Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

The Economics of Competing Water Uses under a FERC Licensing Agreement: Estimation of Property Value, Recreation, and Hydroelectric Impacts

Reservoirs provide a variety of services with economic values across multiple sectors. As demands for reservoir services continue to grow and precipitation patterns evolve, it becomes ever more important to consider the integrated suite of values and tradeoffs that attend changes in water uses and availability. Section 316 (b) of the Clean Water Act requires that owners of certain water cooled power plants evaluate technologies and operational measures that can reduce their impacts to aquatic organisms. The studies must discuss the social costs and benefits of alternative technologies including cooling towers (79 Fed. Reg. 158, 48300 - 48439). Cooling towers achieve their effect through evaporation. This manuscript estimates the property value, recreation, and hydroelectric generation impacts that could result from the evaporative water loss associated with installing cooling towers at the McGuire Nuclear Generating Station (McGuire) located on Lake Norman, North Carolina. Although this study specifically evaluates the effects of evaporative water loss from cooling towers, its methods are applicable to estimating the economic benefits and costs of a new water user or reduced water input in any complex reservoir system that supports steam electric generation, hydroelectric generation, residential properties, recreation, irrigation, and municipal water use.

Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

A novel fracture-cavity reservoir outcrop geological knowledge base construction method considering parameter collection and processing,mutual transformation of data-knowledge,application and update

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base(GKB)tailored to fracture-cavity reservoir outcrops within the North Tarim Basin.The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations,thin section studies,unmanned aerial vehicle scanning,and high-resolution cameras.Subsequently,a three-dimensional digital outcrop model was generated,and the parameters were standardized.An assessment of traditional geological knowledge was conducted to delineate the knowledge framework,content,and system of the GKB.The basic parameter knowledge was extracted using multiscale fine characterization techniques,including core statistics,field observations,and microscopic thin section analysis.Key mechanism knowledge was identified by integrating trace elements from filling,isotope geochemical tests,and water-rock simulation experiments.Significant representational knowledge was then extracted by employing various methods such as multiple linear regression,neural network technology,and discriminant classification.Subsequently,an analogy study was performed on the karst fracture-cavity system(KFCS)in both outcrop and underground reservoir settings.The results underscored several key findings:(1)Utilization of a diverse range of techniques,including outcrop observations,core statistics,unmanned aerial vehicle scanning,high-resolution cameras,thin section analysis,and electron scanning imaging,enabled the acquisition and standardization of data.This facilitated effective management and integration of geological parameter data from multiple sources and scales.(2)The GKB for fracture-cavity reservoir outcrops,encompassing basic parameter knowledge,key mechanism knowledge,and significant representational knowledge,provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs.(3)The developmental characteristics of fracturecavities in karst outcrops offer effective,efficient,and accurate guidance for fracture-cavity research in underground karst reservoirs.The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide.

A deep learning method based on prior knowledge with dual training for solving FPK equation

The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macroscopic variables in the stochastic dynamic system. Traditional methods for solving these equations often struggle with computational efficiency and scalability, particularly in high-dimensional contexts. To address these challenges, this paper proposes a novel deep learning method based on prior knowledge with dual training to solve the stationary FPK equations. Initially, the neural network is pre-trained through the prior knowledge obtained by Monte Carlo simulation(MCS). Subsequently, the second training phase incorporates the FPK differential operator into the loss function, while a supervisory term consisting of local maximum points is specifically included to mitigate the generation of zero solutions. This dual-training strategy not only expedites convergence but also enhances computational efficiency, making the method well-suited for high-dimensional systems. Numerical examples, including two different two-dimensional(2D), six-dimensional(6D), and eight-dimensional(8D) systems, are conducted to assess the efficacy of the proposed method. The results demonstrate robust performance in terms of both computational speed and accuracy for solving FPK equations in the first three systems. While the method is also applicable to high-dimensional systems, such as 8D, it should be noted that computational efficiency may be marginally compromised due to data volume constraints.

