The term ‘intelligence’ as used in this paper refers to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world (...) actors and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts. Of particular importance to the Cognitive Process Ontology is the class Representation that is Warranted. Such a representation is descriptive in nature and deserving of trust in its veridicality. The latter is because a Representation that is Warranted is always produced by a process that was vetted (or successfully designed) to reliably produce veridical representations. As such, Representations that are Warranted are what in other contexts we might refer to as ‘items of knowledge’. (shrink)

Water is the source of life for our planet, guided the ancient civilizations, and formed its current footprint on the earth. Water has always been a crucial element of our biological survival; consequently, humankind has permanently settled around it while carrying the responsibility of protecting it. To understand the water pattern in various cities throughout history and analyze how the emerging problems were overcome, Istanbul Technical University Landscape Architecture Department Graduate Level Design Studio was held under the theme of "Around (...) Water". Despite the adverse effects of the Covid-19 pandemic on education, international researchers contribute to the studio in a beneficial and diversified manner with the effective use of online tools. As a result of the literature review and the online, multidisciplinary education, and research-based design requirements, a new studio method was developed. Water-based case studies worldwide produced enriched outputs. While creating new discussion environments, the diversified outcomes of the studio "Around Water" contributed to the creation of cumulative studio knowledge. (shrink)

Community-based participatory research (CBPR) and community engaged research (CEnR) are key to promoting community and patient engagement in actionable evidence-based strategies to improve research for health equity. Rapid growth of CBPR/CEnR research projects have led to the broad adoption of partnering principles in community-academic partnerships and among some health and academic organizations. Yet, transformation of principles into best practices that foster trust, shared power, and equity outcomes still remain fragmented, are dependent on individuals (...) with long term projects, or are non-existent. This paper describes how we designed our Engage for Equity PLUS intervention that leverages the leadership and membership of champion teams (including community-engaged faculty, community partners and patient advocates) to improve organizational policies and practices to support equity based CBPR/CEnR. (shrink)

Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a (...) knowledge- based system using CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

Outcomes research in healthcare has been a topic much addressed in recent years. Efforts in this direction have been supplemented by work in the areas of guidelines for clinical practice and computer-interpretable workflow and careflow models.In what follows we present the outlines of a framework for understanding the relations between organizations, guidelines, individual patients and patient-related functions. The derived framework provides a means to extract the knowledge contained in the guideline text at different granularities, in ways that can (...) help us to assign tasks within the healthcare organization and to assess clinical performance in realizing the guideline. It does this in a way that preserves the flexibility of the organization in the adoption of the guidelines. (shrink)

Understanding quantitative research instrumentation is critical for advancing educational research, both theory and practice since it contributes to the accuracy and credibility of research findings (Creswell & Plano Clark, 2017; DeVellis, 2017; Streiner et al., 2014). Using inappropriate or poorly designed instruments can result in inaccurate or unreliable data, compromising the quality of the research findings and limiting the research's usefulness. Understanding the appropriate use of quantitative research instruments is critical from a theoretical standpoint (...) for ensuring that research questions are answered accurately. Researchers (pre-service teachers, in-service teachers, teacher educators, and educational leaders) can get high-quality data that can be used to create and test theories in a particular field by selecting and designing the relevant instrument. Furthermore, using reliable and valid quantitative research instruments can boost the credibility of research findings and aid in developing new research questions and hypotheses. From a practical perspective, understanding quantitative research instrumentation can improve the quality of research practice. Researchers can ensure that the data acquired is accurate and reliable by selecting and creating appropriate instruments, which can help to guide decision-making in a particular subject. Furthermore, reliable and valid instruments can guide policy decisions, program development, and evaluation, resulting in more effective interventions and better outcomes. Where and How to Begin? The choice of research instrument depends on the research question, the nature of the phenomenon being studied, and the available resources (Bryman, 2016; Creswell, 2014; Dörnyei, 2007; Hair et al., 2014). Researchers can acquire accurate and valid data to answer their research questions and contribute to the knowledge base in their field by carefully selecting and building instruments. In education and the social sciences, quantitative research frequently involves using various instruments or tools to collect and analyze data. (shrink)

This research explored the utilization of video-based instructional material in enhancing learning competency among a group of sixty (60) students enrolled in grade 8 at Satriwithaya School in Bangkok, Thailand. The study was carried out. out during the 2nd semester of S.Y. 2023–2024. The respondents were chosen using the approach of purposive sampling. The research study employed an experimental approach and utilized quantitative research. Through the use of of a one-group pre- and post-test, the researcher was (...) able to gather the necessary data. The study included statistical measures such as percentages, means, and standard deviations, as well as dependent and independent t-tests, to assess and compare the proficiency and efficacy of utilizing video-based educational materials among students at Satriwithaya School in Bangkok, Thailand. Study findings also revealed that there is a significant difference in pre- and post-test assessment scores before and after using the video-based instructional materials. The research outcomes indicated that the application of video-based teaching resources as an instructional strategy in the field of biology resulted in an increased degree of effectiveness in the students' learning process. The findings of the study suggest that the utilization of video-based instructional materials has favorable outcomes in terms of enhancing pedagogical and educational achievements in the field of biology. This methodology has been demonstrated to provide learners with with the chance to enhance their comprehension through active participation, cultivate their imagination and cleverness, consequently facilitating enhanced educational achievements. Furthermore, the establishment of a conducive learning atmosphere that is both engaging and pleasurable for students can enhance their overall learning experience. Moreover, this approach enhances the teacher's capacity to convey their intended educational objectives to their students proficiently. -/- . (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the (...) salivaomics domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of (...) heart palpitations, 2) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes. (shrink)

Recently, Intelligent Tutoring Systems (ITS) got much attention from researchers even though ITS educational technology began in the late 1960s and ITS is just embryonic from laboratories into the field. In this paper we outline an intelligent tutoring system for teaching basics of the databases system called (MDB). The MDB was built as education system by using the authoring tool (ITSB). MDB contains learning materials as a group of lessons for beginner level which include relational database system and lessons in (...) the process to install and set up a database. MDB system has exams for each level of the Lessons. An evaluation was done to see the effectiveness the MDB among learners and instructors. The outcome of the evaluation was promising. (shrink)

