a deep learning model to address multiple-type human activity detection with finer-resolution images. In this study, we proposed a new multi-task learning model (named PE-MLNet) to simultaneously achieve change detection and land use classification in GF-6 bitemporal images. Meanwhile, we also designed a pooling enhancement module (PEM) to accurately capture multi-scale change details from the bitemporal feature maps through combining differencing and concatenating branches. An independent annotated dataset at Yellow River Delta was taken to examine the effectiveness of PE-MLNet. The results showed that PE-MLNet exhibited obvious improvements in both detection accuracy and detail handling compared with other existing methods. Further analysis uncovered that the areas of buildings, roads, and oil depots has obviously increased, while the farmland and wetland areas largely decreased over the five years, indicating an expansion of human activities and their increased impacts on natural environments.

Various biases in systematic review and meta-analysis and their assessment

Systematic reviews and meta-analyses synthesise existing evidence for clinical and scientific decision-making. Clinicians and researchers need to understand how to interpret the evidence with the bias involved, as well as the various available tools and how to use them when performing a systematic review. The validity of systematic reviews and meta-analyses depends on comprehensive assessment and mitigation of biases. Biases have the potential to compromise the validity and reliability of results. By being aware of and addressing these different biases, researchers and clinicians can more confidently interpret findings and increase the impact and dependability of their conclusions. The article discusses the biases involved in systematic reviews and meta-analyses and various ways to assess them.

You are entitled to access the full text of this document A decision science approach to redesigning organizational structure: empirical insights from business process mapping and strategy alignment

This study investigates the complex relationship between organizational structure and strategic planning, emphasizing how business process mapping contributes to decision-making frameworks in organizational design. Through a qualitative case study approach, it illustrates the benefits of integrating organizational structure with business processes to enhance the implementation of an organization's strategic plan. The research highlights the RACI Matrix as a crucial analytical tool in organizational design, ensuring clarity in roles, responsibilities, and accountability while supporting effective decision-making in business processes. Findings underscore the importance of structuring organizations based on optimized business processes to drive efficiency and strategic alignment. The novelty of this research lies in its methodical approach to translating strategic objectives into actionable business process maps, which subsequently serve as the foundation for designing organizational structures through RACI matrix analysis to achieve enhanced role clarity and adaptability. This alignment optimizes operational coherence and strengthens long-term organizational resilience. The study offers a structured framework for objectively designing organizational structures that directly support strategic objectives, providing valuable insights for practitioners and decision-makers in organizational science.

Mental Health in the Workplace from the Perspective of Oncology Nurses in Iran: A Qualitative Descriptive Study

Background: The workplace and the context in which oncology nurses work are unique due to the complex and dynamic nature of cancer care. Nurses who care for cancer patients are exposed to varying degrees of psychological pressure. The present study was conducted to explore oncology nurses’ perceptions regarding mental health in the workplace. Materials and Methods: This study was conducted in 2018–2019. The participants were recruited through purposive sampling from eight educational specialty cancer treatment centers in Iran. Data were collected through individual semistructured interviews and analyzed using the conventional qualitative content analysis method. The Consolidated criteria for Reporting Qualitative research (COREQ) checklist was used to document the report of the study. Results: The extracted concepts were classified into three main categories and 17 subcategories. The main categories included attention to nurses’ occupational stress-provoking factors, attention to emotional/psychological responses in the workplace, and healthy mental atmosphere in the workplace. Conclusions: The findings indicate that oncology nurses need to be supported to enhance their mental health in the workplace. The findings of this study could help policymakers and nurse managers to understand the importance of improving the mental health of nurses in cancer care. In this regard, it is essential to make the necessary plans and scientific decisions to design and provide strategies to alleviate workplace problems and improve nurses’ mental health.

