Knowledge Base Research Articles

Cost-effective non-revenue water reduction: analysis through pilot activities in Kigali City, Rwanda

ABSTRACT Non-revenue water (NRW) is a major problem for water utilities, and its reduction has become an important challenge in ensuring a sustainable water supply. However, a lack of clarity regarding the cost–benefit analysis of investment in pilot activities to reduce NRW may hinder further investment in such projects. Thus, we attempted to identify strategies for effective NRW reduction and establish a method for estimating their cost-effectiveness. To this end, a project to strengthen the NRW management capacity and build a sustainable NRW reduction system was implemented in Kigali City, Rwanda, between 2016 and 2021. The project included four objectives: enhancement of the planning capacity for NRW reduction; acquisition of basic knowledge, skills, and techniques for NRW control; enhancement of staff capacity to execute NRW reduction measures; and establishment of a system to accurately measure NRW ratios. The project helped to formulate a 5-year plan for NRW reduction and 596 employees of Kigali City were trained for the same. The NRW ratios were reduced by 10 and 12% in the two pilot areas of the city. Cost–benefit analysis revealed the effectiveness of the investment in NRW reduction activities over time.

METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

Respiratory Residency: Establishing Excellence for the Newly Licensed Respiratory Therapist.

Workforce issues have highlighted the fact that newly graduated respiratory therapists (RTs) need more support during their transition to practice; however, there are few data on best practices for RT residency programs. The aim of this project was to evaluate the feasibility and effectiveness of a residency program for licensed RTs with < 1 y of experience post graduation. The approach to improve new graduate transition to practice included a streamlined clinical-based orientation, didactic learning, evidence-based practice plan, and incorporated simulation. Data were collected in a secure electronic database at a multi-campus health care system utilizing anonymous questionnaires. The participants responded to questions about demographic information, overall confidence, clinical skills, and knowledge base topic confidence. Participant responses reported an increase in their overall factor score measures for role confidence, clinical skills, and knowledge-based topics. The collected responses showed an increase in a wide range of clinical skills and other soft skills. These results indicate that a respiratory care residency program adds value to the training, overall well-being, and retention of the newly graduated RT.

Telesurgery a Systematic Literature Review and Future Directions.

To undertake a systematic review of the medical literature on telesurgery, with a key focus on identifying the key technical and non-technical themes searched in medical articles and to analyze gaps in the current knowledge base on telesurgery. It has now been over two decades since the first successful case of telesurgery and since this time there have been significant technological and telecommunications advancements. A systematic review of the literature was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Details of the protocol for this systematic review were registered on PROSPERO (CRD42024511530). In total, 102 unique references which were allocated into five categories; technical, cybersecurity, ethical, financial, regulatory considerations to discuss key themes. The vast majority of references were related to technical considerations which have demonstrated the feasibility of telesurgery. The non-technical considerations have a paucity of literature and a lack of guidance on telesurgery which appears to still be the major barriers to telesurgery. Telesurgery presents many interdisciplinary challenges, encompassing both important technical and non-technical (such as cybersecurity, ethical, financial, and regulatory) considerations. Further research, collaboration between stakeholders, collaborative community of experts, and the development of comprehensive consensus frameworks are essential steps toward the widespread adoption of telesurgery.

Development of Morality and Ethics in Educational Institutions under the Office of the Vocational Education Commission Pathum Thani Province