Enterprise Risk Management involves the process of identification, evaluation, treatment, and communication regarding risks throughout the enterprise. To support the tasks associated with this process, several frameworks and modeling languages have been proposed, such as the Risk and Security Overlay (RSO) of ArchiMate. An ontological investigation of this artifact would reveal its adequacy, capabilities, and limitations w.r.t. the domain of risk and security. Based on that, a language redesign can be proposed as a refinement. Such analysis and redesign have (...) been executed for the risk elements of the RSO grounded in the Common Ontology of Value and Risk. The next step along this line of research is to address the following research problems: What would be the outcome of an ontological analysis of security-related elements of the RSO? That is, can we identify other semantic deficiencies in the RSO through an ontological analysis? Once such an analysis is provided, can we redesign the security elements of the RSO accordingly, in order to produce an improved artifact? Here, with the aid of the Reference Ontology for Security Engineering (ROSE) and the ontological theory of prevention behind it, we address the remaining gap by proceeding with an ontological analysis of the security-related constructs of the RSO. The outcome of this assessment is an ontology-based redesign of the ArchiMate language regarding security modeling. In a nutshell, we report the following contributions: (1) an ontological analysis of the RSO that identifies six limitations concerning security modeling; (2) because of the key role of the notion of prevention in security modeling, the introduction of the ontological theory of prevention in ArchiMate; (3) a well-founded redesign of security elements of ArchiMate; and (4) ontology-based security modeling patterns that are logical consequences of our proposal of redesign due to its underlying ontology of security. As a form of evaluation, we show that our proposal can describe risk treatment options, according to ISO 31000. Finally, besides presenting multiple examples, we proceed with a real-world illustrative application taken from the cybersecurity domain. (shrink)

Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) (...) Confirm the diagnosis of a brain tumor, 2) Consider the possibility of a brain tumor that has metastasized, and 3) Consider the possibility of a brain tumor. Our expert system offers a user-friendly interface, enabling users to select symptoms and receive a diagnosis based on the provided information. This paper discusses the expert system's development, implementation, and evaluation, highlighting its potential to facilitate brain tumor diagnosis and decision-making in clinical settings. Additionally, we provide a literature review that contextualizes our expert system within the broader landscape of rule-based expert systems for brain tumor diagnosis, examining their effectiveness, limitations, and challenges. (shrink)

I make the case that research assesment should be employed to assess the scientific system, or research missions, rather than individual researchers. In order to shift to a more open science ,a thorough revision of the reward and incentives system is since long overdue. I propose here an alternative based on review of research behaviour, such as colkaboration and knowledge sharing,rather than research outcomes.

Lung cancer is a serious and deadly disease that affects the lungs, which are responsible for taking in oxygen and expelling carbon dioxide from the body. The disease can develop in any part of the lungs and is usually caused by smoking or exposure to certain chemicals. The main Objective: of this expert system is to provide an accurate diagnosis of lung cancer and the appropriate treatment options. In this paper, Methods: we present the design and implementation of an expert (...) system that can assist physicians in diagnosing lung cancer and determining the best course of treatment. The system provides an overview of the disease, its causes, symptoms, diagnosis, and treatment, along with relevant medical information. Results: The system was developed using the Clips expert system language and evaluated by medical professionals who found it to be an effective tool for diagnosing and treating lung cancer. Conclusion: The proposed expert system has the potential to improve patient outcomes and reduce healthcare costs associated with the treatment of lung cancer. (shrink)

There are contradictory results regarding how students' learning outcomes can be predicted by various family and community inputs among previous studies, creating an evidence gap. Furthermore, previous studies have mostly concentrated on the cognitive aspect of students' learning outcomes, ignoring the affective and psychomotor dimensions, creating key knowledge gaps. Bridging these gaps, this predictive correlational study was conducted to understand how cultural capital, parental involvement (family inputs), support for schools, security network and school reforms (community inputs) jointly and (...) partially predict students' overall, cognitive, affective and psychomotor learning outcomes in the context of Calabar Education Zone, Nigeria. A random sample of principals (?? = 78) and students (?? = 915) recruited through a multistage approach, participated in the study. Data were collected through the physical administration of three sets of questionnaires designed by the researchers. The psychometric properties of the questionnaires (such as validity, dimensionality, reliability and goodness of fit) were all analysed and found acceptable based on pilot data. Data analysis was performed using SPSS version 27 and AMOS version 26 software. Results from the main study proved, among others, that family inputs (family social capital and parental involvement) jointly and individually had a significant contribution to students' overall, cognitive, affective and psychomotor learning outcomes. Similarly, community inputs (support for school, security network and school reforms) have significant composite and partial contributions to students' overall, cognitive, affective and psychomotor learning outcomes in public secondary schools. This result implies that parents and host community leaders must strengthen their partnerships with secondary schools and contribute their quota to institutions' curricular and co-curricular activities. (shrink)

Learner-centered approaches have emerged as the fundamental pillar of effective pedagogy in the rapidly evolving landscape of 21st century education. This paradigm shift places the learner at the heart of the educational process, recognizing their diverse needs and aspirations. Consequently, the achievements of learners have become crucial indicators of institutional success. While extensive research has focused on student affairs and services, limited attention has been given to understanding the impact of administration on realizing the objectives of these support systems. (...) This study aims to investigate the relationship between the administration of learner-centered academic support systems and the attainment of desired outcomes in student affairs and services within higher education institutions in the Philippines. Utilizing a non-experimental correlational quantitative research design, a sample of three hundred fifty university students from various academic disciplines was randomly selected as participants. The findings demonstrate a strong positive correlation between the administration of learner-centered academic support systems and the achievement of desired outcomes in student affairs and services. As a result, the null hypothesis was rejected based on statistical analysis employing Pearson Correlation (r). These findings highlight the significant impact that learner-centered academic support system administration has on the realization of objectives in student affairs and services within the contemporary higher educational setting. To ensure sustained best practices in student development, it is imperative for the administration to foster collaboration with internal stakeholders and external partners. Recognizing that students are the focal point of instruction, their performance serves as a reflection of the institution’s commitment to providing quality education. By aligning administrative efforts with the evolving needs of students, institutions can enhance student affairs and services and create an optimal learning environment in the 21st century. (shrink)