Recreational Behavior and Spatial Characteristics of Urban Parks Based on Multi-Source Information: A Case Study of Xiaohong Stone Carving Park in Nanjing

Urban parks are an important part of urban recreational spaces,and the development of analysis framework of recreational behavior and space is helpful for the design of urban parks to better meet the activity and usage requirements of visitors.In this paper,Xiaohong Stone Carving Park in Nanjing was taken as the research object to explore the relationship between landscape space and user behavior for design feedback.The orthographic image technology of unmanned aerial vehicle(UAV)and machine deep learning were used to analyze users behavior activities in the park,and the spatial characteristics of each node in the park were evaluated by semantic differential scale to explore the potential causes of behavior activities.The results showed that①there were significant differences in the number of visitors on holidays and working days,and young people and children were the main users.②The users activity of the park differed greatly on the inner and outer tour roads,and the users showed stronger willingness to stay on the inner tour roads.Recreational activities received more attention than cultural ones.③The overall distribution of users was significantly affected by hydrophilicity,and the recreational behavior and activity type were affected by multiple factors which include the hydrophilicity,safety,accessibility of cultural relics and the amount of recreational facilities.Finally,the enhanced strategies such as improving the cultural tour system,establishing hydrophilic safety belts and optimizing recreational facilities were put forward.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Community Knowledge about Bleeding Control in Jazan, KSA: Is There a Need for First Aid Educational Programs

The current study aimed to evaluate the first aid knowledge and general awareness of bleeding control, and their relations with different variables among the population of Jazan City, Saudi Arabia, in 2023. A cross-sectional study was conducted in Jazan City, Saudi Arabia between April 2023 and May 2023. Participants, >13 years old, living in Jazan City, were self-enrolled. Data collection was carried out by distributing a self-reported online survey questionnaire via email and social media apps. A validated, pretested online self-report questionnaire was used for data collection, with data analysis performed using (MS) Excel 2022 and SPSS version 26. A Chi-square test was used to determine the association of sociodemographic variables and Bleeding Control (BC) knowledge with, significance set at p < 0.05. A total of 250 participants completed the questionnaire, predominantly aged between 16 - 25 years, with 152 (60.8%) being male, and about 90% being Saudi citizens. Only 53 (21.2%) participated in previous first aid training concentrating on bleeding control. Only 76 (30.4%) of participants had good knowledge, while 131 (52.4%) exhibited positive attitude towards BC first aid. There were no statistically significant associations between BC knowledge and age, gender, occupation, nationality, and education. However, a significant association was observed between previous BC training and knowledge (Chi-test = 40.373, d.f = 1, p = 0.000) at p < 0.05. Conclusion: The prevalence of poor knowledge of bleeding control among community members in Jazan City was high. The findings of this study should be carefully considered by various healthcare organizations to implement educational first-aid programs and activities aimed at enhancing community awareness and knowledge of bleeding control.

Evaluation of Medical Prescribers’ Theoretical Knowledge on Medical Imaging in the Northern Region of Burkina Faso

Introduction: Medical imaging is a medical specialty that involves producing images of the human body and interpreting them for diagnostic, therapeutic purposes, and for monitoring the progress of pathologies. We aimed to assess the theoretical knowledge of doctors and interns in medical imaging in the northern region of Burkina Faso. Methodology: This was a descriptive cross-sectional survey based on a self-administered questionnaire. Prescribers knowledge was estimated based on scores derived from questionnaire responses. Results: We collected 106 questionnaires out of 163, i.e. a participation rate of 65.03%. The average knowledge score was 81.71% for the contribution of medical imaging to patient management. It was 60.02% for the indications/counter-indications of radiological examinations and 72.56% for the risks associated with exposure to radiation during these examinations. The score was 59.83% for the methods used to select the appropriate radiological examination. As regards the completeness of the clinical and biological information on the forms requesting imaging examinations, the score was 96.65%. Specialist doctors had the highest overall level of knowledge (74.68%). Conclusion: Improved technical facilities, good initial and in-service training, and interdisciplinary collaboration will help to ensure that imaging tests are properly prescribed, leading to better patient care.