Background and Aims: Developing a nation to compete with advanced countries requires prioritizing human resource development, with education serving as the foundation of this progress. Quality education establishes a sustainable knowledge base, essential for achieving economic, social, and political growth. This study aims to: (1) evaluate the level of moral and ethical development among staff in vocational institutions governed by the Vocational Education Commission in Pathum Thani Province; (2) compare staff perspectives on moral and ethical growth based on variables such as gender, position, and age; and (3) identify effective strategies to enhance moral and ethical standards within these educational institutions in Pathum Thani Province. Methodology: The sample group for this research included 348 personnel from institutions under the Office of the Vocational Education Commission in Pathum Thani Province during the 2023 academic year, selected via stratified random sampling. Research tools comprised a questionnaire using a 5-point rating scale, which had an Item-Objective Congruence (IOC) value ranging from 0.80 to 1.00, a reliability coefficient of 0.83, and a structured interview. Data analysis utilized statistical methods such as frequency, percentage, mean, and standard deviation, along with t-tests, F-tests, and pairwise comparisons using Scheffe’s method. Results: The research results indicated that: (1) The overall level of morality and ethics development in educational institutions under the Office of the Vocational Education Commission in Pathum Thani Province was rated high across all dimensions, with consistently high ratings for each specific aspect. (2) Comparative analysis revealed significant differences in morality and ethics development based on gender and position at the 0.05 significance level, but no significant differences were found based on age. (3) Guidelines for enhancing morality and ethics emphasized systematic professional oversight, recognition of outstanding personnel, and structured curricula for moral and ethical growth. The Office of the Vocational Education Commission highlighted merit-based assessments grounded in moral principles, clear standards, and well-defined workloads. Additionally, educational administrators demonstrated effective leadership by modeling rational behavior, prioritizing reason over emotion.

Creating a biomedical knowledge base by addressing GPT inaccurate responses and benchmarking context.

We created GNQA, a generative pre-trained transformer (GPT) knowledge base driven by a performant retrieval augmented generation (RAG) with a focus on aging, dementia, Alzheimer's and diabetes. We uploaded a corpus of three thousand peer reviewed publications on these topics into the RAG. To address concerns about inaccurate responses and GPT 'hallucinations', we implemented a context provenance tracking mechanism that enables researchers to validate responses against the original material and to get references to the original papers. To assess the effectiveness of contextual information we collected evaluations and feedback from both domain expert users and 'citizen scientists' on the relevance of GPT responses. A key innovation of our study is automated evaluation by way of a RAG assessment system (RAGAS). RAGAS combines human expert assessment with AI-driven evaluation to measure the effectiveness of RAG systems. When evaluating the responses to their questions, human respondents give a "thumbs-up" 76% of the time. Meanwhile, RAGAS scores 90% on answer relevance on questions posed by experts. And when GPT-generates questions, RAGAS scores 74% on answer relevance. With RAGAS we created a benchmark that can be used to continuously assess the performance of our knowledge base. Full GNQA functionality is embedded in the free GeneNetwork.org web service, an open-source system containing over 25 years of experimental data on model organisms and human. The code developed for this study is published under a free and open-source software license at https://git.genenetwork.org/gn-ai/tree/README.md .

Knowledge of Cerebral Visual Impairment (CVI) in Children among Eye Care Professionals in Selangor and Kuala Lumpur, Malaysia

Objective: Cerebral Visual Impairment (CVI), a brain-based visual disorder that affects neural networks that process incoming visual information throughout the brain, poses challenges in diagnosis and managing visual impairment in children. With the prevalence rise in CVI cases among children, there is a growing demand for ophthalmologists and optometrists to address these conditions, emphasising the importance of improving knowledge and awareness for better visual rehabilitation. This study intends to quantify the level of understanding of CVI in children among eye care professionals in Selangor and Kuala Lumpur, Malaysia.Material and Methods: Two hundred thirty registered eye care professionals, including ophthalmologists and optometrists across Selangor and Kuala Lumpur, Malaysia, participated in this cross-sectional web-based survey. A standardised questionnaire was created through Google Forms and distributed via social media platforms between April and June 2022.Results: The finding showed that the eye care professionals have significantly basic knowledge regarding CVI in children (x2=369; df=2; p-value≤0.01). Most respondents correctly identified variable loss of vision, visual field defects, and abnormal behaviour as clinical features of CVI in children. Additionally, over half of the respondents selected magnetic resonance imaging (MRI) as the appropriate investigation choice.Conclusion: These findings indicate that eye care professionals have basic knowledge and awareness of CVI in children. More research is needed to enhance knowledge and raise awareness about CVI in children among eye-care professionals.

Design and Implementation of a Geographic Scenario Computation System Based on Knowledge Base

Abstract. The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.