This study aims to (1) describe the application of the roundtable cooperative model on narrative text teaching materials, (2) describe students’ responses to the application of the roundtable cooperative model on narrative text teaching materials, (3) describe the increase in students’ knowledge of narrative text teaching materials. The type of research used was classroom action research. Data collection techniques were observation, interviews, questionnaires, tests, and documentation. The data in this study were in the form of application, response, and (...) increase in students’ knowledge of implementing the roundtable cooperative model on narrative text teaching materials. Data were analyzed using interactive techniques, including data reduction, presentation, and concluding. The data validation technique used data and method triangulation. The study results show that (1) applying the roundtable cooperative model to narrative text teaching materials can improve students’ understanding and writing skills. These improvements include structure, storyline or sequence of events, writing development, and completeness of information according to the theme; (2) students’ responses to the application of the roundtable cooperative model show that 90.3% agree to use the cooperative model in narrative text teaching materials and 9.7% do not agree; (3) there is an increase in student learning outcomes. Before applying the roundtable cooperative model, the students get an average score of 56, and after applying it, they get an average score of 69. The results show that the roundtable cooperative model can be used in the learning process by using media that supports the roundtable cooperative model and teaching materials. (shrink)

Climate change assessments rely upon scenarios of socioeconomic developments to conceptualize alternative outcomes for global greenhouse gas emissions. These are used in conjunction with climate models to make projections of future climate. Specifically, the estimations of greenhouse gas emissions based on socioeconomic scenarios constrain climate models in their outcomes of temperatures, precipitation, etc. Traditionally, the fundamental logic of the socioeconomic scenarios—that is, the logic that makes them plausible—is developed and prioritized using methods that are very subjective. This (...) introduces a fundamental challenge for climate change assessment: The veracity of projections of future climate currently rests on subjective ground. We elaborate on these subjective aspects of scenarios in climate change research. We then consider an alternative method for developing scenarios, a systems dynamics approach called ‘Cross-Impact Balance’ (CIB) analysis. We discuss notions of ‘objective’ and ‘objectivity’ as criteria for distinguishing appropriate scenario methods for climate change research. We distinguish seven distinct meanings of ‘objective,’ and demonstrate that CIB analysis is more objective than traditional subjective approaches. However, we also consider criticisms concerning which of the seven meanings of ‘objective’ are appropriate for scenario work. Finally, we arrive at conclusions regarding which meanings of ‘objective’ and ‘objectivity’ are relevant for climate change research. Because scientific assessments uncover knowledge relevant to the responses of a real, independently existing climate system, this requires scenario methodologies employed in such studies to also uphold the seven meanings of ‘objective’ and ‘objectivity.’. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of (...) three layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Given the unprecedented novel nature and scale of coronavirus and the global nature of this public health crisis, which upended many public/environmental research norms almost overnight. However, with further waves of the virus expected and more pandemics anticipated. The COVID-19 pandemic of 2020 opened our eyes to the ever-changing conditions and uncertainty that exists in our world today, particularly with regards to environmental and public health practices disruption. This paper explores environmental and public health evidence-based practices toward responding (...) to Covid-19. A literature review tried to do a deep dive through the use of various search engines such as Mendeley, Research Gate, CAB Abstract, Google Scholar, Summon, PubMed, Scopus, Hinari, Dimension, OARE Abstract, SSRN, Academia search strategy toward retrieving research publications, “gray literature” as well as reports from expert working groups. To achieve enhanced population health, it is recommended to adopt widespread evidence-based strategies, particularly in this uncertain time. As only together can evidence-informed decision-making (EIDM) can become a reality which include effective policies and practices, transparency and accountability of decisions, and equity outcomes; these are all more relevant in resource-constrained contexts, such as Nigeria. Effective and ethical EIDM though requires the production as well as use of high-quality evidence that are timely, appropriate and structured. One way to do so is through co-production. Co-production (or co-creation or co-design) of environmental/public health evidence considered as a key tool for addressing complex global crises such as the high risk of severe COVID-19 in different nations. A significant evidence-based component of environmental/public health (EBEPH) consist of decisions making based on best accessible, evidence that is peer-reviewed; using data as well as systematic information systems; community engagement in policy-making; conducting sound evaluation; do a thorough program-planning frameworks; as well as disseminating what is being learned. As researchers, scientists, statisticians, journal editors, practitioners, as well as decision makers strive to improve population health, having a natural tendency toward scrutinizing the scientific literature aimed at novel research findings serving as the foundation for intervention as well as prevention programs. The main inspiration behind conducting research ought to be toward stimulating and collaborating appropriately on public/environmental health action. Hence, there is need for a “Plan B” of effective behavioral, environmental, social as well as systems interventions (BESSI) toward reducing transmission. (shrink)

“Pick a card, any card. This has to be a completely free choice.” the magician tells you. But is it really? Although we like to think that we are using our free will to make our decisions, research in psychology has shown that many of our behaviours are automatic and unconsciously influenced by external stimuli (Ariely, 2008; Bargh & Chartrand, 1999; Newell & Shanks, 2014; Nisbett & Wilson, 1977), and that we are often oblivious to the cognitive mechanisms that (...) underpin our decision (Wegner, 2002, 2003). Magicians have exploited this illusory sense of agency for a long time, and have developed a wide range of techniques to influence and control spectators’ choices of such things as card, word, or number (Annemann, 1933; Banachek, 2002a; Jones, 1994; Turner, 2015). These techniques are instances of what is called forcing. -/- Many forces are extremely effective, illustrating various weaknesses in our sense of control over decisions and their outcomes. Researchers have started to investigate them in various ways (Kuhn, Pailh s, & Lan, 2020; Olson, Amlani, Raz, & Rensink, 2015; Pailhes & Kuhn, 2020b, 2020c; Shalom et al., 2013) and are beginning to obtain valuable insights into decision-making processes as well as a better understanding of the cognitive mechanisms that lead people to experience an illusory sense of free will and of agency. -/- Although magicians have acquired large amounts of knowledge in covertly controlling people’s choices, much of this knowledge is only discussed in the context of individual magic tricks, or in books that are not readily accessible to non-magicans. As we and others have argued elsewhere (Ekroll, Sayim, & Wagemans, 2017; Kuhn, 2019; Kuhn, Amlani, & Rensink, 2008; Kuhn, Caffaratti, Teszka, & Rensink, 2014; Macknik et al., 2008; Olson et al., 2015; Olson, Landry, Appourchaux, & Raz, 2016; Shalom et al., 2013; Thomas, Didierjean, Maquestiaux, & Gygax, 2015), a particularly effective way of making this knowledge more available is via the creation of taxonomies centered around psychological mechanisms (Rensink & Kuhn, 2015). For example, the psychologically based taxonomy of misdirection (Kuhn et al., 2014) helps draw links between misdirection and formal theories of perception and cognition. -/- Our aim here is to apply a similar process to the knowledge magicians have about forcing. The present paper develops a psychologically based taxonomy of forcing techniques with two goals in mind. Firstly, it should help uncover the various psychological mechanisms that underlie forcing techniques. Secondly, it should facilitate knowledge transfer between magicians and psychologists. Among other things, this knowledge will allow researchers to gain new insights into the mechanisms underlying decision-making, and the feeling of free will and of agency over choice. We start by defining the magician’s force and then look at some of the past classifications of forcing. (shrink)