Parental Knowledge of Pre-Hospital Management of Avulsed Permanent Tooth in Children at Kath

BACKGROUND: Permanent tooth avulsion is one of the severe forms of dental traumatic injuries. The immediate action taken at the site of the accident is crucial to the prognosis of the tooth. Replantation is considered as the treatment of choice. OBJECTIVE: The study was undertaken to assess the knowledge of parents who accompany their children to the pediatric dental clinic, KATH on the pre-hospital management of avulsed permanent tooth in children. METHODS: A researcher administered structured questionnaire was used to collect data on the knowledge of pre-hospital management of avulsed permanent tooth from 83 parents who accompanied their wards to the pediatric dental clinic at KATH. RESULTS: A total of 83 parents were involved in the study. 30 (36%) were males while 53 (64%) were females. The majority of the parents (57%) were either university trained or had attended college of education. Only 32 parents (39%) were aware of the possibility of replantation. Majority of the parents chose non-physiologic media as the transport media of choice and only 10% would attempt self-replantation before seeking professional help. 76 parents (92%) had no previous education on pre-hospital management of avulsed tooth. CONCLUSION: The results obtained from this study indicate that parental knowledge on pre-hospital management of avulsed permanent tooth is low hence the need for massive public educational campaigns.

The Impact of Optimizing Details in the Operating Room on the Level of Knowledge, Attitude, and Practice of Hospital Infection Prevention and Control by Surgeons, as Well as the Effectiveness of Infection Control

Objective: This paper aims to explore the impact of optimizing details in the operating room on the level of knowledge, attitude, and practice of hospital infection prevention and control by surgeons, as well as the effectiveness of infection control. Methods: From January 2022 to June 2023, a total of 120 patients were screened and randomly divided into a control group (routine care and hospital infection management) and a study group (optimizing details in the operating room). Results: Significant differences were found between the two groups in the data of surgeons’ level of knowledge, attitude, and practice in hospital infection prevention and control, infection rates, and nursing satisfaction, with the study group showing better results (P Conclusion: The use of optimizing details in the operating room among surgeons can effectively improve surgeons’ level of knowledge, attitude, and practice in hospital infection prevention and control, reduce infection occurrence, and is worth promoting.

Two-Stage Approach for Targeted Knowledge Transfer in Self-Knowledge Distillation

Knowledge distillation(KD) enhances student network generalization by transferring dark knowledge from a complex teacher network. To optimize computational expenditure and memory utilization, self-knowledge distillation(SKD) extracts dark knowledge from the model itself rather than an external teacher network. However, previous SKD methods performed distillation indiscriminately on full datasets, overlooking the analysis of representative samples. In this work, we present a novel two-stage approach to providing targeted knowledge on specific samples, named two-stage approach self-knowledge distillation(TOAST). We first soften the hard targets using class medoids generated based on logit vectors per class. Then, we iteratively distill the under-trained data with past predictions of half the batch size. The two-stage knowledge is linearly combined, efficiently enhancing model performance. Extensive experiments conducted on five backbone architectures show our method is model-agnostic and achieves the best generalization performance.Besides, TOAST is strongly compatible with existing augmentation-based regularization methods. Our method also obtains a speedup of up to 2.95x compared with a recent state-of-the-art method.

Community Knowledge and Attitude about Burns First Aid (BFA) in Jazan, Saudi Arabia

Introduction and Significance: Burn injury (BI) is a considerable health issue which is responsible for around 300,000 deaths and affecting about 11 million people every year worldwide. In Saudi Arabia, the prevalence of BIs array from 112 to 518 per 100,000 per year. The appropriate awareness of performing first aid could facilitate to improve the outcomes of burns. Purpose and Objectives: To appraise the community that acknowledges burns, first aid, and associated factors among the community population in Jazan City, Saudi Arabia. The paper aims to identify limitations to encourage additional research and persuade legislators to develop improved burn-injury care recommendations and training programs. Materials and Methods: An observational-based sample survey was conducted among the people who live in Jazan City aging 13 years or more, during April 5 to May 5, 2023. Data collection was done by a validated online self-administrated questionnaire sent randomly to community members in different parts of Jazan City via social media platforms. Collected data were coded and cleaned by an excel program, and finally exported on SPSS 26.0 software. The variables were analyzed using descriptive statistics like frequencies and percentages. Also, the Chi-square test was used to investigate the relation between different variables, with a significance value of P Results: This study included 243 participants (about 62%) among them were mostly male participants (151) having a university degree. The majority of participants 75% did not take any form of BFA training in the past. This study shows that 69.9% of the participants have inadequate awareness, despite 72% having a constructive attitude towards burn first aid. Previous burn-related first aid training was significantly associated with participants’ knowledge of BFA at a p-value less than 0.05. Conclusion: This study indicates a high frequency of Jazan population having inadequate knowledge of burn first aid despite the high prevalence of a favorable attitude. There is a need to develop an effective nationwide burn prevention program and early burn first aid treatment in Saudi Arabia and promote a consistent guideline for burn first aid.