Evolution of the “4-D Approach” to Dynamic Vision for Vehicles

Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its advantage is that only the last image of the sequence needs to be analyzed in detail to allow the full state vectors of moving objects, including their velocity components, to be reconstructed by the feedback of prediction errors. The vehicle carrying the cameras can, thus, together with conventional measurements, directly create a visualization of the situation encountered. In 1994, at the final demonstration of the project PROMETHEUS, two sedan vehicles using this approach were the only ones worldwide capable of driving autonomously in standard heavy traffic on three-lane Autoroutes near Paris at speeds up to 130 km/h (convoy driving, lane changes, passing). Up to ten vehicles nearby could be perceived. In this paper, the three-layer architecture of the perception system is reviewed. At the end of the 1990s, the system evolved from mere recognition of objects in motion, to understanding complex dynamic scenes by developing behavioral capabilities, like fast saccadic changes in the gaze direction for flexible concentration on objects of interest. By analyzing motion of objects over time, the situation for decision making was assessed. In the third-generation system “EMS-vision” behavioral capabilities of agents were represented on an abstract level for characterizing their potential behaviors. These maneuvers form an additional knowledge base. The system has proven capable of driving in networks of minor roads, including off-road sections, with avoidance of negative obstacles (ditches). Results are shown for road vehicle guidance. Potential transitions to a robot mind and to the now-favored CNN are touched on.

CardioGraph: a platform to study variations associated with familiar cardiopathies

BackgroundFamiliar cardiopathies are genetic disorders that affect the heart. Cardiologists face a significant problem when treating patients suffering from these disorders: most DNA variations are novel (i.e., they have not been classified before). To facilitate the analysis of novel variations, we present CardioGraph, a platform specially designed to support the analysis of novel variations and help determine whether they are relevant for diagnosis. To do this, CardioGraph identifies and annotates the consequence of variations and provides contextual information regarding which heart structures, pathways, and biological processes are potentially affected by those variations.MethodsWe conducted our work through three steps. First, we define a data model to support the representation of the heterogeneous information. Second, we instantiate this data model to integrate and represent all the genomics knowledge available for familiar cardiopathies. In this step, we consider genomic data sources and the scientific literature. Third, the design and implementation of the CardioGraph platform. A three-tier structure was used: the database, the backend, and the frontend.ResultsThree main results were obtained: the data model, the knowledge base generated with the instantiation of the data model, and the platform itself. The platform code has been included as supplemental material in this manuscript. Besides, an instance is publicly available in the following link: https://genomics-hub.pros.dsic.upv.es:3090.ConclusionCardioGraph is a platform that supports the analysis of novel variations. Future work will expand the body of knowledge about familiar cardiopathies and include new information about hotspots, functional studies, and previously reported variations.

The role of eco-anxiety in the presentation of bulimia nervosa: a case report

BackgroundDespite a growing literature demonstrating the significant impacts of climate change on mental health, research is urgently needed to investigate how climate change-related concerns may contribute to the development, exacerbation, or re-emergence of eating disorders, as well as affect the effectiveness of existing interventions. This case report contributes to this scant knowledge base by offering empirical evidence for how responses to climate change can influence eating disorder symptoms and, importantly, limit the effectiveness of evidence-based treatments such as Cognitive Behavior Therapy-Enhanced (CBT-E).Case presentationA 24-year-old female graduate student studying environmental science presented to a specialized eating disorder clinic with worsening bulimia nervosa. Her symptoms initially improved with CBT-E; however, after three months, concerns about food waste significantly impeded further progress. The therapist, identifying symptoms of eco-anxiety, adapted standard CBT-E strategies to include psychoeducation about eco-anxiety, cognitive restructuring of beliefs about food waste and other eating-related eco-concerns, relevant exposures related to such concerns, and problem-solving to increase social support. These adaptations led to resumed progress, with the patient achieving nutritional adequacy by treatment end (38 sessions) and maintaining treatment gains through one year follow-up.ConclusionsTo our awareness, this is the first case report on the co-occurrence of eco-anxiety and eating disorders. This case underscores the importance of screening for concurrent eco-anxiety, suggests ways in which eating disorders and eco-anxiety can influence one another longitudinally, describes how coexisting eco-anxiety can limit standard CBT-E’s effectiveness, and provides examples of successful treatment adaptations tailored to address eco-anxiety-related concerns.