In this article, I will explore how the underlying research values of ‘openness’ and ‘mutual responsiveness’, which are central to open science practices, can be integrated into a new ethos of science. Firstly, I will revisit Robert Merton's early contribution to this issue, examining whether the ethos of science should be understood as a set of norms for scientists to practice ‘good’ science or as a set of research values as a functional requirement of the scientific system to (...) produce knowledge, irrespective of individual adherence to these norms. Secondly, I will analyse the recent codification of scientific practice in terms of ‘scientific integrity’, a framework that Merton did not pursue. Based on this analysis, and illustrated on the case of COVID-19 as a case in which the institution of science was challenged to deliver urgently on societal desirable outcomes, I will argue that promoting open science and its core norms of collaboration and openness requires broader governance of the institution of science in its relationship with society at large, rather than relying solely on self-governance within the scientific community through a new ethos of science. This conclusion has implications for re-evaluating research assessments, suggesting that the evaluation of the scientific system should take precedence over evaluating individual researchers, and that incentives should be provided to encourage specific research behaviour rather than solely focusing on individual research outputs. This article is followed up by Commentaries of Carl Mitcham, Alfred Nordmann, Sabina Leonelli, Lukas Fuchs, Monica Edwards-Schachter. (shrink)

The present study aimed to examine how to improve the effectiveness of moral exemplar-applied interventions based on the pillars of the Self-Determination Theory (SDT) framework, autonomy, competence, and relatedness. Past research has mainly focused on the relatedness and attainability of moral exemplars for predicting motivation outcomes. The data for this study consisted of synthesized data sets from previous studies examining the motivational impacts of distinct moral exemplars and intervention methods. The main syntheses for these data sets used (...) Multilevel Modeling (MLM) focusing on relatability, attainability, and intervention methods, corresponding to relatedness, competence, and autonomy in the SDT, respectively, as predictors. In general, there was a significant interaction effect between the attainability or relatability, and the intervention method. Autonomous instruction methods, which support autonomy, were demonstrated to boost motivational outcomes. Implications from this study support the employment of SDT to examine the use of moral exemplars in moral education and were consistent with previous exemplar studies. (shrink)

The aim of this paper is to provide a critical review of flow theory - a psychological state - and its marketing outcomes in online environments. Despite the strong interest of researchers from various disciplines, adapting the flow theory to online environments has been problematic. Based on a careful examination of the literature, we identified conceptual challenges confronted when studying flow online. The paper further discusses some possible solutions for challenges and underlines the need for more studies in (...) this research area. Finally, marketing outcomes of applying flow theory in online environment were examined. (shrink)

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary (...) dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed. (shrink)

Integration sustainability outcomes give attention to construction ecology in the design review of urban environments to comply with Earth’s System that is composed of integral parts of the (i.e., physical, chemical and biological components). Naturally, exchange patterns of industrial ecology have consistent and periodic cycles to preserve energy flows and materials in Earth’s System. When engineering topology is affecting internal and external processes in system networks, it postulated the valence of the first-level spatial outcome (i.e., project compatibility success). These (...) instrumentalities are dependent on relating the second-level outcome (i.e., participant security satisfaction). The construction ecology-based topology (i.e., as feedback energy system) flows from biotic and abiotic resources in the entire Earth’s ecosystems. These spatial outcomes are providing an innovation, as entails a wide range of interactions to state, regulate and feedback “topology” to flow as “interdisciplinary equilibrium” of ecosystems. The interrelation dynamics of ecosystems are performing a process in a certain location within an appropriate time for characterizing their unique structure in “equilibrium patterns”, such as biosphere and collecting a composite structure of many distributed feedback flows. These interdisciplinary systems regulate their dynamics within complex structures. These dynamic mechanisms of the ecosystem regulate physical and chemical properties to enable a gradual and prolonged incremental pattern to develop a stable structure. The engineering topology of construction ecology for integration sustainability outcomes offers an interesting tool for ecologists and engineers in the simulation paradigm as an initial form of development structure within compatible computer software. This approach argues from ecology, resource savings, static load design, financial other pragmatic reasons, while an artistic/architectural perspective, these are not decisive. The paper described an attempt to unify analytic and analogical spatial modeling in developing urban environments as a relational setting, using optimization software and applied as an example of integrated industrial ecology where the construction process is based on a topology optimization approach. (shrink)

In this paper, I examine why moral exemplars are useful and even necessary in moral education despite several critiques from researchers and educators. To support my point, I review recent AI research demonstrating that exemplar-based learning is superior to rule-based learning in model performance in training neural networks, such as large language models. I particularly focus on why education aiming at promoting the development of multifaceted moral functioning can be done effectively by using exemplars, which is similar (...) to exemplar-based learning in AI model training. Furthermore, I discuss the potential limitations and issues related to exemplar-applied moral education with findings from recent studies in AI research raising concerns about model biases and toxic outcomes. I attempt to propose ways to address the concerns regarding employing moral exemplars as well. As remedies, I suggest that autonomy-supporting deliberative and reflective learning processes should be utilized. Furthermore, based on the discussion, I examine how macroscopic socio-cultural aspects influence the effectiveness of exemplar-applied moral education. Suggestions for moral educators and future directions for research in moral education are briefly discussed. (shrink)

Recently, Intelligent Tutoring Systems (ITS) got much attention from researchers even though ITS educational technology began in the late 1960s and ITS is just embryonic from laboratories into the field. In this paper we outline an intelligent tutoring system for teaching basics of the databases system called (MDB). The MDB was built as education system by using the authoring tool (ITSB). MDB contains learning materials as a group of lessons for beginner level which include relational database system and lessons in (...) the process to install and set up a database. MDB system has exams for each level of the Lessons. An evaluation was done to see the effectiveness the MDB among learners and instructors. The outcome of the evaluation was promising. (shrink)