Analysis of the relation between multicriteria decision-making methods and human decision strategies

Purpose This paper aims to investigate the relation between multiple criteria decision-making (MCDM) methods and the decision strategies used by the individual in the process of choosing in a specific context. Design/methodology/approach The method involves an experiment in an automobile-selling shop with 61 individuals performing the task of choosing a used vehicle independently: with and without the use of a decision support systems (DSSs) also using the Think Aloud verbal protocol. The paper used the descriptive statistics and signals test to test hypotheses. Findings The results prove the relation between two of the four analyzed decision-making (DM) strategies: additive difference and elimination by aspect. These two strategies exhibited significant differences for the TransparentChoice and M-MACBETH systems, being similar only for Visual PROMETHEE, and this system being the one that best facilitated the choice process for consumer goods, in this case, used vehicles. Research limitations/implications This study enhances understanding of decision strategies and multicriteria methods in used vehicle selection. Identifying Visual PROMETHEE as highly effective, it suggests that design and usability features play a pivotal role. However, limitations, such as the temporal cutoff and exclusion of electric vehicles, hinder a comprehensive grasp of DM processes in contemporary vehicle choices. Nonetheless, applying these insights to future contexts broadens theoretical contributions, highlighting a key potential of this study. Practical implications The study provides valuable insights into the factors influencing consumer decisions, potentially impacting purchasing patterns and, consequently, the used vehicle market. In addition, it underscores the need to tailor the design and functionalities of DSS to better align with users’ DM approaches, aiming to maximize the effectiveness and acceptance of these systems. Social implications The social contribution of this research lies in its ability to raise awareness among consumers about effective DM strategies, particularly those related to DSSs. This can be achieved through educational initiatives that inform consumers about the benefits of these systems when facing complex decisions, such as purchasing used vehicles. Furthermore, the study’s results have the potential to influence public policies in the automotive industry by allowing regulatory adjustments that promote practices aligned with consumer preferences and needs. This consumer-centric approach may lead to policies more tailored to market dynamics, fostering competitiveness and consumer satisfaction. Originality/value The innovative approach tested three types of DSS in an experiment, exploring their relationship with decision strategies in the specific context of used vehicle selection. This unique methodology contributes to the understanding of DM dynamics, particularly in the automotive scenario, providing a valuable perspective for literature and relevant professionals.

디지털플랫폼정부 구현을 위한 데이터기반행정 활성화 전략 연구: 중앙정부와 지방정부의 비교 분석 논의를 중심으로

This study seeks to address the causes of wicked problems and derive effective solutions through data-driven administrative innovation. In this context, the government is adopting digital platforms to facilitate data-driven, scientific decision-making and foster transparency and openness in administrative operations. Using data from the “Survey on Data-Based Administration in the Era of Cloud Transformation,” this study statistically examines the influence of technical, organizational, managerial, and policy factors on the activation of data-driven administration, specifically aimed at enhancing data sharing and inter-organizational collaboration. Findings indicate that data infrastructure (technical factor), an innovation-oriented organizational culture (organizational and managerial factor), performance management (policy factor), and organizational behavior (acting as a mediator) significantly impact the activation of data-driven administration. Conversely, data capability -a variable measuring public servants’ data analysis and utilization skills within organizational and managerial factors- was not found to significantly influence the activation of data-driven administration or organizational behavior. Additionally, a comparative analysis between central and local governments revealed distinct differences in activation pathways for data-driven administration between these levels of government. These findings suggest that organizational culture, infrastructure, management systems, and changes in organizational behavior are more critical to enabling data-driven government than individual employees' data-related skills and knowledge. The results further underscore the need for customized policies tailored to the specific characteristics and requirements of central and local governments in their efforts to advance data-driven administration.