Knowledge Base Embeddings for a Recommendation Based on Overlapping Knowledge and Graph Learning

Knowledge Base Embeddings for a Recommendation Based on Overlapping Knowledge and Graph Learning

GENERALIZED PASCAL’S PYRAMIDS AND COMBINATORIAL INFORMATION RETRIEVAL PROBLEMS

The work studies combinatorial objects of pyramidal structure. It proposes fundamental relations for generalizations of Pascal’s triangle and pyramid. It considers problems of finding new ways of constructing information relevance indices and offers a way of constructing such an index with the help of Pascal’s generalized pyramids. Using structures of a type of Pascal’s generalized pyramids with weight factors allow construction of an index that reflects a degree of relevance of keywords, as well as makes it possible to allocate subsets of keywords with specific properties. The offered methods of the combinatorial analysis of hierarchical structures in information retrieval problems can be used in creating and analyzing knowledge databases.

Comparative Study on the Applicability of Solid Waste Treatment by Fixed Grate and Fluidized Bed Incinerators: A Bibliometric Review

Using thermal treatment in the waste management process has advantages such as reducing volume and the possibility of energy recovery. There are several incineration technologies with different characteristics and potential. Incineration, however, requires efficient environmental control to reduce associated risks, such as the emission of harmful compounds. The paper aims to compare solid waste treatment processes in grate and fluidized bed incinerators based on technical and environmental characteristics. For this purpose, a bibliometric review was conducted, and consulting works available in the scientific literature describe the waste treatment process with these two technologies. The searches were carried out in the Web of Science and Web of Knowledge databases using the following search engines: “fixed grade incinerator,” “grate incinerator” and “fluidized bed incinerator”. Grate incinerators present a range of elective waste for treatment compared to fluidized bed incinerators, as they do not require homogeneous waste. The study compared solid waste incineration in fixed grate and fluidized bed furnaces. While promising for sludge treatment, the fluidized bed technology may require pre-treatment, increasing costs and limiting its use to larger facilities. Fixed grate furnaces can handle a wider variety of waste without this additional step but generate more halogenated compounds when plastic waste is present. These technologies were primarily studied in Asia and Europe, where landfill space is limited and more significant potential for energy recovery exists. Adopting these technologies in other regions depends on waste characteristics, economic conditions, and environmental impacts.

The Effect Of Financial Literacy On Students' Behavior In Managing Finance (Case Study Of Students In Bandar Lampung)

This study aims to examine the effect of the importance of financial literacy in terms of basic financial knowledge, financial attitudes, and financial behavior on student behavior in managing finances. The sample in this study were all students in Bandar Lampung with a total sample of 100 respondents from various universities in Bandar Lampung. Sampling using random sampling method. This type of research is quantitative using primary data and measurement using a Likert scale. The data analysis technique used is Multiple Linear Regression. The results showed that 1). Basic Knowledge of Finance has a negative and insignificant effect on Financial Management Behavior. 2). Financial attitude has a negative and insignificant effect on Financial Management Behavior. 3). Financial Behavior Has a Positive and Significant Effect on Financial Management Behavior.

INCREASING STUDENTS’ EMPATHY LEVELS IN SCIENCE EDUCATION: A THEMATIC REVIEW

K-12 education as an enterprise always has two main aims; to teach basic skills and knowledge and to cultivate moral citizenry. There is substantial evidence that empathy can promote morality and character development through education. To shed light on what has been done and to identify the pathways already established, a thematic review of research focused on empathy development in K-12 science education has been conducted. A systematic examination of literature published before January 2024 resulted in 28 publications being analyzed in terms of their general characteristics and the specifics of the designed interventions. Results show that educational interventions can improve empathy skills; these studies use collaborative learning environments, take 3-10 weeks to implement, and the most preferred strategy is recognizing different perspectives of a given situation with reflective exercises. All of the studies reviewed positively affected students' affective and cognitive domains. For future research, more interventions designed to improve empathy skills in parallel with the science curriculum are proposed to supply teachers with new comprehensive resources to implement. Keywords: empathy development, empathy skills, science education, thematic review