Human rights may feel self-apparent to us, but less than 80 years ago, one of the most advanced countries at the time acted based on an utterly contrary ideology. The view of social Darwinism that abandoned the idea of the intrinsic value of human lives instead argued that oppression of the inferior is not only inevitable but desirable. One of the many catastrophic outcomes is the medical data obtained from inhuman experiments at concentration camps. Ethical uncertainty over whether (...) the resulting insights should be a part of the medical literature provides a chance to consider the seemingly irreplaceable social construct of human dignity. Would any medical benefit justify the utilization of this illicit data? Would utilization even qualify as an insult to the dignity of the exploited subjects, or is this a question about intersubjective meaning? This work discusses the wisdom in blind adherence to human dignity, the possibility of retrospective insults, moral complicity, contrary viewpoints, and possible resolutions. (shrink)

COVID-19 demonstrated a global catastrophe that touched everybody, including the scientific community. As we respond and recover rapidly from this pandemic, there is an opportunity to guarantee that the fabric of our society includes sustainability, fairness, and care. However, approaches to environmental health attempt to decrease the population burden of COVID-19, toward saving patients from becoming ill along with preserving the allocation of clinical resources and public safety standards. This paper explores environmental and public health evidence-based practices toward responding (...) to Covid-19. A literature review tried to do a deep dive through the use of various search engines such as Mendeley, Research Gate, CAB Abstract, Google Scholar, Summon, PubMed, Scopus, Hinari, Dimension, OARE Abstract, SSRN, Academia search strategy toward reretrieving research publications, “grey literature” as well as reports from expert working groups. To achieve enhanced population health, it is recommended to adopt widespread evidence-based strategies, particularly in this uncertain time. As only together can evidence-informed decision-making (EIDM) can become a reality which includes effective policies and practices, transparency and accountability of decisions, and equity outcomes; these are all more relevant in resource-constrained contexts, such as Nigeria. Effective and ethical EIDM though requires the production as well as use of high-quality evidence that are timely, appropriate and structured. One way to do so is through co-production. Co-production (or co-creation or co-design) of environmental/public health evidence considered as a key tool for addressing complex global crises such as the high risk of severe COVID-19 in different nations. A significant evidence-based component of environmental/public health (EBEPH) consist of decisions making based on best accessible, evidence that is peer-reviewed; using data as well as systematic information systems; community engagement in policy making; conducting sound evaluation; do thorough program-planning frameworks; as well as disseminating what is being learned. As researchers, scientists, statisticians, journal editors, practitioners, as well as decision-makers strive to improve population health, having a natural tendency toward scrutinizing the scientific literature aimed at novel research findings serving as the foundation for intervention as well as prevention programs. The main inspiration behind conducting research ought to be toward stimulating and collaborating appropriately on public/environmental health action. Hence, there is need for a “Plan B” of effective behavioural, environmental, social as well as systems interventions (BESSI) toward reducing transmission. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, (...) achieving an impressive accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

Dear Editors, -/- I found the content of your editorials to be highly intriguing [1,2]. Scientific journals are witnessing a growing prevalence of publications related to artificial intelligence (AI). Three letters to the editor were recently published in your journal [3-5]. The renowned journal Nature has dedicated approximately 25 publications solely to the subject of ChatGPT. Moreover, a quick search on Pubmed using the term "ChatGPT" yields around 900 articles, with the vast majority originating in 2023. These statistics underscore the (...) substantial interest of the scientific community in this area. -/- AI, especially the ChatGPT tool and the recent Bard, have faced criticism and been portrayed as significant adversaries of science. It is evident that many authors or researchers, who may not be well-versed in writing, can greatly benefit from these tools, as mentioned earlier. Without taking a contrarian stance, one should consider the potential advantages of such technologies for researchers in less privileged regions, where access to new technologies is limited, and local or regional challenges abound [6]. AI cannot be confounded with other technologies, as it specifically focuses on replicating human-like intelligence and decision-making processes, rather than simply automating tasks or improving performance based on data patterns. -/- The translation into English poses challenges due to the dominance of English in worldwide publications, with over 95% of articles being published in this language, and even reaching 98% in some fields. Although this manuscript was partially translated using AI, it can still benefit researchers from non-native English regions. Even simple tasks like text editing can be problematic for researchers in underprivileged areas. AI can play a crucial role in the evolution of online lectures and classes, providing valuable support for African maxillofacial surgeons who lack the luxury of taking breaks from work to update their knowledge, as they may be the only available option [7]. One of the remarkable features of AI is its ability to discover knowledge gaps. The use of simple tools like reference organizers is rapidly evolving and can become automated or semi-automated through AI. However, we must carefully consider whether we should refrain from relying too heavily on AI in certain cases, as this could be seen as a significant regression. -/- On the other hand, we must be cautious about freely allowing AI tools to circulate in scientific journals and books without proper regulation [8]. Currently, accurately identifying texts generated by AI is challenging, and their effectiveness remains relatively low, at less than 30%. This means that only about 30 out of every 100 texts can be confidently classified as AI-generated. The technology must advance further to increase detection accuracy or at least raise suspicion. Academic journals no longer view authors acknowledging artificial intelligence tools as co-authors in their research favorably. This is crucial to prevent certain unscrupulous individuals, such as "false prophets," charlatans, and flat-earthers, from infiltrating the realm of science, potentially impeding the progress of serious research conducted by professionals dedicated to advancing humanity through science. -/- It is essential to keep in mind that AI does not generate anything novel. Human authors can not be fully substituted [9]. In research involving groundbreaking concepts, innovations, case reports, or technical notes, the use of AI tends to be less frequent due to its lack of capability in creating innovative outcomes. On the other hand, reviews, whether narrative, systematic, or scoping, are based on existing publications. To address potential fraudulent practices, editors, reviewers, and journals themselves should exercise greater vigilance and apply more stringent filters for this type of publication. -/- An article from over a decade ago already discussed the professions most susceptible to replacement by computerization [10]. Maxillofacial surgeons, physicians, dentists, and psychologists are among the professions that are less likely to be displaced. This position can be attributed to the significant level of patient-professional interaction, the development of specific manual skills over time, and the ability to make adaptable decisions during procedures. These intricacies pose formidable challenges for AI to grasp, regardless of its level of advancement. -/- We should maintain a composed yet vigilant stance at this juncture. Just as Portuguese navigators feared encountering serpents and sea monsters when they ventured into uncharted waters, the uncertainty and novelty of AI can evoke apprehension in us all. Nonetheless, we must embrace the benefits that AI can offer while imposing strict regulations and appropriate penalties to prevent any potential abuses carried out "in the name of science." -/- Sincerely yours,. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. (...) While researchers have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