Competing fairness ideals underlie wealth inequality across decision contexts

AbstractWealth inequality is one of the most profound challenges confronting society today. However, an important issue in addressing inequality lies in formalizing the diversity of individual perspectives regarding what constitutes a fair distribution of resources. We tackle this topic by simulating wealth inequality through the allocation of bonus endowments in both Dictator Game (DG) and Ultimatum Game (UG) settings and capturing distributive decisions. By integrating a computational model, we quantify individual differences in the interplay between financial self-interest and competing pro-social motivations that emerge in the context of pre-existing wealth inequity. Our behavioral results show that, on average, pre-existing wealth influences distributive preferences across both allocations and proposals. Yet, inequality elicits non-uniform fairness concerns. Using a hierarchical clustering approach, we objectively categorise participants’ behavior elucidating four distinct decision strategies: ‘Pro-Self’, ‘Table Egalitarianism’, ‘Total Egalitarianism’, and ‘Moral Opportunism’. A balanced distribution of strategies is observed during allocations (DG), whereas Table Egalitarianism prevails in strategic proposals (UG), highlighting the influence of strategic considerations on decision strategy. Furthermore, we demonstrate an association between strategies across decision contexts. Our findings thus contribute a principled framework to formalize distributive preferences, revealing that, with respect to both altruistic allocations and strategic proposals, competing ideals of fairness underlie distributive preferences under wealth inequality.

Colliding identities? The role of multiple identities among historically underrepresented students pursuing scientific research careers

ABSTRACT Motivated by Identity Integration and Threat theories, this preregistered study examined associations between scientist and ethnic identities and future scientific research career intentions among African American and Hispanic STEM undergraduates (N = 1,247). Scientist identity was positively correlated with junior-year ethnic identity and predicted senior-year scientific research career intentions. Neither scientist nor ethnic identities predicted senior-year self-esteem (which may influence science career decisions). However, minority science training program (MSTP) enrollment influenced the relationship between scientist identity and self-esteem, and between ethnic identity (belongingness) and scientific research career intentions. Neither ethnicity nor gender were moderators. The results indicate that different identities can relate to one another and uniquely influence scientific research career decisions among African American and Hispanic students, especially among MSTP enrollees.

Spatio-temporal evolution and obstacle diagnosis of human settlements based on the “production-living-ecological” functions

Sustainable development is a hot topic of global concern and sustainable human settlements (HS) are crucial to people’s happiness. Thus, strengthening the construction of HS will help enrich human settlements geography with theories of HS interactions, clarify the existing problems of the Chengdu-Chongqing urban agglomeration (CC), promote the harmonization of the human-land relationship, and realize the SDGs. The results were as follows. (1) the quality of HS exhibited an upward trend with diminishing disparities in PLE functions. (2) A spatial layout featuring the high quality of HS in the east and west and low quality in the central areas was identified, with Chengdu and Chongqing serving as two high-value core areas. (3) The evolution of the quality of HS levels to achieve a leapfrog shift was challenging and the spatial spillover effect and phenomenon of “club convergence” were evident. (4) Living function was a primary obstacle at the criterion layer and indicators within production function became major obstacle factors at the element layer. These findings have enriched the research scope of human settlements geography from the scale of urban agglomerations, and provided scientific decisions for the SDGs.

Quantitative estimation of the coefficient of variation for explosive force tests using Myotest and Sergeant Test

This study aims to quantify The Coefficient of Variation to test the explosive force of footballers’ lower extremities using MYOTEST and Sargent test. Tests and measures are considered to be as major parts to take the right scientific decisions in the training process like diagnosis, evaluation, orientation and selection. To complete the study, the researcher used the descriptive method by applying the explosive force test for the lower extremities using the MYOTEST Device (Saut puissance), (Saut détente) and the classic method (Sargent Test) on a sample group of 33 players from M’sila football teams (MCM and WRM) and the study results were as follows: Dispersion in modern tests is less than in traditional tests; The use of modern technologies is more effective in evaluating explosive force of footballers’ lower extremities in terms of coefficients of variation in comparison with traditional tests; The main difference between them is in the classification of normative levels of explosive force according to dispersion and uncertainty ratios.

Investment Market Trend Forecasting and Strategy Optimization based on Big Data Analytics

The financial market is an important part of the global economy, and there exists a certain special internal regularity in its changes. With the rapid development of big data technology, the huge amount of data in the financial market provides new opportunities for the prediction of investment trends and strategy optimization. Based on big data analytics technology, combined with machine learning models, the study explores how to construct investment market trend prediction models through effective data preprocessing and feature engineering, and evaluates and compares the performance of different prediction models. How to accurately predict the direction of the financial market will be an important reference value for relevant practitioners as well as researchers. Traditional financial time series research mainly focuses on linear trend fitting based on statistical knowledge, however, the trend of the financial market is not limited to pure mathematical and rational function curves, the financial market is affected by a combination of various factors from all walks of life. Big data analytics has significant application potential in the investment market, providing investors with more scientific and accurate decision support.