Research fronts and researchers of World Journal of Psychiatry in 2023: A visualization and analysis of mapping knowledge domains

BACKGROUND In the rapidly evolving landscape of psychiatric research,2023 marked another year of significant progress globally,with the World Journal of Psychiatry(WJP)experiencing notable expansion and influence.AIM To conduct a comprehensive visualization and analysis of the articles published in the WJP throughout 2023.By delving into these publications,the aim is to deter-mine the valuable insights that can illuminate pathways for future research endeavors in the field of psychiatry.METHODS A selection process led to the inclusion of 107 papers from the WJP published in 2023,forming the dataset for the analysis.Employing advanced visualization techniques,this study mapped the knowledge domains represented in these papers.RESULTS The findings revealed a prevalent focus on key topics such as depression,mental health,anxiety,schizophrenia,and the impact of coronavirus disease 2019.Additionally,through keyword clustering,it became evident that these papers were predominantly focused on exploring mental health disorders,depression,anxiety,schizophrenia,and related factors.Noteworthy contributions hailed authors in regions such as China,the United Kingdom,United States,and Turkey.Particularly,the paper garnered the highest number of citations,while the American Psychiatric Association was the most cited reference.CONCLUSION It is recommended that the WJP continue in its efforts to enhance the quality of papers published in the field of psychiatry.Additionally,there is a pressing need to delve into the potential applications of digital interventions and artificial intelligence within the discipline.

Potentials and Challenges of Carbon Knowledge Graph in Sustainable Textile Production for Carbon Traceability:A Review

Textile production has received considerable attention owing to its significance in production value,the complexity of its manufacturing processes and the extensive reach of its supply chains.However,textile industry consumes substantial energy and materials and emits greenhouse gases that severely harm the environment.In addressing this challenge,the concept of sustainable production offers crucial guidance for the sustainable development of the textile industry.Low-carbon manufacturing technologies provide robust technical support for the textile industry to transition to a low-carbon model by optimizing production processes,enhancing energy efficiency and minimizing material waste.Consequently,low-carbon manufacturing technologies have gradually been implemented in sustainable textile production scenarios.However,while research on low-carbon manufacturing technologies for textile production has advanced,these studies predominantly concentrate on theoretical methods,with relatively limited exploration of practical applications.To address this gap,a thorough overview of carbon emission management methods and tools in textile production,as well as the characteristics and influencing factors of carbon emissions in key textile manufacturing processes is presented to identify common issues.Additionally,two new concepts,carbon knowledge graph and carbon traceability,are introduced,offering strategic recommendations and application directions for the low-carbon development of sustainable textile production.Beginning with seven key aspects of sustainable textile production,the characteristics of carbon emissions and their influencing factors in key textile manufacturing process are systematically summarized.The aim is to provide guidance and optimization strategies for future emission reduction efforts by exploring the carbon emission situations and influencing factors at each stage.Furthermore,the potential and challenges of carbon knowledge graph technology are summarized in achieving carbon traceability,and several research ideas and suggestions are proposed.