Abstract: In a former paper [21], an exact image compression based on bit-planes blocking was proposed. The proposed algorithm uses two bit codes for block representation. The outcome of the encoding process is two streams: Main Bit Stream, MBS and Residual Bit Stream, RBS. The algorithm core is searching for the greatest block of Unicode to encode in main stream and if not found until size of two by two then it will be kept as is in residual stream. (...) In this paper, a lossy version of that algorithm is presented. The change in the base algorithm is in the definition of the unary-code-block is eased to be above certain percent. The percent is varied from plane to another as their contribution to image power varies. The testing of the proposed algorithm shows comparable results. Image degradations seems restorable even for high compression ratios. (shrink)

Eyal Shahar’s essay review [1] of James Penston’s remarkable book [2] seems more inspired playful academic provocation than review or essay, expressing dramatic views of impossible validity. The account given of modern biostatistical causation reveals the slide from science into the intellectual confusion and non-science RCTs have created: “…. the purpose of medical research is to estimate the magnitude of the effect of a causal contrast, for example the probability ratio of a binary outcome …” But Shahar’s world is (...) simultaneously not probabilistic, but of absolute uncertainty: “We should have no confidence in any type of evidence ….. We should have no confidence at all”. Shahar’s "Causal contrast" is attractive. It seems to make sense, but bypasses in two words the means of establishing causation by the scientific method. This phrase assumes a numeric statistically significant “contrast” is causal rather than a potential correlation requiring further investigation. The concept of “causal contrast” is a slippery slope from sense into biostatistical non-science. This can be illustrated with an hypothetical RCT where 100% of interventions exhibit a posited treatment effect and 0% of placebo controls. Internal validity is seemingly quite reasonably assumed satisfied (common-sense dictating the likelihood of an awesome magnificent fraud, bias or plain error of the magnitude required is infinitesimal). Scientific method appears satisfied. The RCT demonstrates: (1) strict regularity of outcome in the presence of posited cause; (2) the absence of outcome in its absence and (3) an intervention (experiment) showing the direction of causation is from posited cause to posited effect. Now travel further down the slope from science. Assume 50% of interventions and 0% of controls are positive. We compromise scientific method, but justify this by assuming a large subgroup which we say surely must on these figures be exhibiting the posited treatment effect. But what of 10% of interventions and 9% of placebo controls exhibiting the posited treatment effect? Our biostatistician says the 1% “causal contrast” is statistically significant. But we have: (1) minimal evidence of regularity; (2) the posited outcome irrespective of presence of posited cause and (3) our intervention is at the highest equivocal in demonstrating any form of causation. This is not science. It is, however, where biostatistics has unthinkingly taken us, as Penston has shown comprehensively [2]. We, the audience of published medical research, are now for the 10% / 9% example well down the slope from science. An unattractive hypothesis results requiring numerous assumptions similar to these:- "There is a 'contrast' which is ‘causal’, albeit the method employed is not scientific. An effect of the intervention has been observed in a very small subgroup. This subgroup is susceptible to treatment. The similar number of placebo controls exhibiting the outcome sought is irrelevant, because the 1% difference between intervention and controls is statistically significant. The statistical analysis is valid and reliable. The RCT’s internal validity is sufficiently satisfied. No funding or bias or fraud has affected the results or their analysis.” As Penston notes: “Confirming and refuting the results of research is crucial to science …. But … there’s no way of testing the results of any particular large-scale RCT or epidemiological study. Each study … is left hanging in the air, unsupported.” It gets worse. To identify a rare serious adverse reaction of a frequency of 1:10,000 can require a trial of 200,000 or larger split between controls and interventions. This is not done. But for every 100 who prospectively benefit from the intervention, 9,900 also receive it. And for every 100 benefiting one person (who likely gains no benefit) will suffer a serious unidentified adverse reaction. This is also without taking account of more common adverse reactions whether serious or otherwise. References [1] Shahar, E. (2011) Research and medicine: human conjectures at every turn. International Journal of Person Centered Medicine 1 (2), 250-253. [2] Penston, J. (2010). stats.con: How we’ve been fooled by statistics-based research in medicine. London: The London Press, UK. (shrink)

Background: The World Health Organization has endorsed a community-based health insurance scheme (CBHIS) as a shared financing plan to improve access to health services and ensure universal coverage of the healthcare delivery system. Such a contributory scheme is the most likely option to provide health insurance coverage when governments cannot offer direct health care support. Despite improvements in access to current healthcare services, Ethiopia’s healthcare delivery remained low, owing to the country’s underdeveloped healthcare finance system. As a result, the (...) present study assessed CBHIS coverage and its predictors in Ethiopia at the individual and community level. Methods: The availability of CBHIS was checked via a criterion: at least one of the cluster respondents had to be enrolled in CBHIS. This study was based on secondary data from the Ethiopia Mini Demography and Health Survey (EMDHS) 2019 and included 7724 respondents. The study population was described using percentage and frequency. Four multilevel mixed-effects logistic regression modelling stages were performed to control for variations due to heterogeneity across clusters, and determinant predictors of CBHIS enrollment were outplayed. Results: The prevalence of CBHIS enrollment in Ethiopia was 33.13%. Rural residents were 3.218 times (AOR = 3.218; 95% CI: 1.521, 6.809), male household heads were 1.574 times (AOR = 1.574, 95% CI: 1.105, 2.241), getting funds from the safety net program were times 2.062 (AOR = 2.062, 95% CI: 1.297, 3.279), attending the primary educational level was 1.686 times (AOR = 1.686, 95% CI: 1.007, 2.821), bank accounts were 1.373 times (AOR = 1.373, 95% CI: 1.052, 1.792), and wealth index was 1.356 times (AOR = 1.356, 95% CI: 1.001, 1.838) more likely associated with CBHIS coverage, whereas the regions, the other religions, and women aged 20– 24 had lower odds of CBHIS coverage. Conclusion: In Ethiopia, regional healthcare expenditure per capital, religious affiliation, women age range, residents, sex of household head, funds from the safety net program, formal educational level, and having bank accounts were associated with community-based health insurance scheme coverage. (shrink)