Методологическое обоснование содержания, структуры и программной реализации базы геопространственных знаний территориальных образований

The article deals with substantiating the contents, structure and software implementation of the geospatial knowledge base. The importance and relevance of forming its infrastructure and the corresponding informational base on the territory in modern geoinformatics and cartography are noted. The requirements for the contents, formal and logical representation of the mentioned base, its structure and connections of the elements at the logical level are given. The authors consider the software implementation of the processes of searching and issuing geospatial knowledge in a visualized form at the request of untrained users in the field of geoinformatics and cartography. The requisitions and information on the special original software, which allows observing the contents of the geospatial knowledge base, are shown. The effectiveness of visualizing the geospatial knowledge base through interactive configuration of software elements is substantiated, based on the metadata principle proposed by the authors, two formalisms and an algorithm for creating corresponding services for geospatial knowledge and other related data. These formalisms of the client’s parameters and server software applications are described, a variant of the geospatial knowledge base structure is given, and the requirements for the necessary software for the effective solution of the task are formulated. The authors` solution is considered in the form of a complex of multicomponent software, used at working with geodata and creating their own interactive maps by poorly trained non-professional users

Prozessüberwachung durch bildverarbeitende KI

Abstract The proceeding automation of industrial manufacturing processes has reduced human presence on modern shop floors. Since industrial processes are still afflicted with malfunctioning, autonomous monitoring solutions are demanded. This article presents a forming use case in which AI-based image recognition is used to monitor process behaviour and prompt stops when needed. The knowledge base utilized by the AI is obtained by examining descriptive examples of abnormal process behaviour.

Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis

Solar cell research aims to improve power conversion efficiency (PCE). This field has an extensive body of literature on the Web of Science. For researchers, it is impossible to understand the development of the entire field comprehensively through traditional reading methods. Knowledge is recorded in the literature by text and numbers. Researchers acquire knowledge through literature surveying, text reading, and thinking. The conversion from text and numbers to knowledge can be automatically completed by machines, which can avoid path‐dependent perspectives. In this work, an intelligent machine learning method for literature structure delineation and information extraction is proposed. As an example, a knowledge base of organic solar cells (OSCs) is extracted including topic analysis of literature, numerical characteristics of performance, and material information. Seven major research directions of OSCs are identified. The correlations between key performance parameters, including PCE, short‐circuit current density (JSC), open‐circuit voltage (VOC), and fill factor (FF), are revealed from text mining. A donor–acceptor material map of PCE is constructed which provides a road map for OSCs, indicating the bottleneck of this field. Moreover, the method of machine intelligence developed here can be used in any other materials field, aiding a comprehensive understanding of the development quickly.

RDSSMM: A FUZZY EXPERT SYSTEM FOR ENHANCED MALARIA DIAGNOSIS AND MANAGEMENT IN ENDEMIC REGIONS

Malaria continues to be a pressing public health issue in tropical regions, with its deleterious effects on human health well documented. While a minority of cases may be life threatening, delayed diagnosis can significantly worsen the severity of malaria. This paper presents the Recommender and Decision Support System for Malaria Management (RDSSMM), designed to assist researchers, physicians and healthcare professionals in malaria endemic areas. RDSSMM is made up of four main components: a knowledge base, fuzzification module, inference engine, and defuzzification module. Mayo Hospital Lahore, Pakistan was used to develop the fuzzy expert system based on experts’ opinions in the field of medicine. For ease of access, a mobile application has been included which allows patients to get primary diagnosis by keying in their physical signs that are then transformed into fuzzy membership functions. The data was collected from the outpatient department of Mayo Hospital Lahore as one way of evaluating RDSSMM’s effectiveness. Comparing its predictions to expert medical diagnoses and reports pointed out this system’s performance. Malaria risk was correctly predicted 80% of the time by RDSSMM. These findings show that RDSSMM can greatly improve malaria management in rural areas through provision of quick and accurate results, which would facilitate timely decisions regarding malaria testing and monitoring.