Effects of Health Education with Problem-Based Learning Approaches on the Knowledge, Attitude, Practice and Coping Skills of Women with High-Risk Pregnancies in Plateau Areas

Objective: Given the unique cultural background, way of life, and physical environment of the Tibetan Plateau, this study aims to investigate the effects of health education using problem-based learning (PBL) approaches on the knowledge, attitude, practice, and coping skills of women with high-risk pregnancies in this region. Methods: 76 high-risk pregnancy cases were enrolled at Tibet’s Linzhi People’s Hospital between September 2023 and April 2024. 30 patients admitted between September 2023 and December 2023 were selected as the control group and were performed with regular patient education. 46 patients admitted between January 2024 and April 2024 were selected as the observation group and were performed regular patient education with problem-based learning approaches. Two groups’ performance on their health knowledge, attitude, practice and coping skills before and after interventions were evaluated, and patient satisfaction were measured at the end of the study. Results: There was no statistical significance (P P P Conclusions: Health education with problem-based learning approaches is worth promoting as it can help high-risk pregnant women in plateau areas develop better health knowledge, attitude and practice and healthier coping skills. Also, it can improve patient sanctification.

Farmers’ Knowledge and Awareness towards the Use of Agrochemicals in Vegetable Cultivation, Fako Cameroon

Agrochemicals are contemporary, omnipresent tool used in vegetable cultivation. Farmers’ knowledge and awareness of the proper usage of agrochemicals are critical for mitigating the negative effects on human health. This cross-sectional study was aimed at assessing the usage knowledge, risk awareness of toxicological and chemical classes, proper handling and use practices for agrochemicals homologated for use in vegetable farming, and the occurrence of health-related symptoms as a result of exposure among these farmers. The study included 93 vegetable growers from agricultural hotspot towns in Fako, southwest Cameroon. The field study, ran from November 2021 to December 2023, using a questionnaire to collect information on farmers demographic, and their knowledge of pesticide classes, and the related risk of associated with the handling of agrochemicals. Results show that all vegetable farmers, particularly those engaged in agribusiness, employ pesticide inputs to maximize production. Six pesticides, two fertilizer types, and one unknown substance were identified. While 23 active compounds were found, the most utilized were abamectin, emamectin (10.46%), dimethoate (9.30%,) and ethoprophos (8.13%). Two active chemicals, dimethoate and methalaxyl, are illegal yet remain in circulation. Toxicological classes I and II, with the greatest harmful effect on human health, were the most commonly utilized (64.27%). Thirty-nine percent of farmers never use personal protection equipment when working with agrochemicals, demonstrating a significant gap in knowledge and awareness of agrochemicals and their various applications and handling procedures in the field. The government should implement an intensive specialized educational program for on-field farmers with incentives in order to promote sustainable agriculture methods that ensure environmental and human safety.

Analysis of The Use of Implicit Nursing Knowledge in The Teaching of Cardiovascular Internal Medicine Nursing

Objective: To explore the utilization of implicit nursing knowledge in the teaching of cardiovascular internal medicine nursing and to provide a reference for improving the quality and efficiency of cardiovascular internal medicine nursing work. Methods: Thirty-six trainee nurses working in the cardiovascular internal medicine department of our hospital from September 2022 to September 2023 were selected and randomly divided into a control group and an observation group of 18 trainees each. The control adopted the traditional teaching methods while the observation group adopted the implicit nursing knowledge in their clinical practice work. The assessment scores and teamwork ability of the two groups were analyzed and compared. Results: The performance of the observation group was better than that of the control group, and the difference between the two groups was statistically significant (P < 0.05). The teamwork ability of the observation group was significantly better than that of the control group in teamwork ability (P < 0.05). Conclusion: Implicit nursing knowledge teaching is conducive to the cultivation of high-quality nursing talents and meets the development needs of hospitals. Therefore, the importance of implicit nursing knowledge should be strengthened in the teaching of cardiovascular internal medicine nursing and it should be comprehensively organized to improve the quality of nursing services.

Integration Mechanism of Ideological and Political Education with Specialized Knowledge in the Course of Green Building Design

This paper aims to explore the integration of ideological and political education with specialized knowledge in the course of green building design.Through an analysis of the green building design course,combined with the current social development background and the need for ecological and environmental protection,the study investigates how to effectively integrate ideological and political education elements into course instruction to enhance the comprehensive quality and green design capabilities of architecture students.The paper analyzes the content and characteristics of the green building design course and proposes an integration mechanism for ideological and political education in green building design teaching,aiming to cultivate architecture talents with green design concepts and good ideological and political qualities.

Heterogeneous Image Knowledge Driven Visual Perception

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous images.Facing the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.