Recent research on moral dynamics shows that an individual's ethical mind-set moderates the impact of an initial ethical or unethical act on the likelihood of behaving ethically on a subsequent occasion. More specifically, an outcome-based mind-set facilitates Moral Balancing, whereas a rule-based mind-set facilitates Moral Consistency. The objective was to look at the evolution of moral choice across a series of scenarios, that is, to explore if these moral patterns are maintained over time. The results of three (...) studies showed that Moral Balancing is not.. (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze (...) normative ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

: COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts of expectations: (...) the number of newly contaminated cases, the number of passings, and the number of recoveries. However, it is hard to anticipate the patients' real outcomes. To address the issue, a suggested approach based on long transient memory (LSTM) forecasts the number of COVID-19 cases in the next 10 days as well as the impact of preventative measures such as social isolation and lockdown on COVID-19 spread. (shrink)

The study, designed to find out the gender outcome, gender roles, and labeling of labels in the department's head examination, was conducted using 194 study participants recruited from lecturers at Dire Dawa University in Ethiopia 116 women and 78 were males aged 29.23. The following five labels are used: Department head in general, efficient male Department head, efficient female Department head, inefficient male Department head, and inefficient female Department head) were responded to by the research participants. Seven different hypotheses (...) were tested using ANOVA. Findings revealed that gender role orientation had a significant effect on the evaluation of the Department head. Furthermore, there were no significant effects of gender, and attachment of labels on the evaluation of Department head and no interaction effects of gender and gender role orientation, gender and attachment of labels and gender, gender role orientation, and attachment of labels on the evaluation of Department head. (shrink)

The developments along the Mekong River, including in Cambodia, have boomed. There were 755 dams. Of these, 537 have been completed, and 152 have been planned or proposed. Of these, 52 were under construction, and 14 have been canceled or suspended. Of these dams, 392 were hydropower, 337 were irrigation, and 26 were other types (CGIAR, 2015). Even though some officials saw economic development as a result of the hydropower dam, the negative impacts of hydropower dams were seen by many (...) local experts and NGOs. The negative impacts include reduced fishing stocks and exacerbated socio-economic issues (Asia and the Pacific Policy Society, 2022). Experts have conducted some research on ecosystems and livelihoods, but there has been a lack of studies conducted by local communities to investigate their cross-border impacts as a result of the hydropower dam, despite the fact that local communities have been facing issues as a result of the Mekong Development. The community research protocol was developed to improve local communities' understanding of their local contexts and their confidence in negotiating with decision-makers and policymakers to address and respond to their needs and the findings of studies on local planning for sustainable community development. The paper aims to guide community leaders and staff in conducting community research on transboundary impacts on fisheries resources, ecosystems, and the livelihoods of local communities using their knowledge. (shrink)

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation (...) outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks. (shrink)

Context: Many recent research areas such as human cognition and quantum physics call the observer-independence of traditional science into question. Also, there is a growing need for self-reflexivity in science, i.e., a science that reflects on its own outcomes and products. Problem: We introduce the concept of second-order science that is based on the operation of re-entry. Our goal is to provide an overview of this largely unexplored science domain and of potential approaches in second-order fields. Method: (...) We provide the necessary conceptual groundwork for explorations in second-order science, in which we discuss the differences between first- and second-order science and where we present a roadmap for second-order science. The article operates mainly with conceptual differentiations such as the separation between three seemingly identical concepts such as Science II, Science 2.0 and second-order science. Results: Compared with first-order science, the potential of second-order science lies in 1. higher levels of novelty and innovations, 2. higher levels of robustness and 3. wider integration as well as higher generality. As first-order science advances, second-order science, with re-entry as its basic operation, provides three vital functions for first-order science, namely a rich source of novelty and innovation, the necessary quality control and greater integration and generality. Implications: Second-order science should be viewed as a major expansion of traditional scientific fields and as a scientific breakthrough towards a new wave of innovative research. Constructivist content: Second-order science has strong ties with radical constructivism, which can be qualified as the most important root/origin of second-order science. Moreover, it will be argued that a new form of cybernetics is needed to cope with the new problems and challenges of second-order science. (shrink)

Understood in their historical context, current debates about psychiatric classification, prompted by the publication of the DSM-5, open up new opportunities for improved translational research in psychiatry. In this paper, we draw lessons for translational research from three time slices of 20th century psychiatry. From the first time slice, 1913 and the publication of Jaspers’ General Psychopathology, the lesson is that translational research in psychiatry requires a pluralistic approach encompassing equally the sciences of mind (including the social (...) sciences) and of brain. From the second time slice, 1953 and a conference in New York from which our present symptom-based classifications are derived, the lesson is that, while reliability remains the basis of psychiatry as an observational science, validity too is essential to effective translation. From the third time slice, 1997 and a conference on psychiatric classification in Dallas that brought together patients and carers with researchers and clinicians, the lesson is that we need to build further on collaborative models of research combining expertise-by-training with expertise-by-experience. This is important if we are to meet the specific challenges to translation presented by the complexity of the concept of mental disorder, particularly as reflected in the diversity of desired treatment outcomes. Taken together, these three lessons – a pluralistic approach, reliability and validity, and closer collaboration – provide an emerging framework for more effective translation of research into practice in 21st century psychiatry. (shrink)

Measuring the effectiveness of medical interventions faces three epistemological challenges: the choice of good measuring instruments, the use of appropriate analytic measures, and the use of a reliable method of extrapolating measures from an experimental context to a more general context. In practice each of these challenges contributes to overestimating the effectiveness of medical interventions. These challenges suggest the need for corrective normative principles. The instruments employed in clinical research should measure patient-relevant and disease-specific parameters, and should not be (...) sensitive to parameters that are only indirectly relevant. Effectiveness always should be measured and reported in absolute terms (using measures such as 'absolute risk reduction'), and only sometimes should effectiveness also be measured and reported in relative terms (using measures such as 'relative risk reduction')-employment of relative measures promotes an informal fallacy akin to the base-rate fallacy, which can be exploited to exaggerate claims of effectiveness. Finally, extrapolating from research settings to clinical settings should more rigorously take into account possible ways in which the intervention in question can fail to be effective in a target population. (shrink)