Mechanisms of residential spatial differentiation and heritage identity in residential historic district: a case study of the Barkhor Street, Tibet

ABSTRACT Under the dual background of urban renewal and heritage conservation, it is not clear what changes have taken place in the heritage identity and what is the connection with the residential spatial differentiation. This study takes a socio-spatial approach, uses a quantitative analysis approach to assess Barkhor Street’s residential space in terms of two factors: residential quality and heritage utilization. It also looks at how spatial differentiation affects residential historic districts’ heritage identities. It aims to re-understand the transformation process of heritage identity in different contexts. This study revealed that (1) variations in living spaces among people in residential historic districts can yield both positive and negative outcomes for their heritage identity. (2) Heritage identity is mediated in part by residential quality and the use of historical resources. (3) There is also a combined effect of individual attributes like the annual per capita household income, age, and ethnicity. Factors influencing people’ heritage identification include the per capita housing area, type of housing ownership, distance from religious heritage, and store form type. It is advised that mixed-use housing patterns be used to increase housing suitability and that procedures for community involvement and democratic, scientific decision-making be set up for preservation and renewal.

Response of Carbon Storage Evolution to Future Climate and Policy at the Basin Scale

In this study, we explored the impact of land-use changes on carbon sinks in terrestrial ecosystems from a watershed perspective under the backdrop of "carbon peak and neutrality." This will provide robust support for scientific decision-making and the sustainable development of integrated watershed management. Watershed ecosystems encompass natural, social, and economic dimensions and are crucial for improving regional ecological quality and ensuring sustainable development. The Dongting Lake Basin （DLB） is situated at the junction of China's Yangtze River Economic Belt and Coastal Economic Belt, playing an important role in maintaining ecological balance and coordinating economic development. In recent years, rapid economic growth, prolonged agricultural activities, and substantial population pressure have accelerated wetland degradation, the expansion of construction land, and the reduction of ecological land within the basin. Consequently, this has led to a decline in the carbon sequestration capacity of terrestrial ecosystems. This study, utilizing the PLUS model, aims to simulate the spatial dynamics of land use within the DLB across three climate-policy scenarios spanning the temporal domain from 2030 to 2060. Furthermore, employing the IPCC guidelines for national greenhouse gas inventories, the investigation projects the ramifications of prospective land use alterations on the carbon storage （CS） within terrestrial ecosystems in the DLB over the aforementioned timeframe. The outcomes delineated shifts in CS within terrestrial ecosystems under diverse future scenarios. The following key findings were emanated from the analysis： ① During the temporal span from 2020 to 2060, under the auspices of the Natural Development Scenario （NDS）, the encroachment of construction land primarily targeted arable land, forests, and grasslands. ② The Economic Co-ordination Scenario （ECS）, akin to the NDS, curtailed the diversion of cropland and aquatic resources. ③ Conversely, the Ecological Priority Scenario （EPS） prioritized the transformation of grasslands into forests, ensuring the sustained expansion of wooded areas. Moreover, the estimated CS of the DLB was at 3.6×109 t as of 2020. Projections suggested that under the NDS, the CS was slated to witness a meager increase of 2.3×108 t by 2060. Contrarily, the ECS and EPS anticipated CS increments of 4.5×108 t and 4.7×108 t, respectively. All three scenarios revealed a diminishing trend in carbon sinks over the ensuing four decades, with the NDS experiencing the swiftest decline and the EPS exhibiting a more gradual descent, stabilizing by 2050. The findings suggested that policies centered on ecological conservation could significantly augment carbon sequestration. To sustain these carbon sinks, prospective policies ought to be oriented towards substantial investments in agricultural and grassland land management, coupled with the implementation of progressive afforestation strategies. Additionally, efforts to reduce urban land sprawl and careful planning of development zones are also indispensable measures to reduce the loss of CS. In summation, this study furnishes innovative strategies and policy recommendations, thereby contributing to the discourse on the formulation of optimal land management practices.

Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning

Urban greening plays a crucial role in maintaining environmental sustainability and enhancing people’s well-being. However, limited by the shortcomings of traditional methods, studying the heterogeneity and nonlinearity between environmental factors and green view index (GVI) still faces many challenges. To address the concerns of nonlinearity, spatial heterogeneity, and interpretability, an interpretable spatial machine learning framework incorporating the Geographically Weighted Random Forest (GWRF) model and the SHapley Additive exPlanation (Shap) model is proposed in this paper. In this paper, we combine multi-source big data, such as Baidu Street View data and remote sensing images, and utilize semantic segmentation models and geographic data processing techniques to study the global and local interpretation of the Beijing region with GVI as the key indicator. Our research results show that: (1) Within the Sixth Ring Road of Beijing, GVI shows significant spatial clustering phenomenon and positive correlation linkage, and at the same time exhibits significant spatial differences; (2) Among many environmental variables, the increase of green coverage rate has the most significant positive effect on GVI, while the increase of building density shows a strong negative correlation with GVI; (3) The performance of the GWRF model in predicting GVI is excellent and far exceeds that of comparison models.; (4) Whether it is the green coverage rate, urban built environment or socioeconomic factors, their influence on GVI shows non-linear characteristics and a certain threshold effect. With the help of these non-linear influences and explicit threshold effects, quantitative analyses of greening are provided, which can help to assist urban planners in making more scientific and rational decisions when allocating greening resources.

Strategies in Decision Making in a Multiobjective Context: Integration of DOE, NBI, and CFD in the Optimization of a Centrifugal Fan

Multiobjective optimization problems present significant challenges when attempting to balance conflicting variables simultaneously; furthermore, the creation of an adequate dataset can be a complex task. To address these challenges, an integrated approach is adopted, combining Design of Experiments (DOE) and Normal Boundary Intersection (NBI).DOE is employed to plan experiments in a balanced and economical manner, allowing for valuable information to be obtained with fewer resources. Subsequently, NBI is used to optimize multiple objectives simultaneously, creating an equispaced Pareto surface that defines an efficient boundary.To handle uncertainty in parameter estimates and analyze variable clustering, a confidence ellipse is constructed. This ellipse provides a visual representation of the uncertainty associated with estimates and assists in identifying patterns in the data.Once the efficient boundary with numerous results is obtained, the challenge arises of choosing the best solution among those available. For this purpose, Mahalanobis Distance (MD) is utilized, which are weighted Euclidean distances, taking into account the covariance of variables. This technique aids the decision-maker in selecting the most suitable solution.To demonstrate the effectiveness of integrating these techniques, a problem involving Computational Fluid Dynamics (CFD) is explored, encompassing an industrial centrifugal fan. In this scenario, conflicting variables include mass flow rate at the outlet and required torque, with four control variables: number of blades, blade inlet angle, blade outlet angle, and blade length.After identifying the best possible fan based on the smallest MD, the product is computationally replicated under optimal conditions, and the result is verified.This integrated approach combines techniques from DOE, CFD, and NBI that address all stages, from dataset creation to optimization and confirmation of results obtained, offering a comprehensive methodology for solving complex engineering problems.

Deep learning-based prediction of atmospheric turbulence toward satellite-to-ground laser communication.

Atmospheric turbulence is one of the key factors that affect the stability and performance of satellite-to-ground laser communication (SGLC). Predicting turbulence could provide a decisive strategy for the SGLC system to ensure communication performance and is thus of great significance. In this Letter, we proposed a hybrid multi-step prediction method for atmospheric turbulence. In the proof-of-concept experiment, we collected Fried parameters (representing turbulence strength) along the SGLC link continuously for more than 3 months at the Miyun satellite ground station, near Beijing, China, and then trained the model for prediction. The favorable experimental results illustrate that the proposal can achieve 4-h prediction of turbulence Fried parameter at a resolution of 10 min, with performance increase of 7.54%, evaluated by mean absolute percentage error (MAPE).