In biomedical measurement, biomarkers are used to achieve reliable prediction of, and useful causal information about patient outcomes while minimizing complexity of measurement, resources, and invasiveness. A biomarker is an assayable metric that discloses the status of a biological process of interest, be it normative, pathophysiological, or in response to intervention. The greatest utility from biomarkers comes from their ability to help clinicians (and researchers) make and evaluate clinical decisions. In this paper we discuss a specific methodological use of (...) clinical biomarkers in pharmacological measurement: Some biomarkers, called ‘surrogate markers’, are used to substitute for a clinically meaningful endpoint corresponding to events and their penultimate risk factors. We confront the reliability of clinical biomarkers that are used to gather information about clinically meaningful endpoints. Our aim is to present a systematic methodology for assessing the reliability of multiple surrogate markers (and biomarkers in general). To do this we draw upon the robustness analysis literature in the philosophy of science and the empirical use of clinical biomarkers. -/- After introducing robustness analysis we present two problems with biomarkers in relation to reliability. Next, we propose an intervention-based robustness methodology for organizing the reliability of biomarkers in general. We propose three relevant conditions for a robust methodology for biomarkers: (R1) Intervention-based demonstration of partial independence of modes: In biomarkers partial independence can be demonstrated through exogenous interventions that modify a process some number of “steps” removed from each of the markers. (R2) Comparison of diverging and converging results across biomarkers: By systematically comparing partially-independent biomarkers we can track under what conditions markers fail to converge in results, and under which conditions they successfully converge. (R3) Information within the context of theory: Through a systematic cross-comparison of the markers we can make causal conclusions as well as eliminate competing theories. We apply our robust methodology to currently developing Alzheimer’s research to show its usefulness for making causal conclusions. (shrink)

Behavioral genetic (BG) research has yielded many important discoveries about the origins of human behavior, but offers little insight into how we might improve outcomes. We posit that this gap in our knowledge base stems in part from the epidemiologic nature of BG research questions. Namely, BG studies focus on understanding etiology as it currently exists, rather than etiology in environments that could exist but do not as of yet (e.g., etiology following an intervention). Put another way, (...) they focus exclusively on the etiology of “what is” rather than “what could be”. The current paper discusses various aspects of this field-wide methodological reality, and offers a way to overcome it by demonstrating how behavioral geneticists can incorporate an experimental approach into their work. We outline an ongoing study that embeds a randomized intervention within a twin design, connecting “what is” and “what could be” for the first time. We then lay out a more general framework for a new field—experimental BGs—which has the potential to advance both scientific inquiry and related philosophical discussions. (shrink)

Technology firms are now purchasing access to research papers from academic publishers to train their artificial intelligence (AI) models. Using scientific content to train AI can come with multiple benefits, which help improve AI's capability to generate trustworthy outcomes, understand and process issues across a wide range of topics, as well as analyze information, make logical deductions, and draw conclusions. Journal articles are generally considered reliable because of the rigorous peer review system. However, the evaluation process is constrained (...) by several limitations, so a number of unreliable and invalid studies have been published. Retraction is often viewed as a crucial self-correction mechanism within science, allowing the academic community to identify and flag seriously flawed research that has been published. If retracted articles are used to train AI, it could lead to the spread of misinformation. Given the power of AI, this misinformation could be disseminated on a large scale and in a very short time, which might misinform and significantly increase the entropy (uncertainty) within society. Retracted notices can serve as a signal for AI developers to exclude seriously flawed articles from the training process. However, if the scientific content used to train AI is later discovered to be seriously distorted and retracted, it cannot be removed from the model’s knowledge base after the training is complete. As AI is increasingly applied in critical areas such as education and decision-making, training AI on seriously flawed scientific findings could lead to significant systematic consequences. Therefore, collaboration between the scientific community and AI developers to address these issues is crucial. (shrink)

Abstract: The sinking of the RMS Titanic in 1912 remains a poignant historical event that continues to captivate our collective imagination. In this research paper, we delve into the realm of data-driven analysis by applying Artificial Neural Networks (ANNs) to predict the chances of survival for passengers aboard the Titanic. Our study leverages a comprehensive dataset encompassing passenger information, demographics, and cabin class, providing a unique opportunity to explore the complex interplay of factors influencing survival outcomes. Our ANN- (...) class='Hi'>based predictive model achieved an accuracy rate of 78%, shedding light on the underlying patterns within the data. However, this paper underscores the multifaceted nature of survival prediction and acknowledges several challenges. These include missing data imputation, feature engineering, and the inherent noise within historical datasets. We further discuss the importance of model evaluation metrics, showcasing not only accuracy but also precision, recall, and F1-score as essential indicators of predictive performance. The research paper meticulously outlines the architecture of the ANN model, emphasizing the key hyperparameters, activation functions, and regularization techniques employed in model development. Additionally, we address ethical considerations related to data handling and potential biases within the dataset. While our achieved accuracy is a notable achievement, this study emphasizes the importance of interpreting the results with due caution. It underscores the need for a holistic perspective that considers the practical implications of false positives and false negatives, especially in the context of a disaster scenario. In conclusion, this research contributes to our understanding of survival prediction in historical events like the Titanic disaster using modern machine learning techniques. It calls for further exploration and refinement of predictive models, with a focus on enhancing the interpretability and generalizability of such models to real-world applications. (shrink)

Understanding recreational experiences is a longstanding research tradition and key to effective management. Given the complexities of human experience, many approaches have been applied to study recreational experience. Two such approaches are the experiential approach (based in a positivistic paradigm) and emergent experience (based in an interpretive paradigm). While viewed as being complementary, researchers have not offered guidance for incorporating the approaches into a common model of recreational experience. This study utilized longitudinal, qualitative data to examine aspects (...) of recreational experience posited by these two approaches. Results provided a framework for synthesizing across the two approaches. Respondents had clear pre-activity expectations, and most respondents realized their expected outcomes. This supports the experiential approach. Of the 48 activity narratives, 27 experienced something unexpected, and 45 described process-oriented, intrinsic motivation, suggesting evidence of emergent and unique characteristics specific to an individual’s realization of recreational experience. This supports the application of the emergent experience approach to understand how individuals create meaning from recreational engagements. The paper proposes a model for integrating results of the two approaches. While not advocating for any specific approach, the findings can serve as an example of building a holistic model of the outdoor recreation experience. The purpose of the model is to allow for a more complete understanding of how individuals create recreation experiences, more complete documentation of the benefits of outdoor recreation for both researchers and managers, and better synthesis across studies. (shrink)